diff --git "a/Classification/Text Classification.ipynb" "b/Classification/Text Classification.ipynb" new file mode 100644--- /dev/null +++ "b/Classification/Text Classification.ipynb" @@ -0,0 +1 @@ +{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":[" **Import Libraries**"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"xmkyTM2esyly"},"outputs":[],"source":["import csv\n","import json\n","from tensorflow.keras.models import Sequential\n","import tensorflow as tf\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Embedding, LSTM, GRU, Dense, Bidirectional, GlobalAveragePooling1D\n","from keras.callbacks import EarlyStopping\n","import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"l7qGuDMltiqN"},"outputs":[],"source":["# Load Dataset\n","with open(\"Reviews.json\", 'r') as f:\n"," datastore = json.load(f)\n","sentences = []\n","labels = []\n","\n","for item in datastore:\n"," sentences.append(item['Reviews'])\n"," labels.append(item['is_positive?'])"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"MxDtcOACt3mD"},"outputs":[],"source":["# Declare Variables\n","embedding_dim = 16\n","trunc_type = 'post'\n","padding_type = 'post'\n","oov_tok = \"\"\n","training_size = 151\n","early_stop = EarlyStopping(monitor='loss', min_delta=0, patience=20, verbose=2)\n","num_epochs = 1000"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"LOEQxiYDu3cy"},"outputs":[],"source":["training_sentences = sentences[0:training_size]\n","testing_sentences = sentences[training_size:]\n","training_labels = labels[0:training_size]\n","testing_labels = labels[training_size:]"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Tokenize**"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"_RITSXdwS_1A"},"outputs":[],"source":["tokenizer = Tokenizer()\n","tokenizer.fit_on_texts(training_sentences)\n","word_index = tokenizer.word_index\n","vocab_size = len(word_index) + 1"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"RyCEtZ2wvQs2"},"outputs":[],"source":["training_sequences = tokenizer.texts_to_sequences(training_sentences)\n","max_length = max(len(sequence) for sequence in training_sequences)\n","\n","training_padded = pad_sequences(training_sequences,\n"," maxlen=max_length,\n"," padding=padding_type,\n"," truncating=trunc_type)\n","\n","testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n","testing_padded = pad_sequences(testing_sequences,\n"," maxlen=max_length,\n"," padding=padding_type,\n"," truncating=trunc_type)"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"pGi-l_DxwJiX"},"outputs":[],"source":["import numpy as np\n","\n","training_padded = np.array(training_padded)\n","training_labels = np.array(training_labels)\n","testing_padded = np.array(testing_padded)\n","testing_labels = np.array(testing_labels)\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Create Model for BILSTM, LSTM and GRU**"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"qSwVYbOyS_1C"},"outputs":[],"source":["def create_model(model_type):\n"," model = tf.keras.Sequential()\n"," model.add(tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length))\n"," \n"," if model_type == \"biLSTM\":\n"," model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(150, return_sequences=True)))\n"," elif model_type == \"LSTM\":\n"," model.add(tf.keras.layers.LSTM(150, return_sequences=True))\n"," elif model_type == \"GRU\":\n"," model.add(tf.keras.layers.GRU(150, return_sequences=True))\n"," \n"," model.add(tf.keras.layers.GlobalAveragePooling1D())\n"," model.add(tf.keras.layers.De nse(24, activation='relu'))\n"," model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n"," \n"," return model\n","\n","biLSTM_model = create_model(\"biLSTM\")\n","LSTM_model = create_model(\"LSTM\")\n","GRU_model = create_model(\"GRU\")\n","\n","LSTM_model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n","GRU_model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n","biLSTM_model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Train BILSTM**"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"ECGqvGA4ygr5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/1000\n","5/5 - 26s - loss: 0.6535 - accuracy: 0.8808 - val_loss: 0.5288 - val_accuracy: 0.9508 - 26s/epoch - 5s/step\n","Epoch 2/1000\n","5/5 - 1s - loss: 0.5251 - accuracy: 0.8808 - val_loss: 0.2249 - val_accuracy: 0.9508 - 1s/epoch - 232ms/step\n","Epoch 3/1000\n","5/5 - 1s - loss: 0.4101 - accuracy: 0.8808 - val_loss: 0.3412 - val_accuracy: 0.9508 - 1s/epoch - 226ms/step\n","Epoch 4/1000\n","5/5 - 1s - loss: 0.4159 - accuracy: 0.8808 - val_loss: 0.2720 - val_accuracy: 0.9508 - 1s/epoch - 227ms/step\n","Epoch 5/1000\n","5/5 - 1s - loss: 0.3935 - accuracy: 0.8808 - val_loss: 0.2113 - val_accuracy: 0.9508 - 1s/epoch - 224ms/step\n","Epoch 6/1000\n","5/5 - 1s - loss: 0.3793 - accuracy: 0.8808 - val_loss: 0.2282 - val_accuracy: 0.9508 - 1s/epoch - 215ms/step\n","Epoch 7/1000\n","5/5 - 1s - loss: 0.3773 - accuracy: 0.8808 - val_loss: 0.2488 - val_accuracy: 0.9508 - 1s/epoch - 207ms/step\n","Epoch 8/1000\n","5/5 - 1s - loss: 0.3695 - accuracy: 0.8808 - val_loss: 0.2253 - val_accuracy: 0.9508 - 1s/epoch - 219ms/step\n","Epoch 9/1000\n","5/5 - 1s - loss: 0.3696 - accuracy: 0.8808 - val_loss: 0.2119 - val_accuracy: 0.9508 - 1s/epoch - 213ms/step\n","Epoch 10/1000\n","5/5 - 1s - loss: 0.3657 - accuracy: 0.8808 - val_loss: 0.2236 - val_accuracy: 0.9508 - 1s/epoch - 213ms/step\n","Epoch 11/1000\n","5/5 - 1s - loss: 0.3630 - accuracy: 0.8808 - val_loss: 0.2346 - val_accuracy: 0.9508 - 1s/epoch - 220ms/step\n","Epoch 12/1000\n","5/5 - 1s - loss: 0.3659 - accuracy: 0.8808 - val_loss: 0.2231 - val_accuracy: 0.9508 - 1s/epoch - 211ms/step\n","Epoch 13/1000\n","5/5 - 1s - loss: 0.3601 - accuracy: 0.8808 - val_loss: 0.2248 - val_accuracy: 0.9508 - 1s/epoch - 213ms/step\n","Epoch 14/1000\n","5/5 - 1s - loss: 0.3574 - accuracy: 0.8808 - val_loss: 0.2287 - val_accuracy: 0.9508 - 1s/epoch - 213ms/step\n","Epoch 15/1000\n","5/5 - 2s - loss: 0.3573 - accuracy: 0.8808 - val_loss: 0.2231 - val_accuracy: 0.9508 - 2s/epoch - 310ms/step\n","Epoch 16/1000\n","5/5 - 2s - loss: 0.3475 - accuracy: 0.8808 - val_loss: 0.1996 - val_accuracy: 0.9508 - 2s/epoch - 347ms/step\n","Epoch 17/1000\n","5/5 - 1s - loss: 0.3501 - accuracy: 0.8808 - val_loss: 0.1902 - val_accuracy: 0.9508 - 1s/epoch - 227ms/step\n","Epoch 18/1000\n","5/5 - 1s - loss: 0.3350 - accuracy: 0.8808 - val_loss: 0.2148 - val_accuracy: 0.9508 - 1s/epoch - 214ms/step\n","Epoch 19/1000\n","5/5 - 1s - loss: 0.3274 - accuracy: 0.8808 - val_loss: 0.1766 - val_accuracy: 0.9508 - 1s/epoch - 256ms/step\n","Epoch 20/1000\n","5/5 - 2s - loss: 0.2673 - accuracy: 0.8808 - val_loss: 0.0925 - val_accuracy: 0.9508 - 2s/epoch - 349ms/step\n","Epoch 21/1000\n","5/5 - 1s - loss: 0.1259 - accuracy: 0.9205 - val_loss: 0.0514 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 22/1000\n","5/5 - 1s - loss: 0.1023 - accuracy: 0.9603 - val_loss: 0.0384 - val_accuracy: 0.9836 - 1s/epoch - 252ms/step\n","Epoch 23/1000\n","5/5 - 2s - loss: 0.0687 - accuracy: 0.9735 - val_loss: 0.0367 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 24/1000\n","5/5 - 1s - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.0550 - val_accuracy: 0.9836 - 1s/epoch - 243ms/step\n","Epoch 25/1000\n","5/5 - 1s - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9836 - 1s/epoch - 266ms/step\n","Epoch 26/1000\n","5/5 - 1s - loss: 2.8741e-04 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 27/1000\n","5/5 - 1s - loss: 1.0937e-04 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 0.9836 - 1s/epoch - 246ms/step\n","Epoch 28/1000\n","5/5 - 1s - loss: 6.7060e-05 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9836 - 1s/epoch - 237ms/step\n","Epoch 29/1000\n","5/5 - 1s - loss: 5.8088e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 30/1000\n","5/5 - 1s - loss: 5.2547e-05 - accuracy: 1.0000 - val_loss: 0.1481 - val_accuracy: 0.9836 - 1s/epoch - 259ms/step\n","Epoch 31/1000\n","5/5 - 1s - loss: 4.8687e-05 - accuracy: 1.0000 - val_loss: 0.1506 - val_accuracy: 0.9836 - 1s/epoch - 282ms/step\n","Epoch 32/1000\n","5/5 - 1s - loss: 4.5855e-05 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 33/1000\n","5/5 - 1s - loss: 4.3411e-05 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 34/1000\n","5/5 - 1s - loss: 4.1285e-05 - accuracy: 1.0000 - val_loss: 0.1536 - val_accuracy: 0.9836 - 1s/epoch - 228ms/step\n","Epoch 35/1000\n","5/5 - 1s - loss: 3.9364e-05 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9836 - 1s/epoch - 266ms/step\n","Epoch 36/1000\n","5/5 - 2s - loss: 3.7624e-05 - accuracy: 1.0000 - val_loss: 0.1543 - val_accuracy: 0.9836 - 2s/epoch - 354ms/step\n","Epoch 37/1000\n","5/5 - 1s - loss: 3.6068e-05 - accuracy: 1.0000 - val_loss: 0.1545 - val_accuracy: 0.9836 - 1s/epoch - 269ms/step\n","Epoch 38/1000\n","5/5 - 1s - loss: 3.4595e-05 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9836 - 1s/epoch - 249ms/step\n","Epoch 39/1000\n","5/5 - 1s - loss: 3.3290e-05 - accuracy: 1.0000 - val_loss: 0.1547 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 40/1000\n","5/5 - 1s - loss: 3.2081e-05 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9836 - 1s/epoch - 219ms/step\n","Epoch 41/1000\n","5/5 - 1s - loss: 3.0936e-05 - accuracy: 1.0000 - val_loss: 0.1549 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 42/1000\n","5/5 - 2s - loss: 2.9902e-05 - accuracy: 1.0000 - val_loss: 0.1550 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 43/1000\n","5/5 - 2s - loss: 2.8942e-05 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 44/1000\n","5/5 - 1s - loss: 2.8040e-05 - accuracy: 1.0000 - val_loss: 0.1553 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 45/1000\n","5/5 - 1s - loss: 2.7182e-05 - accuracy: 1.0000 - val_loss: 0.1554 - val_accuracy: 0.9836 - 1s/epoch - 244ms/step\n","Epoch 46/1000\n","5/5 - 2s - loss: 2.6396e-05 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9836 - 2s/epoch - 349ms/step\n","Epoch 47/1000\n","5/5 - 1s - loss: 2.5643e-05 - accuracy: 1.0000 - val_loss: 0.1556 - val_accuracy: 0.9836 - 1s/epoch - 248ms/step\n","Epoch 48/1000\n","5/5 - 1s - loss: 2.4933e-05 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9836 - 1s/epoch - 242ms/step\n","Epoch 49/1000\n","5/5 - 1s - loss: 2.4255e-05 - accuracy: 1.0000 - val_loss: 0.1558 - val_accuracy: 0.9836 - 1s/epoch - 244ms/step\n","Epoch 50/1000\n","5/5 - 1s - loss: 2.3614e-05 - accuracy: 1.0000 - val_loss: 0.1560 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 51/1000\n","5/5 - 1s - loss: 2.3008e-05 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9836 - 1s/epoch - 255ms/step\n","Epoch 52/1000\n","5/5 - 1s - loss: 2.2419e-05 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 53/1000\n","5/5 - 1s - loss: 2.1865e-05 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9836 - 1s/epoch - 241ms/step\n","Epoch 54/1000\n","5/5 - 1s - loss: 2.1330e-05 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 55/1000\n","5/5 - 1s - loss: 2.0822e-05 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9836 - 1s/epoch - 251ms/step\n","Epoch 56/1000\n","5/5 - 1s - loss: 2.0332e-05 - accuracy: 1.0000 - val_loss: 0.1568 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 57/1000\n","5/5 - 1s - loss: 1.9862e-05 - accuracy: 1.0000 - val_loss: 0.1569 - val_accuracy: 0.9836 - 1s/epoch - 248ms/step\n","Epoch 58/1000\n","5/5 - 1s - loss: 1.9411e-05 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 59/1000\n","5/5 - 1s - loss: 1.8977e-05 - accuracy: 1.0000 - val_loss: 0.1572 - val_accuracy: 0.9836 - 1s/epoch - 246ms/step\n","Epoch 60/1000\n","5/5 - 1s - loss: 1.8559e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9836 - 1s/epoch - 263ms/step\n","Epoch 61/1000\n","5/5 - 1s - loss: 1.8155e-05 - accuracy: 1.0000 - val_loss: 0.1575 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 62/1000\n","5/5 - 1s - loss: 1.7767e-05 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9836 - 1s/epoch - 235ms/step\n","Epoch 63/1000\n","5/5 - 1s - loss: 1.7392e-05 - accuracy: 1.0000 - val_loss: 0.1578 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 64/1000\n","5/5 - 1s - loss: 1.7031e-05 - accuracy: 1.0000 - val_loss: 0.1580 - val_accuracy: 0.9836 - 1s/epoch - 224ms/step\n","Epoch 65/1000\n","5/5 - 1s - loss: 1.6678e-05 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 66/1000\n","5/5 - 1s - loss: 1.6340e-05 - accuracy: 1.0000 - val_loss: 0.1583 - val_accuracy: 0.9836 - 1s/epoch - 239ms/step\n","Epoch 67/1000\n","5/5 - 1s - loss: 1.6011e-05 - accuracy: 1.0000 - val_loss: 0.1585 - val_accuracy: 0.9836 - 1s/epoch - 248ms/step\n","Epoch 68/1000\n","5/5 - 1s - loss: 1.5696e-05 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9836 - 1s/epoch - 263ms/step\n","Epoch 69/1000\n","5/5 - 1s - loss: 1.5387e-05 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9836 - 1s/epoch - 262ms/step\n","Epoch 70/1000\n","5/5 - 1s - loss: 1.5089e-05 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 71/1000\n","5/5 - 1s - loss: 1.4800e-05 - accuracy: 1.0000 - val_loss: 0.1591 - val_accuracy: 0.9836 - 1s/epoch - 247ms/step\n","Epoch 72/1000\n","5/5 - 1s - loss: 1.4519e-05 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9836 - 1s/epoch - 254ms/step\n","Epoch 73/1000\n","5/5 - 1s - loss: 1.4243e-05 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9836 - 1s/epoch - 227ms/step\n","Epoch 74/1000\n","5/5 - 1s - loss: 1.3981e-05 - accuracy: 1.0000 - val_loss: 0.1596 - val_accuracy: 0.9836 - 1s/epoch - 239ms/step\n","Epoch 75/1000\n","5/5 - 1s - loss: 1.3724e-05 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 76/1000\n","5/5 - 1s - loss: 1.3473e-05 - accuracy: 1.0000 - val_loss: 0.1600 - val_accuracy: 0.9836 - 1s/epoch - 223ms/step\n","Epoch 77/1000\n","5/5 - 2s - loss: 1.3228e-05 - accuracy: 1.0000 - val_loss: 0.1602 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 78/1000\n","5/5 - 2s - loss: 1.2992e-05 - accuracy: 1.0000 - val_loss: 0.1604 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 79/1000\n","5/5 - 2s - loss: 1.2759e-05 - accuracy: 1.0000 - val_loss: 0.1605 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 80/1000\n","5/5 - 1s - loss: 1.2536e-05 - accuracy: 1.0000 - val_loss: 0.1607 - val_accuracy: 0.9836 - 1s/epoch - 246ms/step\n","Epoch 81/1000\n","5/5 - 1s - loss: 1.2314e-05 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9836 - 1s/epoch - 248ms/step\n","Epoch 82/1000\n","5/5 - 1s - loss: 1.2101e-05 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9836 - 1s/epoch - 262ms/step\n","Epoch 83/1000\n","5/5 - 1s - loss: 1.1892e-05 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9836 - 1s/epoch - 243ms/step\n","Epoch 84/1000\n","5/5 - 1s - loss: 1.1687e-05 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 85/1000\n","5/5 - 1s - loss: 1.1494e-05 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 86/1000\n","5/5 - 1s - loss: 1.1298e-05 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 87/1000\n","5/5 - 1s - loss: 1.1108e-05 - accuracy: 1.0000 - val_loss: 0.1620 - val_accuracy: 0.9836 - 1s/epoch - 217ms/step\n","Epoch 88/1000\n","5/5 - 1s - loss: 1.0923e-05 - accuracy: 1.0000 - val_loss: 0.1622 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 89/1000\n","5/5 - 1s - loss: 1.0744e-05 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9836 - 1s/epoch - 223ms/step\n","Epoch 90/1000\n","5/5 - 1s - loss: 1.0569e-05 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 91/1000\n","5/5 - 1s - loss: 1.0396e-05 - accuracy: 1.0000 - val_loss: 0.1627 - val_accuracy: 0.9836 - 1s/epoch - 248ms/step\n","Epoch 92/1000\n","5/5 - 1s - loss: 1.0229e-05 - accuracy: 1.0000 - val_loss: 0.1629 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 93/1000\n","5/5 - 1s - loss: 1.0068e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 94/1000\n","5/5 - 1s - loss: 9.9062e-06 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 95/1000\n","5/5 - 1s - loss: 9.7492e-06 - accuracy: 1.0000 - val_loss: 0.1634 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 96/1000\n","5/5 - 1s - loss: 9.5961e-06 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9836 - 1s/epoch - 280ms/step\n","Epoch 97/1000\n","5/5 - 1s - loss: 9.4430e-06 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9836 - 1s/epoch - 217ms/step\n","Epoch 98/1000\n","5/5 - 1s - loss: 9.2951e-06 - accuracy: 1.0000 - val_loss: 0.1640 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 99/1000\n","5/5 - 1s - loss: 9.1562e-06 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9836 - 1s/epoch - 234ms/step\n","Epoch 100/1000\n","5/5 - 1s - loss: 9.0105e-06 - accuracy: 1.0000 - val_loss: 0.1643 - val_accuracy: 0.9836 - 1s/epoch - 257ms/step\n","Epoch 101/1000\n","5/5 - 1s - loss: 8.8756e-06 - accuracy: 1.0000 - val_loss: 0.1645 - val_accuracy: 0.9836 - 1s/epoch - 264ms/step\n","Epoch 102/1000\n","5/5 - 2s - loss: 8.7380e-06 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 103/1000\n","5/5 - 2s - loss: 8.6084e-06 - accuracy: 1.0000 - val_loss: 0.1649 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 104/1000\n","5/5 - 1s - loss: 8.4775e-06 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9836 - 1s/epoch - 257ms/step\n","Epoch 105/1000\n","5/5 - 2s - loss: 8.3486e-06 - accuracy: 1.0000 - val_loss: 0.1652 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 106/1000\n","5/5 - 1s - loss: 8.2271e-06 - accuracy: 1.0000 - val_loss: 0.1654 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 107/1000\n","5/5 - 1s - loss: 8.1047e-06 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 108/1000\n","5/5 - 1s - loss: 7.9833e-06 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9836 - 1s/epoch - 240ms/step\n","Epoch 109/1000\n","5/5 - 1s - loss: 7.8693e-06 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9836 - 1s/epoch - 274ms/step\n","Epoch 110/1000\n","5/5 - 1s - loss: 7.7508e-06 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9836 - 1s/epoch - 244ms/step\n","Epoch 111/1000\n","5/5 - 1s - loss: 7.6389e-06 - accuracy: 1.0000 - val_loss: 0.1663 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 112/1000\n","5/5 - 1s - loss: 7.5290e-06 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 113/1000\n","5/5 - 1s - loss: 7.4202e-06 - accuracy: 1.0000 - val_loss: 0.1667 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 114/1000\n","5/5 - 1s - loss: 7.3133e-06 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9836 - 1s/epoch - 269ms/step\n","Epoch 115/1000\n","5/5 - 1s - loss: 7.2105e-06 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 116/1000\n","5/5 - 2s - loss: 7.1067e-06 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 117/1000\n","5/5 - 1s - loss: 7.0072e-06 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9836 - 1s/epoch - 251ms/step\n","Epoch 118/1000\n","5/5 - 1s - loss: 6.9063e-06 - accuracy: 1.0000 - val_loss: 0.1676 - val_accuracy: 0.9836 - 1s/epoch - 258ms/step\n","Epoch 119/1000\n","5/5 - 1s - loss: 6.8098e-06 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9836 - 1s/epoch - 275ms/step\n","Epoch 120/1000\n","5/5 - 1s - loss: 6.7161e-06 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 121/1000\n","5/5 - 1s - loss: 6.6212e-06 - accuracy: 1.0000 - val_loss: 0.1682 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 122/1000\n","5/5 - 1s - loss: 6.5304e-06 - accuracy: 1.0000 - val_loss: 0.1683 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 123/1000\n","5/5 - 1s - loss: 6.4383e-06 - accuracy: 1.0000 - val_loss: 0.1685 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 124/1000\n","5/5 - 1s - loss: 6.3506e-06 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9836 - 1s/epoch - 240ms/step\n","Epoch 125/1000\n","5/5 - 1s - loss: 6.2679e-06 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 126/1000\n","5/5 - 1s - loss: 6.1796e-06 - accuracy: 1.0000 - val_loss: 0.1690 - val_accuracy: 0.9836 - 1s/epoch - 241ms/step\n","Epoch 127/1000\n","5/5 - 1s - loss: 6.0953e-06 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9836 - 1s/epoch - 223ms/step\n","Epoch 128/1000\n","5/5 - 1s - loss: 6.0145e-06 - accuracy: 1.0000 - val_loss: 0.1694 - val_accuracy: 0.9836 - 1s/epoch - 222ms/step\n","Epoch 129/1000\n","5/5 - 1s - loss: 5.9360e-06 - accuracy: 1.0000 - val_loss: 0.1696 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 130/1000\n","5/5 - 1s - loss: 5.8538e-06 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 131/1000\n","5/5 - 1s - loss: 5.7762e-06 - accuracy: 1.0000 - val_loss: 0.1699 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 132/1000\n","5/5 - 1s - loss: 5.6991e-06 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9836 - 1s/epoch - 244ms/step\n","Epoch 133/1000\n","5/5 - 1s - loss: 5.6254e-06 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9836 - 1s/epoch - 272ms/step\n","Epoch 134/1000\n","5/5 - 1s - loss: 5.5504e-06 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9836 - 1s/epoch - 268ms/step\n","Epoch 135/1000\n","5/5 - 2s - loss: 5.4769e-06 - accuracy: 1.0000 - val_loss: 0.1706 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 136/1000\n","5/5 - 1s - loss: 5.4072e-06 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 137/1000\n","5/5 - 1s - loss: 5.3363e-06 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.9836 - 1s/epoch - 266ms/step\n","Epoch 138/1000\n","5/5 - 1s - loss: 5.2682e-06 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9836 - 1s/epoch - 258ms/step\n","Epoch 139/1000\n","5/5 - 1s - loss: 5.1990e-06 - accuracy: 1.0000 - val_loss: 0.1712 - val_accuracy: 0.9836 - 1s/epoch - 243ms/step\n","Epoch 140/1000\n","5/5 - 1s - loss: 5.1323e-06 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9836 - 1s/epoch - 262ms/step\n","Epoch 141/1000\n","5/5 - 2s - loss: 5.0674e-06 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 142/1000\n","5/5 - 1s - loss: 5.0037e-06 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9836 - 1s/epoch - 250ms/step\n","Epoch 143/1000\n","5/5 - 1s - loss: 4.9387e-06 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9836 - 1s/epoch - 265ms/step\n","Epoch 144/1000\n","5/5 - 1s - loss: 4.8765e-06 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 145/1000\n","5/5 - 1s - loss: 4.8155e-06 - accuracy: 1.0000 - val_loss: 0.1723 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 146/1000\n","5/5 - 1s - loss: 4.7541e-06 - accuracy: 1.0000 - val_loss: 0.1724 - val_accuracy: 0.9836 - 1s/epoch - 234ms/step\n","Epoch 147/1000\n","5/5 - 1s - loss: 4.6947e-06 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 148/1000\n","5/5 - 1s - loss: 4.6354e-06 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9836 - 1s/epoch - 234ms/step\n","Epoch 149/1000\n","5/5 - 1s - loss: 4.5779e-06 - accuracy: 1.0000 - val_loss: 0.1729 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 150/1000\n","5/5 - 1s - loss: 4.5210e-06 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 151/1000\n","5/5 - 1s - loss: 4.4634e-06 - accuracy: 1.0000 - val_loss: 0.1732 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 152/1000\n","5/5 - 1s - loss: 4.4098e-06 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 153/1000\n","5/5 - 1s - loss: 4.3548e-06 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9836 - 1s/epoch - 210ms/step\n","Epoch 154/1000\n","5/5 - 1s - loss: 4.3002e-06 - accuracy: 1.0000 - val_loss: 0.1737 - val_accuracy: 0.9836 - 1s/epoch - 215ms/step\n","Epoch 155/1000\n","5/5 - 1s - loss: 4.2481e-06 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 156/1000\n","5/5 - 1s - loss: 4.1973e-06 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 157/1000\n","5/5 - 1s - loss: 4.1443e-06 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 158/1000\n","5/5 - 1s - loss: 4.0947e-06 - accuracy: 1.0000 - val_loss: 0.1744 - val_accuracy: 0.9836 - 1s/epoch - 215ms/step\n","Epoch 159/1000\n","5/5 - 1s - loss: 4.0438e-06 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 160/1000\n","5/5 - 1s - loss: 3.9965e-06 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 0.9836 - 1s/epoch - 227ms/step\n","Epoch 161/1000\n","5/5 - 1s - loss: 3.9479e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 162/1000\n","5/5 - 1s - loss: 3.9004e-06 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9836 - 1s/epoch - 215ms/step\n","Epoch 163/1000\n","5/5 - 1s - loss: 3.8541e-06 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 164/1000\n","5/5 - 1s - loss: 3.8086e-06 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 165/1000\n","5/5 - 1s - loss: 3.7634e-06 - accuracy: 1.0000 - val_loss: 0.1755 - val_accuracy: 0.9836 - 1s/epoch - 227ms/step\n","Epoch 166/1000\n","5/5 - 1s - loss: 3.7203e-06 - accuracy: 1.0000 - val_loss: 0.1757 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 167/1000\n","5/5 - 1s - loss: 3.6756e-06 - accuracy: 1.0000 - val_loss: 0.1759 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 168/1000\n","5/5 - 1s - loss: 3.6331e-06 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9836 - 1s/epoch - 219ms/step\n","Epoch 169/1000\n","5/5 - 1s - loss: 3.5896e-06 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 170/1000\n","5/5 - 1s - loss: 3.5482e-06 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9836 - 1s/epoch - 210ms/step\n","Epoch 171/1000\n","5/5 - 1s - loss: 3.5067e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9836 - 1s/epoch - 230ms/step\n","Epoch 172/1000\n","5/5 - 1s - loss: 3.4663e-06 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 173/1000\n","5/5 - 1s - loss: 3.4255e-06 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 174/1000\n","5/5 - 1s - loss: 3.3868e-06 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9836 - 1s/epoch - 241ms/step\n","Epoch 175/1000\n","5/5 - 1s - loss: 3.3475e-06 - accuracy: 1.0000 - val_loss: 0.1771 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 176/1000\n","5/5 - 1s - loss: 3.3091e-06 - accuracy: 1.0000 - val_loss: 0.1773 - val_accuracy: 0.9836 - 1s/epoch - 217ms/step\n","Epoch 177/1000\n","5/5 - 1s - loss: 3.2713e-06 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 178/1000\n","5/5 - 1s - loss: 3.2342e-06 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 179/1000\n","5/5 - 1s - loss: 3.1977e-06 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 180/1000\n","5/5 - 1s - loss: 3.1617e-06 - accuracy: 1.0000 - val_loss: 0.1779 - val_accuracy: 0.9836 - 1s/epoch - 214ms/step\n","Epoch 181/1000\n","5/5 - 1s - loss: 3.1259e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 182/1000\n","5/5 - 1s - loss: 3.0907e-06 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 183/1000\n","5/5 - 1s - loss: 3.0557e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9836 - 1s/epoch - 261ms/step\n","Epoch 184/1000\n","5/5 - 1s - loss: 3.0210e-06 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 185/1000\n","5/5 - 1s - loss: 2.9872e-06 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9836 - 1s/epoch - 212ms/step\n","Epoch 186/1000\n","5/5 - 1s - loss: 2.9549e-06 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9836 - 1s/epoch - 217ms/step\n","Epoch 187/1000\n","5/5 - 1s - loss: 2.9221e-06 - accuracy: 1.0000 - val_loss: 0.1790 - val_accuracy: 0.9836 - 1s/epoch - 213ms/step\n","Epoch 188/1000\n","5/5 - 1s - loss: 2.8896e-06 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9836 - 1s/epoch - 219ms/step\n","Epoch 189/1000\n","5/5 - 2s - loss: 2.8572e-06 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9836 - 2s/epoch - 368ms/step\n","Epoch 190/1000\n","5/5 - 2s - loss: 2.8259e-06 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9836 - 2s/epoch - 467ms/step\n","Epoch 191/1000\n","5/5 - 2s - loss: 2.7948e-06 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9836 - 2s/epoch - 365ms/step\n","Epoch 192/1000\n","5/5 - 2s - loss: 2.7649e-06 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9836 - 2s/epoch - 421ms/step\n","Epoch 193/1000\n","5/5 - 2s - loss: 2.7338e-06 - accuracy: 1.0000 - val_loss: 0.1799 - val_accuracy: 0.9836 - 2s/epoch - 416ms/step\n","Epoch 194/1000\n","5/5 - 2s - loss: 2.7044e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 195/1000\n","5/5 - 1s - loss: 2.6750e-06 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 196/1000\n","5/5 - 1s - loss: 2.6461e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 197/1000\n","5/5 - 1s - loss: 2.6175e-06 - accuracy: 1.0000 - val_loss: 0.1805 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 198/1000\n","5/5 - 1s - loss: 2.5897e-06 - accuracy: 1.0000 - val_loss: 0.1806 - val_accuracy: 0.9836 - 1s/epoch - 269ms/step\n","Epoch 199/1000\n","5/5 - 1s - loss: 2.5615e-06 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9836 - 1s/epoch - 260ms/step\n","Epoch 200/1000\n","5/5 - 1s - loss: 2.5343e-06 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9836 - 1s/epoch - 252ms/step\n","Epoch 201/1000\n","5/5 - 1s - loss: 2.5077e-06 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9836 - 1s/epoch - 258ms/step\n","Epoch 202/1000\n","5/5 - 1s - loss: 2.4804e-06 - accuracy: 1.0000 - val_loss: 0.1812 - val_accuracy: 0.9836 - 1s/epoch - 247ms/step\n","Epoch 203/1000\n","5/5 - 1s - loss: 2.4541e-06 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 204/1000\n","5/5 - 1s - loss: 2.4281e-06 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9836 - 1s/epoch - 258ms/step\n","Epoch 205/1000\n","5/5 - 1s - loss: 2.4029e-06 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9836 - 1s/epoch - 266ms/step\n","Epoch 206/1000\n","5/5 - 1s - loss: 2.3781e-06 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9836 - 1s/epoch - 235ms/step\n","Epoch 207/1000\n","5/5 - 1s - loss: 2.3525e-06 - accuracy: 1.0000 - val_loss: 0.1819 - val_accuracy: 0.9836 - 1s/epoch - 224ms/step\n","Epoch 208/1000\n","5/5 - 1s - loss: 2.3279e-06 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 209/1000\n","5/5 - 1s - loss: 2.3036e-06 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 210/1000\n","5/5 - 1s - loss: 2.2801e-06 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 211/1000\n","5/5 - 1s - loss: 2.2563e-06 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9836 - 1s/epoch - 231ms/step\n","Epoch 212/1000\n","5/5 - 1s - loss: 2.2335e-06 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9836 - 1s/epoch - 269ms/step\n","Epoch 213/1000\n","5/5 - 1s - loss: 2.2105e-06 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9836 - 1s/epoch - 273ms/step\n","Epoch 214/1000\n","5/5 - 1s - loss: 2.1874e-06 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9836 - 1s/epoch - 271ms/step\n","Epoch 215/1000\n","5/5 - 1s - loss: 2.1655e-06 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 216/1000\n","5/5 - 1s - loss: 2.1431e-06 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9836 - 1s/epoch - 283ms/step\n","Epoch 217/1000\n","5/5 - 1s - loss: 2.1210e-06 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9836 - 1s/epoch - 256ms/step\n","Epoch 218/1000\n","5/5 - 2s - loss: 2.1003e-06 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 219/1000\n","5/5 - 2s - loss: 2.0778e-06 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 220/1000\n","5/5 - 2s - loss: 2.0577e-06 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9836 - 2s/epoch - 352ms/step\n","Epoch 221/1000\n","5/5 - 2s - loss: 2.0367e-06 - accuracy: 1.0000 - val_loss: 0.1839 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 222/1000\n","5/5 - 2s - loss: 2.0155e-06 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9836 - 2s/epoch - 396ms/step\n","Epoch 223/1000\n","5/5 - 1s - loss: 1.9958e-06 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9836 - 1s/epoch - 264ms/step\n","Epoch 224/1000\n","5/5 - 1s - loss: 1.9762e-06 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9836 - 1s/epoch - 258ms/step\n","Epoch 225/1000\n","5/5 - 1s - loss: 1.9562e-06 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9836 - 1s/epoch - 271ms/step\n","Epoch 226/1000\n","5/5 - 1s - loss: 1.9368e-06 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9836 - 1s/epoch - 257ms/step\n","Epoch 227/1000\n","5/5 - 2s - loss: 1.9176e-06 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9836 - 2s/epoch - 391ms/step\n","Epoch 228/1000\n","5/5 - 1s - loss: 1.8989e-06 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 229/1000\n","5/5 - 2s - loss: 1.8797e-06 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9836 - 2s/epoch - 374ms/step\n","Epoch 230/1000\n","5/5 - 2s - loss: 1.8617e-06 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9836 - 2s/epoch - 361ms/step\n","Epoch 231/1000\n","5/5 - 2s - loss: 1.8429e-06 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 232/1000\n","5/5 - 2s - loss: 1.8259e-06 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 233/1000\n","5/5 - 2s - loss: 1.8071e-06 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9836 - 2s/epoch - 300ms/step\n","Epoch 234/1000\n","5/5 - 1s - loss: 1.7898e-06 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 235/1000\n","5/5 - 1s - loss: 1.7725e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9836 - 1s/epoch - 273ms/step\n","Epoch 236/1000\n","5/5 - 1s - loss: 1.7554e-06 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9836 - 1s/epoch - 280ms/step\n","Epoch 237/1000\n","5/5 - 2s - loss: 1.7385e-06 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 238/1000\n","5/5 - 2s - loss: 1.7217e-06 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 239/1000\n","5/5 - 2s - loss: 1.7049e-06 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 240/1000\n","5/5 - 1s - loss: 1.6887e-06 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9836 - 1s/epoch - 261ms/step\n","Epoch 241/1000\n","5/5 - 1s - loss: 1.6724e-06 - accuracy: 1.0000 - val_loss: 0.1866 - val_accuracy: 0.9836 - 1s/epoch - 213ms/step\n","Epoch 242/1000\n","5/5 - 1s - loss: 1.6568e-06 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 243/1000\n","5/5 - 1s - loss: 1.6416e-06 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 244/1000\n","5/5 - 1s - loss: 1.6248e-06 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9836 - 1s/epoch - 270ms/step\n","Epoch 245/1000\n","5/5 - 1s - loss: 1.6104e-06 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9836 - 1s/epoch - 236ms/step\n","Epoch 246/1000\n","5/5 - 1s - loss: 1.5950e-06 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9836 - 1s/epoch - 213ms/step\n","Epoch 247/1000\n","5/5 - 1s - loss: 1.5799e-06 - accuracy: 1.0000 - val_loss: 0.1874 - val_accuracy: 0.9836 - 1s/epoch - 212ms/step\n","Epoch 248/1000\n","5/5 - 1s - loss: 1.5653e-06 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9836 - 1s/epoch - 226ms/step\n","Epoch 249/1000\n","5/5 - 1s - loss: 1.5508e-06 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 250/1000\n","5/5 - 1s - loss: 1.5359e-06 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 251/1000\n","5/5 - 1s - loss: 1.5215e-06 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9836 - 1s/epoch - 228ms/step\n","Epoch 252/1000\n","5/5 - 1s - loss: 1.5080e-06 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 253/1000\n","5/5 - 1s - loss: 1.4938e-06 - accuracy: 1.0000 - val_loss: 0.1882 - val_accuracy: 0.9836 - 1s/epoch - 228ms/step\n","Epoch 254/1000\n","5/5 - 1s - loss: 1.4800e-06 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9836 - 1s/epoch - 213ms/step\n","Epoch 255/1000\n","5/5 - 1s - loss: 1.4663e-06 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9836 - 1s/epoch - 230ms/step\n","Epoch 256/1000\n","5/5 - 2s - loss: 1.4528e-06 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 257/1000\n","5/5 - 1s - loss: 1.4398e-06 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 258/1000\n","5/5 - 1s - loss: 1.4267e-06 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9836 - 1s/epoch - 282ms/step\n","Epoch 259/1000\n","5/5 - 1s - loss: 1.4136e-06 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9836 - 1s/epoch - 267ms/step\n","Epoch 260/1000\n","5/5 - 1s - loss: 1.4009e-06 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9836 - 1s/epoch - 268ms/step\n","Epoch 261/1000\n","5/5 - 1s - loss: 1.3880e-06 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9836 - 1s/epoch - 287ms/step\n","Epoch 262/1000\n","5/5 - 2s - loss: 1.3758e-06 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 263/1000\n","5/5 - 3s - loss: 1.3634e-06 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9836 - 3s/epoch - 577ms/step\n","Epoch 264/1000\n","5/5 - 3s - loss: 1.3508e-06 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9836 - 3s/epoch - 576ms/step\n","Epoch 265/1000\n","5/5 - 2s - loss: 1.3389e-06 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9836 - 2s/epoch - 448ms/step\n","Epoch 266/1000\n","5/5 - 2s - loss: 1.3273e-06 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9836 - 2s/epoch - 457ms/step\n","Epoch 267/1000\n","5/5 - 1s - loss: 1.3148e-06 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9836 - 1s/epoch - 241ms/step\n","Epoch 268/1000\n","5/5 - 1s - loss: 1.3035e-06 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9836 - 1s/epoch - 270ms/step\n","Epoch 269/1000\n","5/5 - 2s - loss: 1.2916e-06 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9836 - 2s/epoch - 454ms/step\n","Epoch 270/1000\n","5/5 - 2s - loss: 1.2805e-06 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 271/1000\n","5/5 - 2s - loss: 1.2688e-06 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9836 - 2s/epoch - 351ms/step\n","Epoch 272/1000\n","5/5 - 2s - loss: 1.2579e-06 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 273/1000\n","5/5 - 2s - loss: 1.2468e-06 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 274/1000\n","5/5 - 2s - loss: 1.2358e-06 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 275/1000\n","5/5 - 2s - loss: 1.2248e-06 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 276/1000\n","5/5 - 2s - loss: 1.2147e-06 - accuracy: 1.0000 - val_loss: 0.1910 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 277/1000\n","5/5 - 2s - loss: 1.2043e-06 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 278/1000\n","5/5 - 2s - loss: 1.1932e-06 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 279/1000\n","5/5 - 2s - loss: 1.1830e-06 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9836 - 2s/epoch - 373ms/step\n","Epoch 280/1000\n","5/5 - 2s - loss: 1.1733e-06 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 281/1000\n","5/5 - 2s - loss: 1.1629e-06 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 282/1000\n","5/5 - 2s - loss: 1.1532e-06 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9836 - 2s/epoch - 398ms/step\n","Epoch 283/1000\n","5/5 - 2s - loss: 1.1432e-06 - accuracy: 1.0000 - val_loss: 0.1918 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 284/1000\n","5/5 - 2s - loss: 1.1335e-06 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 285/1000\n","5/5 - 2s - loss: 1.1240e-06 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 286/1000\n","5/5 - 1s - loss: 1.1141e-06 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 287/1000\n","5/5 - 2s - loss: 1.1049e-06 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 288/1000\n","5/5 - 2s - loss: 1.0958e-06 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 289/1000\n","5/5 - 2s - loss: 1.0861e-06 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 290/1000\n","5/5 - 2s - loss: 1.0771e-06 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 291/1000\n","5/5 - 2s - loss: 1.0680e-06 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 292/1000\n","5/5 - 2s - loss: 1.0593e-06 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 293/1000\n","5/5 - 2s - loss: 1.0502e-06 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 294/1000\n","5/5 - 2s - loss: 1.0413e-06 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 295/1000\n","5/5 - 2s - loss: 1.0329e-06 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9836 - 2s/epoch - 349ms/step\n","Epoch 296/1000\n","5/5 - 2s - loss: 1.0239e-06 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 297/1000\n","5/5 - 2s - loss: 1.0158e-06 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 298/1000\n","5/5 - 2s - loss: 1.0074e-06 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 299/1000\n","5/5 - 2s - loss: 9.9899e-07 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 300/1000\n","5/5 - 2s - loss: 9.9085e-07 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 301/1000\n","5/5 - 2s - loss: 9.8287e-07 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 302/1000\n","5/5 - 2s - loss: 9.7462e-07 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 303/1000\n","5/5 - 2s - loss: 9.6676e-07 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9836 - 2s/epoch - 397ms/step\n","Epoch 304/1000\n","5/5 - 2s - loss: 9.5884e-07 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9836 - 2s/epoch - 354ms/step\n","Epoch 305/1000\n","5/5 - 2s - loss: 9.5108e-07 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9836 - 2s/epoch - 407ms/step\n","Epoch 306/1000\n","5/5 - 2s - loss: 9.4304e-07 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9836 - 2s/epoch - 475ms/step\n","Epoch 307/1000\n","5/5 - 2s - loss: 9.3571e-07 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9836 - 2s/epoch - 397ms/step\n","Epoch 308/1000\n","5/5 - 3s - loss: 9.2807e-07 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9836 - 3s/epoch - 538ms/step\n","Epoch 309/1000\n","5/5 - 2s - loss: 9.2044e-07 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9836 - 2s/epoch - 425ms/step\n","Epoch 310/1000\n","5/5 - 2s - loss: 9.1297e-07 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9836 - 2s/epoch - 382ms/step\n","Epoch 311/1000\n","5/5 - 2s - loss: 9.0564e-07 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 312/1000\n","5/5 - 2s - loss: 8.9878e-07 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 313/1000\n","5/5 - 2s - loss: 8.9143e-07 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9836 - 2s/epoch - 386ms/step\n","Epoch 314/1000\n","5/5 - 2s - loss: 8.8435e-07 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9836 - 2s/epoch - 446ms/step\n","Epoch 315/1000\n","5/5 - 2s - loss: 8.7709e-07 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9836 - 2s/epoch - 447ms/step\n","Epoch 316/1000\n","5/5 - 2s - loss: 8.7033e-07 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9836 - 2s/epoch - 454ms/step\n","Epoch 317/1000\n","5/5 - 2s - loss: 8.6328e-07 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 318/1000\n","5/5 - 2s - loss: 8.5655e-07 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 319/1000\n","5/5 - 2s - loss: 8.4978e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9836 - 2s/epoch - 366ms/step\n","Epoch 320/1000\n","5/5 - 2s - loss: 8.4296e-07 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9836 - 2s/epoch - 400ms/step\n","Epoch 321/1000\n","5/5 - 2s - loss: 8.3654e-07 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9836 - 2s/epoch - 347ms/step\n","Epoch 322/1000\n","5/5 - 2s - loss: 8.2972e-07 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 323/1000\n","5/5 - 2s - loss: 8.2358e-07 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 324/1000\n","5/5 - 2s - loss: 8.1703e-07 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 325/1000\n","5/5 - 2s - loss: 8.1048e-07 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9836 - 2s/epoch - 322ms/step\n","Epoch 326/1000\n","5/5 - 2s - loss: 8.0442e-07 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9836 - 2s/epoch - 404ms/step\n","Epoch 327/1000\n","5/5 - 2s - loss: 7.9813e-07 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 328/1000\n","5/5 - 2s - loss: 7.9201e-07 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9836 - 2s/epoch - 351ms/step\n","Epoch 329/1000\n","5/5 - 2s - loss: 7.8605e-07 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 330/1000\n","5/5 - 2s - loss: 7.7997e-07 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 331/1000\n","5/5 - 2s - loss: 7.7415e-07 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 332/1000\n","5/5 - 2s - loss: 7.6803e-07 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9836 - 2s/epoch - 375ms/step\n","Epoch 333/1000\n","5/5 - 2s - loss: 7.6233e-07 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 334/1000\n","5/5 - 2s - loss: 7.5662e-07 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 335/1000\n","5/5 - 2s - loss: 7.5079e-07 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 336/1000\n","5/5 - 2s - loss: 7.4518e-07 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 337/1000\n","5/5 - 2s - loss: 7.3967e-07 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 338/1000\n","5/5 - 2s - loss: 7.3379e-07 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9836 - 2s/epoch - 355ms/step\n","Epoch 339/1000\n","5/5 - 2s - loss: 7.2852e-07 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 340/1000\n","5/5 - 2s - loss: 7.2314e-07 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 341/1000\n","5/5 - 2s - loss: 7.1760e-07 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 342/1000\n","5/5 - 2s - loss: 7.1227e-07 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 343/1000\n","5/5 - 2s - loss: 7.0687e-07 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 344/1000\n","5/5 - 2s - loss: 7.0159e-07 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 345/1000\n","5/5 - 2s - loss: 6.9645e-07 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 346/1000\n","5/5 - 2s - loss: 6.9122e-07 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 347/1000\n","5/5 - 2s - loss: 6.8607e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 348/1000\n","5/5 - 2s - loss: 6.8098e-07 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 349/1000\n","5/5 - 2s - loss: 6.7591e-07 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 350/1000\n","5/5 - 2s - loss: 6.7095e-07 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 351/1000\n","5/5 - 2s - loss: 6.6604e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 352/1000\n","5/5 - 2s - loss: 6.6142e-07 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 353/1000\n","5/5 - 2s - loss: 6.5635e-07 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 354/1000\n","5/5 - 2s - loss: 6.5170e-07 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 355/1000\n","5/5 - 2s - loss: 6.4705e-07 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 356/1000\n","5/5 - 2s - loss: 6.4220e-07 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9836 - 2s/epoch - 358ms/step\n","Epoch 357/1000\n","5/5 - 2s - loss: 6.3763e-07 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 358/1000\n","5/5 - 2s - loss: 6.3324e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9836 - 2s/epoch - 370ms/step\n","Epoch 359/1000\n","5/5 - 2s - loss: 6.2853e-07 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 360/1000\n","5/5 - 2s - loss: 6.2420e-07 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 361/1000\n","5/5 - 2s - loss: 6.1957e-07 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 362/1000\n","5/5 - 2s - loss: 6.1513e-07 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 363/1000\n","5/5 - 2s - loss: 6.1081e-07 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 364/1000\n","5/5 - 2s - loss: 6.0645e-07 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 365/1000\n","5/5 - 2s - loss: 6.0224e-07 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 366/1000\n","5/5 - 2s - loss: 5.9797e-07 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 367/1000\n","5/5 - 2s - loss: 5.9374e-07 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 368/1000\n","5/5 - 2s - loss: 5.8963e-07 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 369/1000\n","5/5 - 2s - loss: 5.8534e-07 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 370/1000\n","5/5 - 2s - loss: 5.8146e-07 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 371/1000\n","5/5 - 2s - loss: 5.7734e-07 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 372/1000\n","5/5 - 2s - loss: 5.7317e-07 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 373/1000\n","5/5 - 2s - loss: 5.6931e-07 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 374/1000\n","5/5 - 2s - loss: 5.6533e-07 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9836 - 2s/epoch - 385ms/step\n","Epoch 375/1000\n","5/5 - 2s - loss: 5.6134e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 2s/epoch - 322ms/step\n","Epoch 376/1000\n","5/5 - 2s - loss: 5.5761e-07 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9836 - 2s/epoch - 343ms/step\n","Epoch 377/1000\n","5/5 - 2s - loss: 5.5370e-07 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 378/1000\n","5/5 - 2s - loss: 5.4990e-07 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 379/1000\n","5/5 - 2s - loss: 5.4605e-07 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9836 - 2s/epoch - 351ms/step\n","Epoch 380/1000\n","5/5 - 2s - loss: 5.4237e-07 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 381/1000\n","5/5 - 2s - loss: 5.3879e-07 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 382/1000\n","5/5 - 2s - loss: 5.3497e-07 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 383/1000\n","5/5 - 2s - loss: 5.3141e-07 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 384/1000\n","5/5 - 1s - loss: 5.2773e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 385/1000\n","5/5 - 2s - loss: 5.2426e-07 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 386/1000\n","5/5 - 2s - loss: 5.2051e-07 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 387/1000\n","5/5 - 2s - loss: 5.1707e-07 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 388/1000\n","5/5 - 1s - loss: 5.1361e-07 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 389/1000\n","5/5 - 1s - loss: 5.1005e-07 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 390/1000\n","5/5 - 1s - loss: 5.0670e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 391/1000\n","5/5 - 1s - loss: 5.0319e-07 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 392/1000\n","5/5 - 1s - loss: 4.9990e-07 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 393/1000\n","5/5 - 2s - loss: 4.9652e-07 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 394/1000\n","5/5 - 2s - loss: 4.9324e-07 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9836 - 2s/epoch - 366ms/step\n","Epoch 395/1000\n","5/5 - 2s - loss: 4.8997e-07 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 396/1000\n","5/5 - 2s - loss: 4.8671e-07 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 397/1000\n","5/5 - 2s - loss: 4.8345e-07 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 398/1000\n","5/5 - 1s - loss: 4.8028e-07 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 399/1000\n","5/5 - 2s - loss: 4.7704e-07 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 400/1000\n","5/5 - 2s - loss: 4.7403e-07 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 401/1000\n","5/5 - 1s - loss: 4.7094e-07 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 402/1000\n","5/5 - 1s - loss: 4.6787e-07 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 403/1000\n","5/5 - 1s - loss: 4.6477e-07 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 404/1000\n","5/5 - 2s - loss: 4.6173e-07 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 405/1000\n","5/5 - 1s - loss: 4.5878e-07 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 406/1000\n","5/5 - 2s - loss: 4.5586e-07 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 407/1000\n","5/5 - 2s - loss: 4.5293e-07 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 408/1000\n","5/5 - 2s - loss: 4.4985e-07 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9836 - 2s/epoch - 352ms/step\n","Epoch 409/1000\n","5/5 - 2s - loss: 4.4695e-07 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 410/1000\n","5/5 - 2s - loss: 4.4419e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 411/1000\n","5/5 - 1s - loss: 4.4120e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 412/1000\n","5/5 - 2s - loss: 4.3839e-07 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 413/1000\n","5/5 - 1s - loss: 4.3561e-07 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 414/1000\n","5/5 - 2s - loss: 4.3276e-07 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 415/1000\n","5/5 - 1s - loss: 4.2992e-07 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 416/1000\n","5/5 - 1s - loss: 4.2718e-07 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 417/1000\n","5/5 - 1s - loss: 4.2436e-07 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 418/1000\n","5/5 - 2s - loss: 4.2173e-07 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 419/1000\n","5/5 - 1s - loss: 4.1890e-07 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 420/1000\n","5/5 - 2s - loss: 4.1634e-07 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9836 - 2s/epoch - 300ms/step\n","Epoch 421/1000\n","5/5 - 1s - loss: 4.1359e-07 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 422/1000\n","5/5 - 2s - loss: 4.1104e-07 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 423/1000\n","5/5 - 2s - loss: 4.0838e-07 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 424/1000\n","5/5 - 1s - loss: 4.0572e-07 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 425/1000\n","5/5 - 2s - loss: 4.0319e-07 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 426/1000\n","5/5 - 1s - loss: 4.0059e-07 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9836 - 1s/epoch - 287ms/step\n","Epoch 427/1000\n","5/5 - 2s - loss: 3.9818e-07 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 428/1000\n","5/5 - 2s - loss: 3.9567e-07 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 429/1000\n","5/5 - 1s - loss: 3.9321e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9836 - 1s/epoch - 283ms/step\n","Epoch 430/1000\n","5/5 - 1s - loss: 3.9077e-07 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9836 - 1s/epoch - 286ms/step\n","Epoch 431/1000\n","5/5 - 1s - loss: 3.8826e-07 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 432/1000\n","5/5 - 2s - loss: 3.8593e-07 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 433/1000\n","5/5 - 2s - loss: 3.8354e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 434/1000\n","5/5 - 2s - loss: 3.8109e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 435/1000\n","5/5 - 2s - loss: 3.7878e-07 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 436/1000\n","5/5 - 2s - loss: 3.7647e-07 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 437/1000\n","5/5 - 2s - loss: 3.7403e-07 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 438/1000\n","5/5 - 2s - loss: 3.7192e-07 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9836 - 2s/epoch - 389ms/step\n","Epoch 439/1000\n","5/5 - 2s - loss: 3.6951e-07 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 440/1000\n","5/5 - 2s - loss: 3.6726e-07 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 441/1000\n","5/5 - 2s - loss: 3.6499e-07 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 442/1000\n","5/5 - 2s - loss: 3.6265e-07 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 443/1000\n","5/5 - 2s - loss: 3.6065e-07 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 444/1000\n","5/5 - 2s - loss: 3.5843e-07 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 445/1000\n","5/5 - 2s - loss: 3.5621e-07 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 446/1000\n","5/5 - 2s - loss: 3.5409e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 447/1000\n","5/5 - 2s - loss: 3.5191e-07 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 448/1000\n","5/5 - 2s - loss: 3.4984e-07 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 449/1000\n","5/5 - 2s - loss: 3.4770e-07 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 450/1000\n","5/5 - 2s - loss: 3.4564e-07 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 451/1000\n","5/5 - 2s - loss: 3.4355e-07 - accuracy: 1.0000 - val_loss: 0.2089 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 452/1000\n","5/5 - 2s - loss: 3.4151e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 453/1000\n","5/5 - 2s - loss: 3.3947e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 454/1000\n","5/5 - 2s - loss: 3.3746e-07 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 455/1000\n","5/5 - 2s - loss: 3.3550e-07 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 456/1000\n","5/5 - 2s - loss: 3.3347e-07 - accuracy: 1.0000 - val_loss: 0.2093 - val_accuracy: 0.9836 - 2s/epoch - 351ms/step\n","Epoch 457/1000\n","5/5 - 2s - loss: 3.3156e-07 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 458/1000\n","5/5 - 1s - loss: 3.2960e-07 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 459/1000\n","5/5 - 2s - loss: 3.2755e-07 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 460/1000\n","5/5 - 2s - loss: 3.2573e-07 - accuracy: 1.0000 - val_loss: 0.2096 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 461/1000\n","5/5 - 2s - loss: 3.2383e-07 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 462/1000\n","5/5 - 2s - loss: 3.2186e-07 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 463/1000\n","5/5 - 1s - loss: 3.1995e-07 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 464/1000\n","5/5 - 2s - loss: 3.1809e-07 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 465/1000\n","5/5 - 2s - loss: 3.1621e-07 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 466/1000\n","5/5 - 2s - loss: 3.1439e-07 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 467/1000\n","5/5 - 2s - loss: 3.1250e-07 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 468/1000\n","5/5 - 2s - loss: 3.1069e-07 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 469/1000\n","5/5 - 2s - loss: 3.0886e-07 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9836 - 2s/epoch - 349ms/step\n","Epoch 470/1000\n","5/5 - 2s - loss: 3.0691e-07 - accuracy: 1.0000 - val_loss: 0.2105 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 471/1000\n","5/5 - 2s - loss: 3.0523e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 472/1000\n","5/5 - 2s - loss: 3.0337e-07 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9836 - 2s/epoch - 343ms/step\n","Epoch 473/1000\n","5/5 - 1s - loss: 3.0158e-07 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 474/1000\n","5/5 - 2s - loss: 2.9987e-07 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 475/1000\n","5/5 - 2s - loss: 2.9804e-07 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 476/1000\n","5/5 - 2s - loss: 2.9632e-07 - accuracy: 1.0000 - val_loss: 0.2110 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 477/1000\n","5/5 - 1s - loss: 2.9461e-07 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 478/1000\n","5/5 - 2s - loss: 2.9292e-07 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 479/1000\n","5/5 - 2s - loss: 2.9127e-07 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 480/1000\n","5/5 - 2s - loss: 2.8954e-07 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 481/1000\n","5/5 - 2s - loss: 2.8792e-07 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 482/1000\n","5/5 - 2s - loss: 2.8626e-07 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 483/1000\n","5/5 - 2s - loss: 2.8463e-07 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 484/1000\n","5/5 - 2s - loss: 2.8301e-07 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 485/1000\n","5/5 - 2s - loss: 2.8133e-07 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 486/1000\n","5/5 - 1s - loss: 2.7982e-07 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 487/1000\n","5/5 - 2s - loss: 2.7825e-07 - accuracy: 1.0000 - val_loss: 0.2119 - val_accuracy: 0.9836 - 2s/epoch - 343ms/step\n","Epoch 488/1000\n","5/5 - 2s - loss: 2.7664e-07 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9836 - 2s/epoch - 389ms/step\n","Epoch 489/1000\n","5/5 - 2s - loss: 2.7508e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 490/1000\n","5/5 - 1s - loss: 2.7354e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 491/1000\n","5/5 - 2s - loss: 2.7201e-07 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 492/1000\n","5/5 - 1s - loss: 2.7053e-07 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 493/1000\n","5/5 - 2s - loss: 2.6898e-07 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 494/1000\n","5/5 - 2s - loss: 2.6752e-07 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 495/1000\n","5/5 - 1s - loss: 2.6607e-07 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 496/1000\n","5/5 - 1s - loss: 2.6452e-07 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9836 - 1s/epoch - 287ms/step\n","Epoch 497/1000\n","5/5 - 1s - loss: 2.6314e-07 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9836 - 1s/epoch - 284ms/step\n","Epoch 498/1000\n","5/5 - 2s - loss: 2.6168e-07 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 499/1000\n","5/5 - 2s - loss: 2.6025e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9836 - 2s/epoch - 397ms/step\n","Epoch 500/1000\n","5/5 - 2s - loss: 2.5881e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 501/1000\n","5/5 - 2s - loss: 2.5740e-07 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 502/1000\n","5/5 - 1s - loss: 2.5596e-07 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 503/1000\n","5/5 - 2s - loss: 2.5461e-07 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 504/1000\n","5/5 - 2s - loss: 2.5317e-07 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 505/1000\n","5/5 - 1s - loss: 2.5186e-07 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 506/1000\n","5/5 - 1s - loss: 2.5047e-07 - accuracy: 1.0000 - val_loss: 0.2134 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 507/1000\n","5/5 - 2s - loss: 2.4911e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9836 - 2s/epoch - 349ms/step\n","Epoch 508/1000\n","5/5 - 2s - loss: 2.4774e-07 - accuracy: 1.0000 - val_loss: 0.2136 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 509/1000\n","5/5 - 1s - loss: 2.4636e-07 - accuracy: 1.0000 - val_loss: 0.2136 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 510/1000\n","5/5 - 1s - loss: 2.4504e-07 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 511/1000\n","5/5 - 2s - loss: 2.4374e-07 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 512/1000\n","5/5 - 2s - loss: 2.4240e-07 - accuracy: 1.0000 - val_loss: 0.2139 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 513/1000\n","5/5 - 2s - loss: 2.4113e-07 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 514/1000\n","5/5 - 1s - loss: 2.3983e-07 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 515/1000\n","5/5 - 2s - loss: 2.3859e-07 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 516/1000\n","5/5 - 1s - loss: 2.3732e-07 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9836 - 1s/epoch - 286ms/step\n","Epoch 517/1000\n","5/5 - 1s - loss: 2.3605e-07 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 518/1000\n","5/5 - 1s - loss: 2.3477e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 519/1000\n","5/5 - 1s - loss: 2.3353e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 520/1000\n","5/5 - 2s - loss: 2.3229e-07 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 521/1000\n","5/5 - 2s - loss: 2.3105e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 522/1000\n","5/5 - 1s - loss: 2.2987e-07 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 523/1000\n","5/5 - 1s - loss: 2.2861e-07 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 524/1000\n","5/5 - 2s - loss: 2.2739e-07 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 525/1000\n","5/5 - 1s - loss: 2.2619e-07 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 526/1000\n","5/5 - 2s - loss: 2.2495e-07 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 527/1000\n","5/5 - 2s - loss: 2.2382e-07 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 528/1000\n","5/5 - 1s - loss: 2.2257e-07 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 529/1000\n","5/5 - 1s - loss: 2.2139e-07 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 530/1000\n","5/5 - 2s - loss: 2.2017e-07 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 531/1000\n","5/5 - 2s - loss: 2.1905e-07 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 532/1000\n","5/5 - 2s - loss: 2.1787e-07 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 533/1000\n","5/5 - 2s - loss: 2.1669e-07 - accuracy: 1.0000 - val_loss: 0.2155 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 534/1000\n","5/5 - 1s - loss: 2.1557e-07 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 535/1000\n","5/5 - 2s - loss: 2.1441e-07 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 536/1000\n","5/5 - 2s - loss: 2.1326e-07 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 537/1000\n","5/5 - 2s - loss: 2.1213e-07 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 538/1000\n","5/5 - 2s - loss: 2.1108e-07 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 539/1000\n","5/5 - 2s - loss: 2.0987e-07 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 540/1000\n","5/5 - 1s - loss: 2.0883e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 541/1000\n","5/5 - 1s - loss: 2.0769e-07 - accuracy: 1.0000 - val_loss: 0.2162 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 542/1000\n","5/5 - 1s - loss: 2.0660e-07 - accuracy: 1.0000 - val_loss: 0.2162 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 543/1000\n","5/5 - 2s - loss: 2.0554e-07 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 544/1000\n","5/5 - 2s - loss: 2.0446e-07 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 545/1000\n","5/5 - 1s - loss: 2.0345e-07 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 546/1000\n","5/5 - 2s - loss: 2.0235e-07 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 547/1000\n","5/5 - 2s - loss: 2.0129e-07 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9836 - 2s/epoch - 373ms/step\n","Epoch 548/1000\n","5/5 - 2s - loss: 2.0032e-07 - accuracy: 1.0000 - val_loss: 0.2167 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 549/1000\n","5/5 - 2s - loss: 1.9920e-07 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 550/1000\n","5/5 - 2s - loss: 1.9823e-07 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9836 - 2s/epoch - 388ms/step\n","Epoch 551/1000\n","5/5 - 2s - loss: 1.9714e-07 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 552/1000\n","5/5 - 2s - loss: 1.9618e-07 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 553/1000\n","5/5 - 1s - loss: 1.9509e-07 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 554/1000\n","5/5 - 2s - loss: 1.9409e-07 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 555/1000\n","5/5 - 1s - loss: 1.9308e-07 - accuracy: 1.0000 - val_loss: 0.2172 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 556/1000\n","5/5 - 2s - loss: 1.9209e-07 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 557/1000\n","5/5 - 2s - loss: 1.9105e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 558/1000\n","5/5 - 2s - loss: 1.9007e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 559/1000\n","5/5 - 2s - loss: 1.8907e-07 - accuracy: 1.0000 - val_loss: 0.2175 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 560/1000\n","5/5 - 2s - loss: 1.8815e-07 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 561/1000\n","5/5 - 2s - loss: 1.8714e-07 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 562/1000\n","5/5 - 1s - loss: 1.8616e-07 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 563/1000\n","5/5 - 2s - loss: 1.8522e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 564/1000\n","5/5 - 2s - loss: 1.8428e-07 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 565/1000\n","5/5 - 2s - loss: 1.8331e-07 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 566/1000\n","5/5 - 1s - loss: 1.8238e-07 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 567/1000\n","5/5 - 2s - loss: 1.8143e-07 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 568/1000\n","5/5 - 2s - loss: 1.8054e-07 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 569/1000\n","5/5 - 2s - loss: 1.7958e-07 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 570/1000\n","5/5 - 2s - loss: 1.7868e-07 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 571/1000\n","5/5 - 2s - loss: 1.7780e-07 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9836 - 2s/epoch - 347ms/step\n","Epoch 572/1000\n","5/5 - 1s - loss: 1.7692e-07 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 573/1000\n","5/5 - 2s - loss: 1.7602e-07 - accuracy: 1.0000 - val_loss: 0.2186 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 574/1000\n","5/5 - 2s - loss: 1.7517e-07 - accuracy: 1.0000 - val_loss: 0.2186 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 575/1000\n","5/5 - 1s - loss: 1.7425e-07 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 576/1000\n","5/5 - 2s - loss: 1.7342e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 577/1000\n","5/5 - 2s - loss: 1.7257e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 578/1000\n","5/5 - 2s - loss: 1.7168e-07 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 579/1000\n","5/5 - 2s - loss: 1.7090e-07 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 580/1000\n","5/5 - 2s - loss: 1.7002e-07 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 581/1000\n","5/5 - 2s - loss: 1.6921e-07 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 582/1000\n","5/5 - 1s - loss: 1.6836e-07 - accuracy: 1.0000 - val_loss: 0.2192 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 583/1000\n","5/5 - 2s - loss: 1.6756e-07 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 584/1000\n","5/5 - 2s - loss: 1.6674e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 585/1000\n","5/5 - 2s - loss: 1.6591e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9836 - 2s/epoch - 384ms/step\n","Epoch 586/1000\n","5/5 - 2s - loss: 1.6515e-07 - accuracy: 1.0000 - val_loss: 0.2195 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 587/1000\n","5/5 - 2s - loss: 1.6433e-07 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 588/1000\n","5/5 - 2s - loss: 1.6355e-07 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 589/1000\n","5/5 - 2s - loss: 1.6280e-07 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 590/1000\n","5/5 - 2s - loss: 1.6203e-07 - accuracy: 1.0000 - val_loss: 0.2198 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 591/1000\n","5/5 - 2s - loss: 1.6122e-07 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 592/1000\n","5/5 - 2s - loss: 1.6045e-07 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 593/1000\n","5/5 - 1s - loss: 1.5971e-07 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 594/1000\n","5/5 - 1s - loss: 1.5897e-07 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 595/1000\n","5/5 - 2s - loss: 1.5826e-07 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 596/1000\n","5/5 - 2s - loss: 1.5749e-07 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 597/1000\n","5/5 - 2s - loss: 1.5677e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 598/1000\n","5/5 - 2s - loss: 1.5607e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 599/1000\n","5/5 - 2s - loss: 1.5531e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 600/1000\n","5/5 - 2s - loss: 1.5458e-07 - accuracy: 1.0000 - val_loss: 0.2205 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 601/1000\n","5/5 - 2s - loss: 1.5386e-07 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 602/1000\n","5/5 - 2s - loss: 1.5314e-07 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 603/1000\n","5/5 - 2s - loss: 1.5243e-07 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 604/1000\n","5/5 - 2s - loss: 1.5172e-07 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 605/1000\n","5/5 - 2s - loss: 1.5102e-07 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 606/1000\n","5/5 - 1s - loss: 1.5033e-07 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 607/1000\n","5/5 - 1s - loss: 1.4959e-07 - accuracy: 1.0000 - val_loss: 0.2210 - val_accuracy: 0.9836 - 1s/epoch - 278ms/step\n","Epoch 608/1000\n","5/5 - 2s - loss: 1.4891e-07 - accuracy: 1.0000 - val_loss: 0.2211 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 609/1000\n","5/5 - 2s - loss: 1.4821e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 610/1000\n","5/5 - 1s - loss: 1.4754e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9836 - 1s/epoch - 286ms/step\n","Epoch 611/1000\n","5/5 - 1s - loss: 1.4686e-07 - accuracy: 1.0000 - val_loss: 0.2213 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 612/1000\n","5/5 - 1s - loss: 1.4621e-07 - accuracy: 1.0000 - val_loss: 0.2214 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 613/1000\n","5/5 - 2s - loss: 1.4550e-07 - accuracy: 1.0000 - val_loss: 0.2214 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 614/1000\n","5/5 - 1s - loss: 1.4484e-07 - accuracy: 1.0000 - val_loss: 0.2215 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 615/1000\n","5/5 - 2s - loss: 1.4416e-07 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 616/1000\n","5/5 - 1s - loss: 1.4352e-07 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 617/1000\n","5/5 - 2s - loss: 1.4286e-07 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 618/1000\n","5/5 - 1s - loss: 1.4220e-07 - accuracy: 1.0000 - val_loss: 0.2218 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 619/1000\n","5/5 - 2s - loss: 1.4152e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 620/1000\n","5/5 - 2s - loss: 1.4090e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9836 - 2s/epoch - 300ms/step\n","Epoch 621/1000\n","5/5 - 2s - loss: 1.4021e-07 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 622/1000\n","5/5 - 2s - loss: 1.3962e-07 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 623/1000\n","5/5 - 2s - loss: 1.3893e-07 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 624/1000\n","5/5 - 2s - loss: 1.3829e-07 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 625/1000\n","5/5 - 2s - loss: 1.3768e-07 - accuracy: 1.0000 - val_loss: 0.2223 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 626/1000\n","5/5 - 2s - loss: 1.3704e-07 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 627/1000\n","5/5 - 2s - loss: 1.3640e-07 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 628/1000\n","5/5 - 2s - loss: 1.3575e-07 - accuracy: 1.0000 - val_loss: 0.2225 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 629/1000\n","5/5 - 2s - loss: 1.3514e-07 - accuracy: 1.0000 - val_loss: 0.2226 - val_accuracy: 0.9836 - 2s/epoch - 375ms/step\n","Epoch 630/1000\n","5/5 - 2s - loss: 1.3453e-07 - accuracy: 1.0000 - val_loss: 0.2226 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 631/1000\n","5/5 - 1s - loss: 1.3389e-07 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 632/1000\n","5/5 - 1s - loss: 1.3328e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 633/1000\n","5/5 - 1s - loss: 1.3266e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 634/1000\n","5/5 - 2s - loss: 1.3203e-07 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 635/1000\n","5/5 - 2s - loss: 1.3142e-07 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 636/1000\n","5/5 - 1s - loss: 1.3081e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9836 - 1s/epoch - 282ms/step\n","Epoch 637/1000\n","5/5 - 1s - loss: 1.3022e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9836 - 1s/epoch - 284ms/step\n","Epoch 638/1000\n","5/5 - 1s - loss: 1.2964e-07 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 639/1000\n","5/5 - 2s - loss: 1.2904e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 640/1000\n","5/5 - 1s - loss: 1.2842e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 641/1000\n","5/5 - 2s - loss: 1.2780e-07 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 642/1000\n","5/5 - 1s - loss: 1.2722e-07 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 643/1000\n","5/5 - 2s - loss: 1.2669e-07 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 644/1000\n","5/5 - 1s - loss: 1.2605e-07 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 645/1000\n","5/5 - 1s - loss: 1.2549e-07 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 646/1000\n","5/5 - 1s - loss: 1.2486e-07 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 647/1000\n","5/5 - 2s - loss: 1.2430e-07 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 648/1000\n","5/5 - 2s - loss: 1.2374e-07 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 649/1000\n","5/5 - 1s - loss: 1.2318e-07 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 650/1000\n","5/5 - 1s - loss: 1.2257e-07 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 651/1000\n","5/5 - 1s - loss: 1.2204e-07 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 652/1000\n","5/5 - 1s - loss: 1.2147e-07 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9836 - 1s/epoch - 284ms/step\n","Epoch 653/1000\n","5/5 - 2s - loss: 1.2092e-07 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 654/1000\n","5/5 - 2s - loss: 1.2035e-07 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 655/1000\n","5/5 - 1s - loss: 1.1981e-07 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 656/1000\n","5/5 - 1s - loss: 1.1924e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9836 - 1s/epoch - 282ms/step\n","Epoch 657/1000\n","5/5 - 1s - loss: 1.1871e-07 - accuracy: 1.0000 - val_loss: 0.2245 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 658/1000\n","5/5 - 2s - loss: 1.1815e-07 - accuracy: 1.0000 - val_loss: 0.2245 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 659/1000\n","5/5 - 1s - loss: 1.1764e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9836 - 1s/epoch - 287ms/step\n","Epoch 660/1000\n","5/5 - 1s - loss: 1.1708e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 661/1000\n","5/5 - 2s - loss: 1.1653e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 662/1000\n","5/5 - 2s - loss: 1.1597e-07 - accuracy: 1.0000 - val_loss: 0.2248 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 663/1000\n","5/5 - 2s - loss: 1.1543e-07 - accuracy: 1.0000 - val_loss: 0.2249 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 664/1000\n","5/5 - 2s - loss: 1.1491e-07 - accuracy: 1.0000 - val_loss: 0.2249 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 665/1000\n","5/5 - 2s - loss: 1.1435e-07 - accuracy: 1.0000 - val_loss: 0.2250 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 666/1000\n","5/5 - 2s - loss: 1.1383e-07 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 667/1000\n","5/5 - 2s - loss: 1.1332e-07 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9836 - 2s/epoch - 363ms/step\n","Epoch 668/1000\n","5/5 - 2s - loss: 1.1274e-07 - accuracy: 1.0000 - val_loss: 0.2252 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 669/1000\n","5/5 - 1s - loss: 1.1223e-07 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 670/1000\n","5/5 - 1s - loss: 1.1167e-07 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 671/1000\n","5/5 - 2s - loss: 1.1115e-07 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 672/1000\n","5/5 - 2s - loss: 1.1063e-07 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 673/1000\n","5/5 - 1s - loss: 1.1010e-07 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9836 - 1s/epoch - 286ms/step\n","Epoch 674/1000\n","5/5 - 2s - loss: 1.0959e-07 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 675/1000\n","5/5 - 2s - loss: 1.0906e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 676/1000\n","5/5 - 1s - loss: 1.0852e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 677/1000\n","5/5 - 2s - loss: 1.0802e-07 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 678/1000\n","5/5 - 2s - loss: 1.0752e-07 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 679/1000\n","5/5 - 1s - loss: 1.0701e-07 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 680/1000\n","5/5 - 2s - loss: 1.0650e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 681/1000\n","5/5 - 2s - loss: 1.0600e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 682/1000\n","5/5 - 2s - loss: 1.0550e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 683/1000\n","5/5 - 2s - loss: 1.0500e-07 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9836 - 2s/epoch - 300ms/step\n","Epoch 684/1000\n","5/5 - 2s - loss: 1.0449e-07 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 685/1000\n","5/5 - 2s - loss: 1.0401e-07 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 686/1000\n","5/5 - 2s - loss: 1.0352e-07 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9836 - 2s/epoch - 300ms/step\n","Epoch 687/1000\n","5/5 - 2s - loss: 1.0303e-07 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9836 - 2s/epoch - 372ms/step\n","Epoch 688/1000\n","5/5 - 2s - loss: 1.0255e-07 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 689/1000\n","5/5 - 2s - loss: 1.0207e-07 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 690/1000\n","5/5 - 2s - loss: 1.0158e-07 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 691/1000\n","5/5 - 2s - loss: 1.0112e-07 - accuracy: 1.0000 - val_loss: 0.2267 - val_accuracy: 0.9836 - 2s/epoch - 394ms/step\n","Epoch 692/1000\n","5/5 - 2s - loss: 1.0062e-07 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9836 - 2s/epoch - 351ms/step\n","Epoch 693/1000\n","5/5 - 2s - loss: 1.0016e-07 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 694/1000\n","5/5 - 2s - loss: 9.9676e-08 - accuracy: 1.0000 - val_loss: 0.2269 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 695/1000\n","5/5 - 2s - loss: 9.9196e-08 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 696/1000\n","5/5 - 2s - loss: 9.8745e-08 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 697/1000\n","5/5 - 2s - loss: 9.8269e-08 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 698/1000\n","5/5 - 2s - loss: 9.7818e-08 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 699/1000\n","5/5 - 2s - loss: 9.7335e-08 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 700/1000\n","5/5 - 2s - loss: 9.6871e-08 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 701/1000\n","5/5 - 2s - loss: 9.6417e-08 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 702/1000\n","5/5 - 2s - loss: 9.5989e-08 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9836 - 2s/epoch - 348ms/step\n","Epoch 703/1000\n","5/5 - 2s - loss: 9.5487e-08 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 704/1000\n","5/5 - 2s - loss: 9.5038e-08 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 705/1000\n","5/5 - 2s - loss: 9.4616e-08 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 706/1000\n","5/5 - 2s - loss: 9.4144e-08 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 707/1000\n","5/5 - 2s - loss: 9.3715e-08 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 708/1000\n","5/5 - 2s - loss: 9.3258e-08 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 709/1000\n","5/5 - 2s - loss: 9.2838e-08 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 710/1000\n","5/5 - 2s - loss: 9.2379e-08 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 711/1000\n","5/5 - 2s - loss: 9.1947e-08 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9836 - 2s/epoch - 372ms/step\n","Epoch 712/1000\n","5/5 - 2s - loss: 9.1506e-08 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 713/1000\n","5/5 - 2s - loss: 9.1073e-08 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 714/1000\n","5/5 - 2s - loss: 9.0640e-08 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 715/1000\n","5/5 - 2s - loss: 9.0223e-08 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9836 - 2s/epoch - 354ms/step\n","Epoch 716/1000\n","5/5 - 2s - loss: 8.9782e-08 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9836 - 2s/epoch - 341ms/step\n","Epoch 717/1000\n","5/5 - 2s - loss: 8.9378e-08 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 718/1000\n","5/5 - 2s - loss: 8.8962e-08 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 719/1000\n","5/5 - 1s - loss: 8.8540e-08 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 720/1000\n","5/5 - 2s - loss: 8.8134e-08 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 721/1000\n","5/5 - 1s - loss: 8.7735e-08 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 722/1000\n","5/5 - 1s - loss: 8.7328e-08 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 723/1000\n","5/5 - 1s - loss: 8.6920e-08 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 724/1000\n","5/5 - 2s - loss: 8.6518e-08 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 725/1000\n","5/5 - 1s - loss: 8.6127e-08 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 726/1000\n","5/5 - 2s - loss: 8.5735e-08 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 727/1000\n","5/5 - 1s - loss: 8.5357e-08 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 728/1000\n","5/5 - 1s - loss: 8.4949e-08 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 729/1000\n","5/5 - 1s - loss: 8.4565e-08 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 730/1000\n","5/5 - 2s - loss: 8.4187e-08 - accuracy: 1.0000 - val_loss: 0.2292 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 731/1000\n","5/5 - 1s - loss: 8.3788e-08 - accuracy: 1.0000 - val_loss: 0.2292 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 732/1000\n","5/5 - 1s - loss: 8.3419e-08 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 733/1000\n","5/5 - 1s - loss: 8.3038e-08 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 0.9836 - 1s/epoch - 280ms/step\n","Epoch 734/1000\n","5/5 - 2s - loss: 8.2672e-08 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 735/1000\n","5/5 - 1s - loss: 8.2273e-08 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 736/1000\n","5/5 - 1s - loss: 8.1934e-08 - accuracy: 1.0000 - val_loss: 0.2296 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 737/1000\n","5/5 - 2s - loss: 8.1541e-08 - accuracy: 1.0000 - val_loss: 0.2296 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 738/1000\n","5/5 - 2s - loss: 8.1191e-08 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 739/1000\n","5/5 - 2s - loss: 8.0820e-08 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 740/1000\n","5/5 - 2s - loss: 8.0468e-08 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 741/1000\n","5/5 - 2s - loss: 8.0115e-08 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 742/1000\n","5/5 - 2s - loss: 7.9749e-08 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 743/1000\n","5/5 - 2s - loss: 7.9389e-08 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 744/1000\n","5/5 - 2s - loss: 7.9040e-08 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 745/1000\n","5/5 - 1s - loss: 7.8692e-08 - accuracy: 1.0000 - val_loss: 0.2301 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 746/1000\n","5/5 - 1s - loss: 7.8351e-08 - accuracy: 1.0000 - val_loss: 0.2302 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 747/1000\n","5/5 - 1s - loss: 7.7990e-08 - accuracy: 1.0000 - val_loss: 0.2302 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 748/1000\n","5/5 - 2s - loss: 7.7632e-08 - accuracy: 1.0000 - val_loss: 0.2303 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 749/1000\n","5/5 - 1s - loss: 7.7308e-08 - accuracy: 1.0000 - val_loss: 0.2304 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 750/1000\n","5/5 - 1s - loss: 7.6970e-08 - accuracy: 1.0000 - val_loss: 0.2304 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 751/1000\n","5/5 - 2s - loss: 7.6620e-08 - accuracy: 1.0000 - val_loss: 0.2305 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 752/1000\n","5/5 - 1s - loss: 7.6302e-08 - accuracy: 1.0000 - val_loss: 0.2305 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 753/1000\n","5/5 - 1s - loss: 7.5957e-08 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 754/1000\n","5/5 - 2s - loss: 7.5637e-08 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 755/1000\n","5/5 - 1s - loss: 7.5311e-08 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 756/1000\n","5/5 - 1s - loss: 7.4971e-08 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 757/1000\n","5/5 - 2s - loss: 7.4653e-08 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9836 - 2s/epoch - 320ms/step\n","Epoch 758/1000\n","5/5 - 2s - loss: 7.4332e-08 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 759/1000\n","5/5 - 1s - loss: 7.4011e-08 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 760/1000\n","5/5 - 1s - loss: 7.3687e-08 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 761/1000\n","5/5 - 1s - loss: 7.3374e-08 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.9836 - 1s/epoch - 289ms/step\n","Epoch 762/1000\n","5/5 - 2s - loss: 7.3065e-08 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9836 - 2s/epoch - 317ms/step\n","Epoch 763/1000\n","5/5 - 1s - loss: 7.2746e-08 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9836 - 1s/epoch - 295ms/step\n","Epoch 764/1000\n","5/5 - 1s - loss: 7.2436e-08 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9836 - 1s/epoch - 286ms/step\n","Epoch 765/1000\n","5/5 - 1s - loss: 7.2124e-08 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 766/1000\n","5/5 - 1s - loss: 7.1822e-08 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 767/1000\n","5/5 - 2s - loss: 7.1509e-08 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 768/1000\n","5/5 - 2s - loss: 7.1204e-08 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 769/1000\n","5/5 - 2s - loss: 7.0908e-08 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 770/1000\n","5/5 - 2s - loss: 7.0606e-08 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 771/1000\n","5/5 - 2s - loss: 7.0314e-08 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9836 - 2s/epoch - 352ms/step\n","Epoch 772/1000\n","5/5 - 2s - loss: 7.0020e-08 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9836 - 2s/epoch - 350ms/step\n","Epoch 773/1000\n","5/5 - 2s - loss: 6.9712e-08 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 774/1000\n","5/5 - 2s - loss: 6.9433e-08 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 775/1000\n","5/5 - 2s - loss: 6.9132e-08 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 776/1000\n","5/5 - 2s - loss: 6.8855e-08 - accuracy: 1.0000 - val_loss: 0.2320 - val_accuracy: 0.9836 - 2s/epoch - 337ms/step\n","Epoch 777/1000\n","5/5 - 2s - loss: 6.8580e-08 - accuracy: 1.0000 - val_loss: 0.2320 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 778/1000\n","5/5 - 2s - loss: 6.8295e-08 - accuracy: 1.0000 - val_loss: 0.2321 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 779/1000\n","5/5 - 2s - loss: 6.8016e-08 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 780/1000\n","5/5 - 2s - loss: 6.7739e-08 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9836 - 2s/epoch - 365ms/step\n","Epoch 781/1000\n","5/5 - 2s - loss: 6.7453e-08 - accuracy: 1.0000 - val_loss: 0.2323 - val_accuracy: 0.9836 - 2s/epoch - 385ms/step\n","Epoch 782/1000\n","5/5 - 2s - loss: 6.7195e-08 - accuracy: 1.0000 - val_loss: 0.2323 - val_accuracy: 0.9836 - 2s/epoch - 370ms/step\n","Epoch 783/1000\n","5/5 - 2s - loss: 6.6913e-08 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9836 - 2s/epoch - 348ms/step\n","Epoch 784/1000\n","5/5 - 2s - loss: 6.6648e-08 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 785/1000\n","5/5 - 2s - loss: 6.6373e-08 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9836 - 2s/epoch - 357ms/step\n","Epoch 786/1000\n","5/5 - 2s - loss: 6.6110e-08 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 787/1000\n","5/5 - 2s - loss: 6.5856e-08 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 788/1000\n","5/5 - 2s - loss: 6.5603e-08 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 0.9836 - 2s/epoch - 349ms/step\n","Epoch 789/1000\n","5/5 - 2s - loss: 6.5344e-08 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9836 - 2s/epoch - 365ms/step\n","Epoch 790/1000\n","5/5 - 2s - loss: 6.5086e-08 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 791/1000\n","5/5 - 2s - loss: 6.4849e-08 - accuracy: 1.0000 - val_loss: 0.2329 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 792/1000\n","5/5 - 2s - loss: 6.4623e-08 - accuracy: 1.0000 - val_loss: 0.2329 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 793/1000\n","5/5 - 2s - loss: 6.4373e-08 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 794/1000\n","5/5 - 2s - loss: 6.4129e-08 - accuracy: 1.0000 - val_loss: 0.2331 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 795/1000\n","5/5 - 2s - loss: 6.3893e-08 - accuracy: 1.0000 - val_loss: 0.2331 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 796/1000\n","5/5 - 2s - loss: 6.3655e-08 - accuracy: 1.0000 - val_loss: 0.2332 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 797/1000\n","5/5 - 2s - loss: 6.3428e-08 - accuracy: 1.0000 - val_loss: 0.2332 - val_accuracy: 0.9836 - 2s/epoch - 357ms/step\n","Epoch 798/1000\n","5/5 - 2s - loss: 6.3191e-08 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 799/1000\n","5/5 - 2s - loss: 6.2970e-08 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 800/1000\n","5/5 - 2s - loss: 6.2742e-08 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 801/1000\n","5/5 - 2s - loss: 6.2520e-08 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 802/1000\n","5/5 - 2s - loss: 6.2304e-08 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 803/1000\n","5/5 - 2s - loss: 6.2089e-08 - accuracy: 1.0000 - val_loss: 0.2336 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 804/1000\n","5/5 - 2s - loss: 6.1871e-08 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 805/1000\n","5/5 - 2s - loss: 6.1655e-08 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 806/1000\n","5/5 - 2s - loss: 6.1450e-08 - accuracy: 1.0000 - val_loss: 0.2338 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 807/1000\n","5/5 - 2s - loss: 6.1230e-08 - accuracy: 1.0000 - val_loss: 0.2338 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 808/1000\n","5/5 - 2s - loss: 6.1022e-08 - accuracy: 1.0000 - val_loss: 0.2339 - val_accuracy: 0.9836 - 2s/epoch - 322ms/step\n","Epoch 809/1000\n","5/5 - 2s - loss: 6.0837e-08 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9836 - 2s/epoch - 322ms/step\n","Epoch 810/1000\n","5/5 - 2s - loss: 6.0619e-08 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 811/1000\n","5/5 - 2s - loss: 6.0411e-08 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9836 - 2s/epoch - 352ms/step\n","Epoch 812/1000\n","5/5 - 2s - loss: 6.0221e-08 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9836 - 2s/epoch - 347ms/step\n","Epoch 813/1000\n","5/5 - 2s - loss: 6.0028e-08 - accuracy: 1.0000 - val_loss: 0.2342 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 814/1000\n","5/5 - 2s - loss: 5.9808e-08 - accuracy: 1.0000 - val_loss: 0.2343 - val_accuracy: 0.9836 - 2s/epoch - 365ms/step\n","Epoch 815/1000\n","5/5 - 2s - loss: 5.9629e-08 - accuracy: 1.0000 - val_loss: 0.2343 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 816/1000\n","5/5 - 2s - loss: 5.9437e-08 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 817/1000\n","5/5 - 2s - loss: 5.9228e-08 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 818/1000\n","5/5 - 2s - loss: 5.9044e-08 - accuracy: 1.0000 - val_loss: 0.2345 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 819/1000\n","5/5 - 2s - loss: 5.8851e-08 - accuracy: 1.0000 - val_loss: 0.2346 - val_accuracy: 0.9836 - 2s/epoch - 382ms/step\n","Epoch 820/1000\n","5/5 - 2s - loss: 5.8662e-08 - accuracy: 1.0000 - val_loss: 0.2346 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 821/1000\n","5/5 - 2s - loss: 5.8476e-08 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 822/1000\n","5/5 - 2s - loss: 5.8287e-08 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 823/1000\n","5/5 - 2s - loss: 5.8097e-08 - accuracy: 1.0000 - val_loss: 0.2348 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 824/1000\n","5/5 - 2s - loss: 5.7924e-08 - accuracy: 1.0000 - val_loss: 0.2349 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 825/1000\n","5/5 - 2s - loss: 5.7739e-08 - accuracy: 1.0000 - val_loss: 0.2349 - val_accuracy: 0.9836 - 2s/epoch - 308ms/step\n","Epoch 826/1000\n","5/5 - 2s - loss: 5.7557e-08 - accuracy: 1.0000 - val_loss: 0.2350 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 827/1000\n","5/5 - 2s - loss: 5.7387e-08 - accuracy: 1.0000 - val_loss: 0.2350 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 828/1000\n","5/5 - 2s - loss: 5.7214e-08 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 829/1000\n","5/5 - 2s - loss: 5.7018e-08 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 830/1000\n","5/5 - 2s - loss: 5.6860e-08 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 831/1000\n","5/5 - 2s - loss: 5.6681e-08 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 832/1000\n","5/5 - 2s - loss: 5.6518e-08 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 833/1000\n","5/5 - 2s - loss: 5.6351e-08 - accuracy: 1.0000 - val_loss: 0.2354 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 834/1000\n","5/5 - 1s - loss: 5.6178e-08 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 835/1000\n","5/5 - 2s - loss: 5.6018e-08 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 836/1000\n","5/5 - 1s - loss: 5.5847e-08 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 837/1000\n","5/5 - 2s - loss: 5.5686e-08 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 838/1000\n","5/5 - 1s - loss: 5.5513e-08 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 839/1000\n","5/5 - 1s - loss: 5.5346e-08 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9836 - 1s/epoch - 290ms/step\n","Epoch 840/1000\n","5/5 - 2s - loss: 5.5178e-08 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 841/1000\n","5/5 - 2s - loss: 5.5031e-08 - accuracy: 1.0000 - val_loss: 0.2359 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 842/1000\n","5/5 - 1s - loss: 5.4855e-08 - accuracy: 1.0000 - val_loss: 0.2359 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 843/1000\n","5/5 - 2s - loss: 5.4686e-08 - accuracy: 1.0000 - val_loss: 0.2360 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 844/1000\n","5/5 - 2s - loss: 5.4533e-08 - accuracy: 1.0000 - val_loss: 0.2361 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 845/1000\n","5/5 - 2s - loss: 5.4367e-08 - accuracy: 1.0000 - val_loss: 0.2361 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 846/1000\n","5/5 - 2s - loss: 5.4216e-08 - accuracy: 1.0000 - val_loss: 0.2362 - val_accuracy: 0.9836 - 2s/epoch - 408ms/step\n","Epoch 847/1000\n","5/5 - 2s - loss: 5.4056e-08 - accuracy: 1.0000 - val_loss: 0.2362 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 848/1000\n","5/5 - 2s - loss: 5.3897e-08 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9836 - 2s/epoch - 361ms/step\n","Epoch 849/1000\n","5/5 - 2s - loss: 5.3732e-08 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9836 - 2s/epoch - 344ms/step\n","Epoch 850/1000\n","5/5 - 2s - loss: 5.3578e-08 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9836 - 2s/epoch - 402ms/step\n","Epoch 851/1000\n","5/5 - 2s - loss: 5.3417e-08 - accuracy: 1.0000 - val_loss: 0.2365 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 852/1000\n","5/5 - 2s - loss: 5.3273e-08 - accuracy: 1.0000 - val_loss: 0.2365 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 853/1000\n","5/5 - 2s - loss: 5.3134e-08 - accuracy: 1.0000 - val_loss: 0.2366 - val_accuracy: 0.9836 - 2s/epoch - 337ms/step\n","Epoch 854/1000\n","5/5 - 2s - loss: 5.2988e-08 - accuracy: 1.0000 - val_loss: 0.2366 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 855/1000\n","5/5 - 2s - loss: 5.2839e-08 - accuracy: 1.0000 - val_loss: 0.2367 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 856/1000\n","5/5 - 1s - loss: 5.2691e-08 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9836 - 1s/epoch - 284ms/step\n","Epoch 857/1000\n","5/5 - 2s - loss: 5.2541e-08 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 858/1000\n","5/5 - 2s - loss: 5.2414e-08 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 859/1000\n","5/5 - 2s - loss: 5.2272e-08 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 860/1000\n","5/5 - 1s - loss: 5.2127e-08 - accuracy: 1.0000 - val_loss: 0.2370 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 861/1000\n","5/5 - 2s - loss: 5.1984e-08 - accuracy: 1.0000 - val_loss: 0.2370 - val_accuracy: 0.9836 - 2s/epoch - 314ms/step\n","Epoch 862/1000\n","5/5 - 1s - loss: 5.1841e-08 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 863/1000\n","5/5 - 2s - loss: 5.1699e-08 - accuracy: 1.0000 - val_loss: 0.2372 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 864/1000\n","5/5 - 1s - loss: 5.1577e-08 - accuracy: 1.0000 - val_loss: 0.2372 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 865/1000\n","5/5 - 2s - loss: 5.1414e-08 - accuracy: 1.0000 - val_loss: 0.2373 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 866/1000\n","5/5 - 2s - loss: 5.1279e-08 - accuracy: 1.0000 - val_loss: 0.2373 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 867/1000\n","5/5 - 2s - loss: 5.1140e-08 - accuracy: 1.0000 - val_loss: 0.2374 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 868/1000\n","5/5 - 2s - loss: 5.0996e-08 - accuracy: 1.0000 - val_loss: 0.2375 - val_accuracy: 0.9836 - 2s/epoch - 304ms/step\n","Epoch 869/1000\n","5/5 - 2s - loss: 5.0859e-08 - accuracy: 1.0000 - val_loss: 0.2375 - val_accuracy: 0.9836 - 2s/epoch - 353ms/step\n","Epoch 870/1000\n","5/5 - 1s - loss: 5.0719e-08 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 871/1000\n","5/5 - 2s - loss: 5.0584e-08 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 872/1000\n","5/5 - 2s - loss: 5.0437e-08 - accuracy: 1.0000 - val_loss: 0.2377 - val_accuracy: 0.9836 - 2s/epoch - 333ms/step\n","Epoch 873/1000\n","5/5 - 2s - loss: 5.0305e-08 - accuracy: 1.0000 - val_loss: 0.2377 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 874/1000\n","5/5 - 2s - loss: 5.0175e-08 - accuracy: 1.0000 - val_loss: 0.2378 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 875/1000\n","5/5 - 2s - loss: 5.0039e-08 - accuracy: 1.0000 - val_loss: 0.2379 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 876/1000\n","5/5 - 2s - loss: 4.9905e-08 - accuracy: 1.0000 - val_loss: 0.2379 - val_accuracy: 0.9836 - 2s/epoch - 356ms/step\n","Epoch 877/1000\n","5/5 - 2s - loss: 4.9770e-08 - accuracy: 1.0000 - val_loss: 0.2380 - val_accuracy: 0.9836 - 2s/epoch - 363ms/step\n","Epoch 878/1000\n","5/5 - 2s - loss: 4.9644e-08 - accuracy: 1.0000 - val_loss: 0.2380 - val_accuracy: 0.9836 - 2s/epoch - 356ms/step\n","Epoch 879/1000\n","5/5 - 2s - loss: 4.9511e-08 - accuracy: 1.0000 - val_loss: 0.2381 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 880/1000\n","5/5 - 2s - loss: 4.9375e-08 - accuracy: 1.0000 - val_loss: 0.2381 - val_accuracy: 0.9836 - 2s/epoch - 383ms/step\n","Epoch 881/1000\n","5/5 - 2s - loss: 4.9260e-08 - accuracy: 1.0000 - val_loss: 0.2382 - val_accuracy: 0.9836 - 2s/epoch - 305ms/step\n","Epoch 882/1000\n","5/5 - 1s - loss: 4.9130e-08 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 883/1000\n","5/5 - 1s - loss: 4.8998e-08 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 884/1000\n","5/5 - 2s - loss: 4.8873e-08 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 885/1000\n","5/5 - 2s - loss: 4.8759e-08 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 886/1000\n","5/5 - 2s - loss: 4.8629e-08 - accuracy: 1.0000 - val_loss: 0.2385 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 887/1000\n","5/5 - 2s - loss: 4.8508e-08 - accuracy: 1.0000 - val_loss: 0.2385 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 888/1000\n","5/5 - 2s - loss: 4.8382e-08 - accuracy: 1.0000 - val_loss: 0.2386 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 889/1000\n","5/5 - 2s - loss: 4.8265e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9836 - 2s/epoch - 362ms/step\n","Epoch 890/1000\n","5/5 - 2s - loss: 4.8146e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9836 - 2s/epoch - 324ms/step\n","Epoch 891/1000\n","5/5 - 2s - loss: 4.8027e-08 - accuracy: 1.0000 - val_loss: 0.2388 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 892/1000\n","5/5 - 2s - loss: 4.7909e-08 - accuracy: 1.0000 - val_loss: 0.2388 - val_accuracy: 0.9836 - 2s/epoch - 327ms/step\n","Epoch 893/1000\n","5/5 - 2s - loss: 4.7788e-08 - accuracy: 1.0000 - val_loss: 0.2389 - val_accuracy: 0.9836 - 2s/epoch - 375ms/step\n","Epoch 894/1000\n","5/5 - 2s - loss: 4.7666e-08 - accuracy: 1.0000 - val_loss: 0.2389 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 895/1000\n","5/5 - 2s - loss: 4.7551e-08 - accuracy: 1.0000 - val_loss: 0.2390 - val_accuracy: 0.9836 - 2s/epoch - 359ms/step\n","Epoch 896/1000\n","5/5 - 2s - loss: 4.7435e-08 - accuracy: 1.0000 - val_loss: 0.2391 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 897/1000\n","5/5 - 2s - loss: 4.7309e-08 - accuracy: 1.0000 - val_loss: 0.2391 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 898/1000\n","5/5 - 2s - loss: 4.7189e-08 - accuracy: 1.0000 - val_loss: 0.2392 - val_accuracy: 0.9836 - 2s/epoch - 362ms/step\n","Epoch 899/1000\n","5/5 - 2s - loss: 4.7065e-08 - accuracy: 1.0000 - val_loss: 0.2392 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 900/1000\n","5/5 - 2s - loss: 4.6957e-08 - accuracy: 1.0000 - val_loss: 0.2393 - val_accuracy: 0.9836 - 2s/epoch - 342ms/step\n","Epoch 901/1000\n","5/5 - 2s - loss: 4.6836e-08 - accuracy: 1.0000 - val_loss: 0.2393 - val_accuracy: 0.9836 - 2s/epoch - 352ms/step\n","Epoch 902/1000\n","5/5 - 2s - loss: 4.6727e-08 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9836 - 2s/epoch - 364ms/step\n","Epoch 903/1000\n","5/5 - 2s - loss: 4.6598e-08 - accuracy: 1.0000 - val_loss: 0.2395 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 904/1000\n","5/5 - 2s - loss: 4.6493e-08 - accuracy: 1.0000 - val_loss: 0.2395 - val_accuracy: 0.9836 - 2s/epoch - 355ms/step\n","Epoch 905/1000\n","5/5 - 2s - loss: 4.6368e-08 - accuracy: 1.0000 - val_loss: 0.2396 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 906/1000\n","5/5 - 2s - loss: 4.6262e-08 - accuracy: 1.0000 - val_loss: 0.2396 - val_accuracy: 0.9836 - 2s/epoch - 335ms/step\n","Epoch 907/1000\n","5/5 - 2s - loss: 4.6148e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 908/1000\n","5/5 - 2s - loss: 4.6017e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 909/1000\n","5/5 - 2s - loss: 4.5916e-08 - accuracy: 1.0000 - val_loss: 0.2398 - val_accuracy: 0.9836 - 2s/epoch - 315ms/step\n","Epoch 910/1000\n","5/5 - 2s - loss: 4.5805e-08 - accuracy: 1.0000 - val_loss: 0.2399 - val_accuracy: 0.9836 - 2s/epoch - 357ms/step\n","Epoch 911/1000\n","5/5 - 2s - loss: 4.5687e-08 - accuracy: 1.0000 - val_loss: 0.2399 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 912/1000\n","5/5 - 2s - loss: 4.5583e-08 - accuracy: 1.0000 - val_loss: 0.2400 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 913/1000\n","5/5 - 2s - loss: 4.5471e-08 - accuracy: 1.0000 - val_loss: 0.2400 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 914/1000\n","5/5 - 2s - loss: 4.5359e-08 - accuracy: 1.0000 - val_loss: 0.2401 - val_accuracy: 0.9836 - 2s/epoch - 348ms/step\n","Epoch 915/1000\n","5/5 - 2s - loss: 4.5243e-08 - accuracy: 1.0000 - val_loss: 0.2401 - val_accuracy: 0.9836 - 2s/epoch - 368ms/step\n","Epoch 916/1000\n","5/5 - 2s - loss: 4.5133e-08 - accuracy: 1.0000 - val_loss: 0.2402 - val_accuracy: 0.9836 - 2s/epoch - 387ms/step\n","Epoch 917/1000\n","5/5 - 2s - loss: 4.5021e-08 - accuracy: 1.0000 - val_loss: 0.2403 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 918/1000\n","5/5 - 2s - loss: 4.4904e-08 - accuracy: 1.0000 - val_loss: 0.2403 - val_accuracy: 0.9836 - 2s/epoch - 361ms/step\n","Epoch 919/1000\n","5/5 - 2s - loss: 4.4793e-08 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 920/1000\n","5/5 - 2s - loss: 4.4684e-08 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.9836 - 2s/epoch - 319ms/step\n","Epoch 921/1000\n","5/5 - 2s - loss: 4.4581e-08 - accuracy: 1.0000 - val_loss: 0.2405 - val_accuracy: 0.9836 - 2s/epoch - 403ms/step\n","Epoch 922/1000\n","5/5 - 2s - loss: 4.4460e-08 - accuracy: 1.0000 - val_loss: 0.2405 - val_accuracy: 0.9836 - 2s/epoch - 348ms/step\n","Epoch 923/1000\n","5/5 - 2s - loss: 4.4355e-08 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 924/1000\n","5/5 - 2s - loss: 4.4243e-08 - accuracy: 1.0000 - val_loss: 0.2407 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 925/1000\n","5/5 - 2s - loss: 4.4136e-08 - accuracy: 1.0000 - val_loss: 0.2407 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 926/1000\n","5/5 - 2s - loss: 4.4020e-08 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9836 - 2s/epoch - 345ms/step\n","Epoch 927/1000\n","5/5 - 2s - loss: 4.3917e-08 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 928/1000\n","5/5 - 2s - loss: 4.3812e-08 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 929/1000\n","5/5 - 2s - loss: 4.3710e-08 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9836 - 2s/epoch - 311ms/step\n","Epoch 930/1000\n","5/5 - 2s - loss: 4.3599e-08 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9836 - 2s/epoch - 338ms/step\n","Epoch 931/1000\n","5/5 - 2s - loss: 4.3491e-08 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 932/1000\n","5/5 - 2s - loss: 4.3381e-08 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9836 - 2s/epoch - 331ms/step\n","Epoch 933/1000\n","5/5 - 2s - loss: 4.3281e-08 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9836 - 2s/epoch - 326ms/step\n","Epoch 934/1000\n","5/5 - 2s - loss: 4.3182e-08 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9836 - 2s/epoch - 367ms/step\n","Epoch 935/1000\n","5/5 - 2s - loss: 4.3077e-08 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 936/1000\n","5/5 - 2s - loss: 4.2972e-08 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9836 - 2s/epoch - 325ms/step\n","Epoch 937/1000\n","5/5 - 2s - loss: 4.2870e-08 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9836 - 2s/epoch - 313ms/step\n","Epoch 938/1000\n","5/5 - 2s - loss: 4.2772e-08 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9836 - 2s/epoch - 336ms/step\n","Epoch 939/1000\n","5/5 - 2s - loss: 4.2667e-08 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9836 - 2s/epoch - 330ms/step\n","Epoch 940/1000\n","5/5 - 2s - loss: 4.2565e-08 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9836 - 2s/epoch - 376ms/step\n","Epoch 941/1000\n","5/5 - 2s - loss: 4.2473e-08 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9836 - 2s/epoch - 307ms/step\n","Epoch 942/1000\n","5/5 - 2s - loss: 4.2366e-08 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 943/1000\n","5/5 - 1s - loss: 4.2263e-08 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 944/1000\n","5/5 - 1s - loss: 4.2170e-08 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 945/1000\n","5/5 - 2s - loss: 4.2061e-08 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 946/1000\n","5/5 - 2s - loss: 4.1974e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 947/1000\n","5/5 - 1s - loss: 4.1874e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9836 - 1s/epoch - 291ms/step\n","Epoch 948/1000\n","5/5 - 1s - loss: 4.1774e-08 - accuracy: 1.0000 - val_loss: 0.2420 - val_accuracy: 0.9836 - 1s/epoch - 288ms/step\n","Epoch 949/1000\n","5/5 - 1s - loss: 4.1677e-08 - accuracy: 1.0000 - val_loss: 0.2420 - val_accuracy: 0.9836 - 1s/epoch - 298ms/step\n","Epoch 950/1000\n","5/5 - 2s - loss: 4.1582e-08 - accuracy: 1.0000 - val_loss: 0.2421 - val_accuracy: 0.9836 - 2s/epoch - 323ms/step\n","Epoch 951/1000\n","5/5 - 2s - loss: 4.1479e-08 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 952/1000\n","5/5 - 1s - loss: 4.1379e-08 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9836 - 1s/epoch - 297ms/step\n","Epoch 953/1000\n","5/5 - 2s - loss: 4.1283e-08 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9836 - 2s/epoch - 318ms/step\n","Epoch 954/1000\n","5/5 - 2s - loss: 4.1187e-08 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 955/1000\n","5/5 - 2s - loss: 4.1090e-08 - accuracy: 1.0000 - val_loss: 0.2424 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 956/1000\n","5/5 - 2s - loss: 4.0989e-08 - accuracy: 1.0000 - val_loss: 0.2424 - val_accuracy: 0.9836 - 2s/epoch - 309ms/step\n","Epoch 957/1000\n","5/5 - 1s - loss: 4.0891e-08 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9836 - 1s/epoch - 292ms/step\n","Epoch 958/1000\n","5/5 - 2s - loss: 4.0797e-08 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9836 - 2s/epoch - 312ms/step\n","Epoch 959/1000\n","5/5 - 2s - loss: 4.0708e-08 - accuracy: 1.0000 - val_loss: 0.2426 - val_accuracy: 0.9836 - 2s/epoch - 374ms/step\n","Epoch 960/1000\n","5/5 - 2s - loss: 4.0607e-08 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9836 - 2s/epoch - 346ms/step\n","Epoch 961/1000\n","5/5 - 2s - loss: 4.0513e-08 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9836 - 2s/epoch - 306ms/step\n","Epoch 962/1000\n","5/5 - 2s - loss: 4.0408e-08 - accuracy: 1.0000 - val_loss: 0.2428 - val_accuracy: 0.9836 - 2s/epoch - 334ms/step\n","Epoch 963/1000\n","5/5 - 1s - loss: 4.0324e-08 - accuracy: 1.0000 - val_loss: 0.2428 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 964/1000\n","5/5 - 1s - loss: 4.0233e-08 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 965/1000\n","5/5 - 2s - loss: 4.0142e-08 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 966/1000\n","5/5 - 2s - loss: 4.0042e-08 - accuracy: 1.0000 - val_loss: 0.2430 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 967/1000\n","5/5 - 2s - loss: 3.9960e-08 - accuracy: 1.0000 - val_loss: 0.2430 - val_accuracy: 0.9836 - 2s/epoch - 303ms/step\n","Epoch 968/1000\n","5/5 - 2s - loss: 3.9867e-08 - accuracy: 1.0000 - val_loss: 0.2431 - val_accuracy: 0.9836 - 2s/epoch - 329ms/step\n","Epoch 969/1000\n","5/5 - 2s - loss: 3.9781e-08 - accuracy: 1.0000 - val_loss: 0.2431 - val_accuracy: 0.9836 - 2s/epoch - 302ms/step\n","Epoch 970/1000\n","5/5 - 2s - loss: 3.9691e-08 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 971/1000\n","5/5 - 1s - loss: 3.9597e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9836 - 1s/epoch - 300ms/step\n","Epoch 972/1000\n","5/5 - 1s - loss: 3.9515e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9836 - 1s/epoch - 296ms/step\n","Epoch 973/1000\n","5/5 - 1s - loss: 3.9419e-08 - accuracy: 1.0000 - val_loss: 0.2434 - val_accuracy: 0.9836 - 1s/epoch - 293ms/step\n","Epoch 974/1000\n","5/5 - 2s - loss: 3.9333e-08 - accuracy: 1.0000 - val_loss: 0.2434 - val_accuracy: 0.9836 - 2s/epoch - 321ms/step\n","Epoch 975/1000\n","5/5 - 2s - loss: 3.9242e-08 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9836 - 2s/epoch - 310ms/step\n","Epoch 976/1000\n","5/5 - 2s - loss: 3.9156e-08 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9836 - 2s/epoch - 332ms/step\n","Epoch 977/1000\n","5/5 - 2s - loss: 3.9068e-08 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9836 - 2s/epoch - 337ms/step\n","Epoch 978/1000\n","5/5 - 1s - loss: 3.8981e-08 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 979/1000\n","5/5 - 2s - loss: 3.8884e-08 - accuracy: 1.0000 - val_loss: 0.2437 - val_accuracy: 0.9836 - 2s/epoch - 301ms/step\n","Epoch 980/1000\n","5/5 - 2s - loss: 3.8798e-08 - accuracy: 1.0000 - val_loss: 0.2437 - val_accuracy: 0.9836 - 2s/epoch - 494ms/step\n","Epoch 981/1000\n","5/5 - 2s - loss: 3.8717e-08 - accuracy: 1.0000 - val_loss: 0.2438 - val_accuracy: 0.9836 - 2s/epoch - 340ms/step\n","Epoch 982/1000\n","5/5 - 2s - loss: 3.8622e-08 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9836 - 2s/epoch - 405ms/step\n","Epoch 983/1000\n","5/5 - 2s - loss: 3.8542e-08 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9836 - 2s/epoch - 328ms/step\n","Epoch 984/1000\n","5/5 - 1s - loss: 3.8445e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9836 - 1s/epoch - 277ms/step\n","Epoch 985/1000\n","5/5 - 1s - loss: 3.8364e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9836 - 1s/epoch - 253ms/step\n","Epoch 986/1000\n","5/5 - 1s - loss: 3.8280e-08 - accuracy: 1.0000 - val_loss: 0.2441 - val_accuracy: 0.9836 - 1s/epoch - 267ms/step\n","Epoch 987/1000\n","5/5 - 1s - loss: 3.8194e-08 - accuracy: 1.0000 - val_loss: 0.2441 - val_accuracy: 0.9836 - 1s/epoch - 294ms/step\n","Epoch 988/1000\n","5/5 - 2s - loss: 3.8106e-08 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 0.9836 - 2s/epoch - 339ms/step\n","Epoch 989/1000\n","5/5 - 1s - loss: 3.8028e-08 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 0.9836 - 1s/epoch - 259ms/step\n","Epoch 990/1000\n","5/5 - 1s - loss: 3.7940e-08 - accuracy: 1.0000 - val_loss: 0.2443 - val_accuracy: 0.9836 - 1s/epoch - 263ms/step\n","Epoch 991/1000\n","5/5 - 1s - loss: 3.7855e-08 - accuracy: 1.0000 - val_loss: 0.2443 - val_accuracy: 0.9836 - 1s/epoch - 264ms/step\n","Epoch 992/1000\n","5/5 - 1s - loss: 3.7767e-08 - accuracy: 1.0000 - val_loss: 0.2444 - val_accuracy: 0.9836 - 1s/epoch - 245ms/step\n","Epoch 993/1000\n","5/5 - 1s - loss: 3.7681e-08 - accuracy: 1.0000 - val_loss: 0.2444 - val_accuracy: 0.9836 - 1s/epoch - 235ms/step\n","Epoch 994/1000\n","5/5 - 1s - loss: 3.7602e-08 - accuracy: 1.0000 - val_loss: 0.2445 - val_accuracy: 0.9836 - 1s/epoch - 270ms/step\n","Epoch 995/1000\n","5/5 - 1s - loss: 3.7520e-08 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9836 - 1s/epoch - 283ms/step\n","Epoch 996/1000\n","5/5 - 1s - loss: 3.7432e-08 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9836 - 1s/epoch - 250ms/step\n","Epoch 997/1000\n","5/5 - 1s - loss: 3.7355e-08 - accuracy: 1.0000 - val_loss: 0.2447 - val_accuracy: 0.9836 - 1s/epoch - 265ms/step\n","Epoch 998/1000\n","5/5 - 1s - loss: 3.7274e-08 - accuracy: 1.0000 - val_loss: 0.2447 - val_accuracy: 0.9836 - 1s/epoch - 287ms/step\n","Epoch 999/1000\n","5/5 - 1s - loss: 3.7189e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9836 - 1s/epoch - 269ms/step\n","Epoch 1000/1000\n","5/5 - 1s - loss: 3.7115e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9836 - 1s/epoch - 279ms/step\n"]}],"source":["biLSTM_history = biLSTM_model.fit(training_padded,\n"," training_labels,\n"," epochs=num_epochs,\n"," validation_data = (testing_padded, testing_labels),\n"," verbose=2, callbacks=[early_stop])"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Train LSTM**"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"TuEtPbIDS_1E"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/1000\n","5/5 - 11s - loss: 0.6735 - accuracy: 0.7417 - val_loss: 0.6129 - val_accuracy: 0.9508 - 11s/epoch - 2s/step\n","Epoch 2/1000\n","5/5 - 1s - loss: 0.5456 - accuracy: 0.8808 - val_loss: 0.2103 - val_accuracy: 0.9508 - 721ms/epoch - 144ms/step\n","Epoch 3/1000\n","5/5 - 1s - loss: 0.3863 - accuracy: 0.8808 - val_loss: 0.2295 - val_accuracy: 0.9508 - 655ms/epoch - 131ms/step\n","Epoch 4/1000\n","5/5 - 1s - loss: 0.3763 - accuracy: 0.8808 - val_loss: 0.2447 - val_accuracy: 0.9508 - 647ms/epoch - 129ms/step\n","Epoch 5/1000\n","5/5 - 1s - loss: 0.3674 - accuracy: 0.8808 - val_loss: 0.2205 - val_accuracy: 0.9508 - 636ms/epoch - 127ms/step\n","Epoch 6/1000\n","5/5 - 1s - loss: 0.3677 - accuracy: 0.8808 - val_loss: 0.2125 - val_accuracy: 0.9508 - 632ms/epoch - 126ms/step\n","Epoch 7/1000\n","5/5 - 1s - loss: 0.3648 - accuracy: 0.8808 - val_loss: 0.2218 - val_accuracy: 0.9508 - 644ms/epoch - 129ms/step\n","Epoch 8/1000\n","5/5 - 1s - loss: 0.3638 - accuracy: 0.8808 - val_loss: 0.2303 - val_accuracy: 0.9508 - 696ms/epoch - 139ms/step\n","Epoch 9/1000\n","5/5 - 1s - loss: 0.3628 - accuracy: 0.8808 - val_loss: 0.2321 - val_accuracy: 0.9508 - 847ms/epoch - 169ms/step\n","Epoch 10/1000\n","5/5 - 1s - loss: 0.3620 - accuracy: 0.8808 - val_loss: 0.2197 - val_accuracy: 0.9508 - 810ms/epoch - 162ms/step\n","Epoch 11/1000\n","5/5 - 1s - loss: 0.3616 - accuracy: 0.8808 - val_loss: 0.2137 - val_accuracy: 0.9508 - 672ms/epoch - 134ms/step\n","Epoch 12/1000\n","5/5 - 1s - loss: 0.3594 - accuracy: 0.8808 - val_loss: 0.2222 - val_accuracy: 0.9508 - 660ms/epoch - 132ms/step\n","Epoch 13/1000\n","5/5 - 1s - loss: 0.3623 - accuracy: 0.8808 - val_loss: 0.2331 - val_accuracy: 0.9508 - 649ms/epoch - 130ms/step\n","Epoch 14/1000\n","5/5 - 1s - loss: 0.3530 - accuracy: 0.8808 - val_loss: 0.2052 - val_accuracy: 0.9508 - 672ms/epoch - 134ms/step\n","Epoch 15/1000\n","5/5 - 1s - loss: 0.3478 - accuracy: 0.8808 - val_loss: 0.2023 - val_accuracy: 0.9508 - 705ms/epoch - 141ms/step\n","Epoch 16/1000\n","5/5 - 1s - loss: 0.3139 - accuracy: 0.8874 - val_loss: 0.1474 - val_accuracy: 0.9672 - 685ms/epoch - 137ms/step\n","Epoch 17/1000\n","5/5 - 1s - loss: 0.1260 - accuracy: 0.9801 - val_loss: 0.0808 - val_accuracy: 0.9836 - 658ms/epoch - 132ms/step\n","Epoch 18/1000\n","5/5 - 1s - loss: 0.0442 - accuracy: 0.9934 - val_loss: 0.0720 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 19/1000\n","5/5 - 1s - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.1145 - val_accuracy: 0.9672 - 660ms/epoch - 132ms/step\n","Epoch 20/1000\n","5/5 - 1s - loss: 0.0267 - accuracy: 0.9934 - val_loss: 0.0482 - val_accuracy: 0.9836 - 676ms/epoch - 135ms/step\n","Epoch 21/1000\n","5/5 - 1s - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0484 - val_accuracy: 0.9836 - 678ms/epoch - 136ms/step\n","Epoch 22/1000\n","5/5 - 1s - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.0538 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 23/1000\n","5/5 - 1s - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0615 - val_accuracy: 0.9836 - 731ms/epoch - 146ms/step\n","Epoch 24/1000\n","5/5 - 1s - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0689 - val_accuracy: 0.9836 - 668ms/epoch - 134ms/step\n","Epoch 25/1000\n","5/5 - 1s - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 26/1000\n","5/5 - 1s - loss: 8.1851e-04 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9836 - 645ms/epoch - 129ms/step\n","Epoch 27/1000\n","5/5 - 1s - loss: 6.3812e-04 - accuracy: 1.0000 - val_loss: 0.0847 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 28/1000\n","5/5 - 1s - loss: 4.9994e-04 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 29/1000\n","5/5 - 1s - loss: 4.2436e-04 - accuracy: 1.0000 - val_loss: 0.0906 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 30/1000\n","5/5 - 1s - loss: 3.7568e-04 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 31/1000\n","5/5 - 1s - loss: 3.3846e-04 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 32/1000\n","5/5 - 1s - loss: 3.0814e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 33/1000\n","5/5 - 1s - loss: 2.8732e-04 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 34/1000\n","5/5 - 1s - loss: 2.6989e-04 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 35/1000\n","5/5 - 1s - loss: 2.5211e-04 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 36/1000\n","5/5 - 1s - loss: 2.3858e-04 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9836 - 637ms/epoch - 127ms/step\n","Epoch 37/1000\n","5/5 - 1s - loss: 2.2566e-04 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9836 - 597ms/epoch - 119ms/step\n","Epoch 38/1000\n","5/5 - 1s - loss: 2.1460e-04 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 39/1000\n","5/5 - 1s - loss: 2.0560e-04 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9836 - 600ms/epoch - 120ms/step\n","Epoch 40/1000\n","5/5 - 1s - loss: 1.9605e-04 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 41/1000\n","5/5 - 1s - loss: 1.8708e-04 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 42/1000\n","5/5 - 1s - loss: 1.7976e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 43/1000\n","5/5 - 1s - loss: 1.7223e-04 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 44/1000\n","5/5 - 1s - loss: 1.6550e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 45/1000\n","5/5 - 1s - loss: 1.5890e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 46/1000\n","5/5 - 1s - loss: 1.5297e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 47/1000\n","5/5 - 1s - loss: 1.4769e-04 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 48/1000\n","5/5 - 1s - loss: 1.4228e-04 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 49/1000\n","5/5 - 1s - loss: 1.3750e-04 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 50/1000\n","5/5 - 1s - loss: 1.3274e-04 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 51/1000\n","5/5 - 1s - loss: 1.2841e-04 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9836 - 632ms/epoch - 126ms/step\n","Epoch 52/1000\n","5/5 - 1s - loss: 1.2422e-04 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 53/1000\n","5/5 - 1s - loss: 1.2028e-04 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 54/1000\n","5/5 - 1s - loss: 1.1669e-04 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 55/1000\n","5/5 - 1s - loss: 1.1311e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9836 - 683ms/epoch - 137ms/step\n","Epoch 56/1000\n","5/5 - 1s - loss: 1.0957e-04 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 57/1000\n","5/5 - 1s - loss: 1.0632e-04 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 58/1000\n","5/5 - 1s - loss: 1.0330e-04 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 59/1000\n","5/5 - 1s - loss: 1.0023e-04 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9836 - 705ms/epoch - 141ms/step\n","Epoch 60/1000\n","5/5 - 1s - loss: 9.7460e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 61/1000\n","5/5 - 1s - loss: 9.4777e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9836 - 617ms/epoch - 123ms/step\n","Epoch 62/1000\n","5/5 - 1s - loss: 9.2112e-05 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 63/1000\n","5/5 - 1s - loss: 8.9588e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 64/1000\n","5/5 - 1s - loss: 8.7145e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 65/1000\n","5/5 - 1s - loss: 8.4869e-05 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 66/1000\n","5/5 - 1s - loss: 8.2840e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 67/1000\n","5/5 - 1s - loss: 8.0478e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 68/1000\n","5/5 - 1s - loss: 7.8517e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9836 - 639ms/epoch - 128ms/step\n","Epoch 69/1000\n","5/5 - 1s - loss: 7.6625e-05 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 70/1000\n","5/5 - 1s - loss: 7.4644e-05 - accuracy: 1.0000 - val_loss: 0.1203 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 71/1000\n","5/5 - 1s - loss: 7.2902e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9836 - 699ms/epoch - 140ms/step\n","Epoch 72/1000\n","5/5 - 1s - loss: 7.1212e-05 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9836 - 723ms/epoch - 145ms/step\n","Epoch 73/1000\n","5/5 - 1s - loss: 6.9471e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 74/1000\n","5/5 - 1s - loss: 6.7968e-05 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9836 - 729ms/epoch - 146ms/step\n","Epoch 75/1000\n","5/5 - 1s - loss: 6.6263e-05 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 76/1000\n","5/5 - 1s - loss: 6.4924e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 77/1000\n","5/5 - 1s - loss: 6.3386e-05 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9836 - 630ms/epoch - 126ms/step\n","Epoch 78/1000\n","5/5 - 1s - loss: 6.2091e-05 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 79/1000\n","5/5 - 1s - loss: 6.0706e-05 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 80/1000\n","5/5 - 1s - loss: 5.9413e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9836 - 633ms/epoch - 127ms/step\n","Epoch 81/1000\n","5/5 - 1s - loss: 5.8169e-05 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9836 - 657ms/epoch - 131ms/step\n","Epoch 82/1000\n","5/5 - 1s - loss: 5.6960e-05 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 83/1000\n","5/5 - 1s - loss: 5.5787e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 84/1000\n","5/5 - 1s - loss: 5.4679e-05 - accuracy: 1.0000 - val_loss: 0.1252 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 85/1000\n","5/5 - 1s - loss: 5.3554e-05 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9836 - 619ms/epoch - 124ms/step\n","Epoch 86/1000\n","5/5 - 1s - loss: 5.2492e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 87/1000\n","5/5 - 1s - loss: 5.1444e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 88/1000\n","5/5 - 1s - loss: 5.0449e-05 - accuracy: 1.0000 - val_loss: 0.1264 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 89/1000\n","5/5 - 1s - loss: 4.9496e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 90/1000\n","5/5 - 1s - loss: 4.8552e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 91/1000\n","5/5 - 1s - loss: 4.7655e-05 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 92/1000\n","5/5 - 1s - loss: 4.6710e-05 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 93/1000\n","5/5 - 1s - loss: 4.5873e-05 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 94/1000\n","5/5 - 1s - loss: 4.5027e-05 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 95/1000\n","5/5 - 1s - loss: 4.4190e-05 - accuracy: 1.0000 - val_loss: 0.1285 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 96/1000\n","5/5 - 1s - loss: 4.3385e-05 - accuracy: 1.0000 - val_loss: 0.1288 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 97/1000\n","5/5 - 1s - loss: 4.2636e-05 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 98/1000\n","5/5 - 1s - loss: 4.1882e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 99/1000\n","5/5 - 1s - loss: 4.1203e-05 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 100/1000\n","5/5 - 1s - loss: 4.0421e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 101/1000\n","5/5 - 1s - loss: 3.9729e-05 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 102/1000\n","5/5 - 1s - loss: 3.9080e-05 - accuracy: 1.0000 - val_loss: 0.1304 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 103/1000\n","5/5 - 1s - loss: 3.8402e-05 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9836 - 597ms/epoch - 119ms/step\n","Epoch 104/1000\n","5/5 - 1s - loss: 3.7761e-05 - accuracy: 1.0000 - val_loss: 0.1310 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 105/1000\n","5/5 - 1s - loss: 3.7171e-05 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 106/1000\n","5/5 - 1s - loss: 3.6515e-05 - accuracy: 1.0000 - val_loss: 0.1315 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 107/1000\n","5/5 - 1s - loss: 3.5934e-05 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 108/1000\n","5/5 - 1s - loss: 3.5380e-05 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9836 - 604ms/epoch - 121ms/step\n","Epoch 109/1000\n","5/5 - 1s - loss: 3.4771e-05 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 110/1000\n","5/5 - 1s - loss: 3.4272e-05 - accuracy: 1.0000 - val_loss: 0.1325 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 111/1000\n","5/5 - 1s - loss: 3.3720e-05 - accuracy: 1.0000 - val_loss: 0.1328 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 112/1000\n","5/5 - 1s - loss: 3.3193e-05 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 113/1000\n","5/5 - 1s - loss: 3.2666e-05 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 114/1000\n","5/5 - 1s - loss: 3.2203e-05 - accuracy: 1.0000 - val_loss: 0.1335 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 115/1000\n","5/5 - 1s - loss: 3.1676e-05 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 116/1000\n","5/5 - 1s - loss: 3.1221e-05 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9836 - 695ms/epoch - 139ms/step\n","Epoch 117/1000\n","5/5 - 1s - loss: 3.0771e-05 - accuracy: 1.0000 - val_loss: 0.1342 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 118/1000\n","5/5 - 1s - loss: 3.0313e-05 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 119/1000\n","5/5 - 1s - loss: 2.9866e-05 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 120/1000\n","5/5 - 1s - loss: 2.9442e-05 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 121/1000\n","5/5 - 1s - loss: 2.9011e-05 - accuracy: 1.0000 - val_loss: 0.1350 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 122/1000\n","5/5 - 1s - loss: 2.8592e-05 - accuracy: 1.0000 - val_loss: 0.1353 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 123/1000\n","5/5 - 1s - loss: 2.8198e-05 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9836 - 837ms/epoch - 167ms/step\n","Epoch 124/1000\n","5/5 - 1s - loss: 2.7803e-05 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 125/1000\n","5/5 - 1s - loss: 2.7404e-05 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 126/1000\n","5/5 - 1s - loss: 2.7020e-05 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9836 - 774ms/epoch - 155ms/step\n","Epoch 127/1000\n","5/5 - 1s - loss: 2.6662e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9836 - 662ms/epoch - 132ms/step\n","Epoch 128/1000\n","5/5 - 1s - loss: 2.6305e-05 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 129/1000\n","5/5 - 1s - loss: 2.5941e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 130/1000\n","5/5 - 1s - loss: 2.5576e-05 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 131/1000\n","5/5 - 1s - loss: 2.5231e-05 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 132/1000\n","5/5 - 1s - loss: 2.4912e-05 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 133/1000\n","5/5 - 1s - loss: 2.4587e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 134/1000\n","5/5 - 1s - loss: 2.4254e-05 - accuracy: 1.0000 - val_loss: 0.1378 - val_accuracy: 0.9836 - 758ms/epoch - 152ms/step\n","Epoch 135/1000\n","5/5 - 1s - loss: 2.3934e-05 - accuracy: 1.0000 - val_loss: 0.1380 - val_accuracy: 0.9836 - 737ms/epoch - 147ms/step\n","Epoch 136/1000\n","5/5 - 1s - loss: 2.3615e-05 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9836 - 757ms/epoch - 151ms/step\n","Epoch 137/1000\n","5/5 - 1s - loss: 2.3325e-05 - accuracy: 1.0000 - val_loss: 0.1384 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 138/1000\n","5/5 - 1s - loss: 2.3032e-05 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 139/1000\n","5/5 - 1s - loss: 2.2725e-05 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 140/1000\n","5/5 - 1s - loss: 2.2443e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9836 - 869ms/epoch - 174ms/step\n","Epoch 141/1000\n","5/5 - 1s - loss: 2.2169e-05 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9836 - 884ms/epoch - 177ms/step\n","Epoch 142/1000\n","5/5 - 1s - loss: 2.1883e-05 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9836 - 809ms/epoch - 162ms/step\n","Epoch 143/1000\n","5/5 - 1s - loss: 2.1616e-05 - accuracy: 1.0000 - val_loss: 0.1396 - val_accuracy: 0.9836 - 710ms/epoch - 142ms/step\n","Epoch 144/1000\n","5/5 - 1s - loss: 2.1339e-05 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9836 - 783ms/epoch - 157ms/step\n","Epoch 145/1000\n","5/5 - 1s - loss: 2.1082e-05 - accuracy: 1.0000 - val_loss: 0.1400 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 146/1000\n","5/5 - 1s - loss: 2.0831e-05 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9836 - 783ms/epoch - 157ms/step\n","Epoch 147/1000\n","5/5 - 1s - loss: 2.0583e-05 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9836 - 680ms/epoch - 136ms/step\n","Epoch 148/1000\n","5/5 - 1s - loss: 2.0324e-05 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 149/1000\n","5/5 - 1s - loss: 2.0081e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9836 - 827ms/epoch - 165ms/step\n","Epoch 150/1000\n","5/5 - 1s - loss: 1.9852e-05 - accuracy: 1.0000 - val_loss: 0.1409 - val_accuracy: 0.9836 - 719ms/epoch - 144ms/step\n","Epoch 151/1000\n","5/5 - 1s - loss: 1.9618e-05 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9836 - 727ms/epoch - 145ms/step\n","Epoch 152/1000\n","5/5 - 1s - loss: 1.9380e-05 - accuracy: 1.0000 - val_loss: 0.1413 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 153/1000\n","5/5 - 1s - loss: 1.9165e-05 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 154/1000\n","5/5 - 1s - loss: 1.8941e-05 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 155/1000\n","5/5 - 1s - loss: 1.8718e-05 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9836 - 601ms/epoch - 120ms/step\n","Epoch 156/1000\n","5/5 - 1s - loss: 1.8510e-05 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 157/1000\n","5/5 - 1s - loss: 1.8293e-05 - accuracy: 1.0000 - val_loss: 0.1421 - val_accuracy: 0.9836 - 597ms/epoch - 119ms/step\n","Epoch 158/1000\n","5/5 - 1s - loss: 1.8093e-05 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 159/1000\n","5/5 - 1s - loss: 1.7890e-05 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 160/1000\n","5/5 - 1s - loss: 1.7698e-05 - accuracy: 1.0000 - val_loss: 0.1427 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 161/1000\n","5/5 - 1s - loss: 1.7488e-05 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 162/1000\n","5/5 - 1s - loss: 1.7302e-05 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 163/1000\n","5/5 - 1s - loss: 1.7110e-05 - accuracy: 1.0000 - val_loss: 0.1431 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 164/1000\n","5/5 - 1s - loss: 1.6926e-05 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9836 - 810ms/epoch - 162ms/step\n","Epoch 165/1000\n","5/5 - 1s - loss: 1.6743e-05 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9836 - 720ms/epoch - 144ms/step\n","Epoch 166/1000\n","5/5 - 1s - loss: 1.6560e-05 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 167/1000\n","5/5 - 1s - loss: 1.6377e-05 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9836 - 664ms/epoch - 133ms/step\n","Epoch 168/1000\n","5/5 - 1s - loss: 1.6213e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9836 - 674ms/epoch - 135ms/step\n","Epoch 169/1000\n","5/5 - 1s - loss: 1.6037e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 170/1000\n","5/5 - 1s - loss: 1.5860e-05 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 171/1000\n","5/5 - 1s - loss: 1.5700e-05 - accuracy: 1.0000 - val_loss: 0.1444 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 172/1000\n","5/5 - 1s - loss: 1.5528e-05 - accuracy: 1.0000 - val_loss: 0.1446 - val_accuracy: 0.9836 - 629ms/epoch - 126ms/step\n","Epoch 173/1000\n","5/5 - 1s - loss: 1.5368e-05 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9836 - 629ms/epoch - 126ms/step\n","Epoch 174/1000\n","5/5 - 1s - loss: 1.5214e-05 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 175/1000\n","5/5 - 1s - loss: 1.5052e-05 - accuracy: 1.0000 - val_loss: 0.1451 - val_accuracy: 0.9836 - 677ms/epoch - 135ms/step\n","Epoch 176/1000\n","5/5 - 1s - loss: 1.4899e-05 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 177/1000\n","5/5 - 1s - loss: 1.4743e-05 - accuracy: 1.0000 - val_loss: 0.1454 - val_accuracy: 0.9836 - 674ms/epoch - 135ms/step\n","Epoch 178/1000\n","5/5 - 1s - loss: 1.4598e-05 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 179/1000\n","5/5 - 1s - loss: 1.4443e-05 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9836 - 659ms/epoch - 132ms/step\n","Epoch 180/1000\n","5/5 - 1s - loss: 1.4305e-05 - accuracy: 1.0000 - val_loss: 0.1458 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 181/1000\n","5/5 - 1s - loss: 1.4155e-05 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 0.9836 - 658ms/epoch - 132ms/step\n","Epoch 182/1000\n","5/5 - 1s - loss: 1.4014e-05 - accuracy: 1.0000 - val_loss: 0.1461 - val_accuracy: 0.9836 - 652ms/epoch - 130ms/step\n","Epoch 183/1000\n","5/5 - 1s - loss: 1.3880e-05 - accuracy: 1.0000 - val_loss: 0.1463 - val_accuracy: 0.9836 - 657ms/epoch - 131ms/step\n","Epoch 184/1000\n","5/5 - 1s - loss: 1.3738e-05 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9836 - 617ms/epoch - 123ms/step\n","Epoch 185/1000\n","5/5 - 1s - loss: 1.3600e-05 - accuracy: 1.0000 - val_loss: 0.1466 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 186/1000\n","5/5 - 1s - loss: 1.3465e-05 - accuracy: 1.0000 - val_loss: 0.1468 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 187/1000\n","5/5 - 1s - loss: 1.3330e-05 - accuracy: 1.0000 - val_loss: 0.1469 - val_accuracy: 0.9836 - 631ms/epoch - 126ms/step\n","Epoch 188/1000\n","5/5 - 1s - loss: 1.3203e-05 - accuracy: 1.0000 - val_loss: 0.1471 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 189/1000\n","5/5 - 1s - loss: 1.3075e-05 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9836 - 634ms/epoch - 127ms/step\n","Epoch 190/1000\n","5/5 - 1s - loss: 1.2947e-05 - accuracy: 1.0000 - val_loss: 0.1474 - val_accuracy: 0.9836 - 630ms/epoch - 126ms/step\n","Epoch 191/1000\n","5/5 - 1s - loss: 1.2820e-05 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 192/1000\n","5/5 - 1s - loss: 1.2698e-05 - accuracy: 1.0000 - val_loss: 0.1476 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 193/1000\n","5/5 - 1s - loss: 1.2580e-05 - accuracy: 1.0000 - val_loss: 0.1478 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 194/1000\n","5/5 - 1s - loss: 1.2452e-05 - accuracy: 1.0000 - val_loss: 0.1479 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 195/1000\n","5/5 - 1s - loss: 1.2342e-05 - accuracy: 1.0000 - val_loss: 0.1481 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 196/1000\n","5/5 - 1s - loss: 1.2215e-05 - accuracy: 1.0000 - val_loss: 0.1482 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 197/1000\n","5/5 - 1s - loss: 1.2100e-05 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 198/1000\n","5/5 - 1s - loss: 1.1996e-05 - accuracy: 1.0000 - val_loss: 0.1485 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 199/1000\n","5/5 - 1s - loss: 1.1880e-05 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 200/1000\n","5/5 - 1s - loss: 1.1769e-05 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 201/1000\n","5/5 - 1s - loss: 1.1660e-05 - accuracy: 1.0000 - val_loss: 0.1490 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 202/1000\n","5/5 - 1s - loss: 1.1546e-05 - accuracy: 1.0000 - val_loss: 0.1491 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 203/1000\n","5/5 - 1s - loss: 1.1448e-05 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 204/1000\n","5/5 - 1s - loss: 1.1337e-05 - accuracy: 1.0000 - val_loss: 0.1494 - val_accuracy: 0.9836 - 617ms/epoch - 123ms/step\n","Epoch 205/1000\n","5/5 - 1s - loss: 1.1243e-05 - accuracy: 1.0000 - val_loss: 0.1495 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 206/1000\n","5/5 - 1s - loss: 1.1132e-05 - accuracy: 1.0000 - val_loss: 0.1496 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 207/1000\n","5/5 - 1s - loss: 1.1037e-05 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 208/1000\n","5/5 - 1s - loss: 1.0937e-05 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9836 - 617ms/epoch - 123ms/step\n","Epoch 209/1000\n","5/5 - 1s - loss: 1.0836e-05 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 210/1000\n","5/5 - 1s - loss: 1.0743e-05 - accuracy: 1.0000 - val_loss: 0.1502 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 211/1000\n","5/5 - 1s - loss: 1.0648e-05 - accuracy: 1.0000 - val_loss: 0.1503 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 212/1000\n","5/5 - 1s - loss: 1.0553e-05 - accuracy: 1.0000 - val_loss: 0.1504 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 213/1000\n","5/5 - 1s - loss: 1.0464e-05 - accuracy: 1.0000 - val_loss: 0.1505 - val_accuracy: 0.9836 - 619ms/epoch - 124ms/step\n","Epoch 214/1000\n","5/5 - 1s - loss: 1.0365e-05 - accuracy: 1.0000 - val_loss: 0.1507 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 215/1000\n","5/5 - 1s - loss: 1.0274e-05 - accuracy: 1.0000 - val_loss: 0.1508 - val_accuracy: 0.9836 - 659ms/epoch - 132ms/step\n","Epoch 216/1000\n","5/5 - 1s - loss: 1.0189e-05 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9836 - 775ms/epoch - 155ms/step\n","Epoch 217/1000\n","5/5 - 1s - loss: 1.0097e-05 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9836 - 721ms/epoch - 144ms/step\n","Epoch 218/1000\n","5/5 - 1s - loss: 1.0009e-05 - accuracy: 1.0000 - val_loss: 0.1512 - val_accuracy: 0.9836 - 698ms/epoch - 140ms/step\n","Epoch 219/1000\n","5/5 - 1s - loss: 9.9207e-06 - accuracy: 1.0000 - val_loss: 0.1513 - val_accuracy: 0.9836 - 932ms/epoch - 186ms/step\n","Epoch 220/1000\n","5/5 - 1s - loss: 9.8362e-06 - accuracy: 1.0000 - val_loss: 0.1515 - val_accuracy: 0.9836 - 803ms/epoch - 161ms/step\n","Epoch 221/1000\n","5/5 - 1s - loss: 9.7514e-06 - accuracy: 1.0000 - val_loss: 0.1516 - val_accuracy: 0.9836 - 732ms/epoch - 146ms/step\n","Epoch 222/1000\n","5/5 - 1s - loss: 9.6702e-06 - accuracy: 1.0000 - val_loss: 0.1517 - val_accuracy: 0.9836 - 825ms/epoch - 165ms/step\n","Epoch 223/1000\n","5/5 - 1s - loss: 9.5856e-06 - accuracy: 1.0000 - val_loss: 0.1518 - val_accuracy: 0.9836 - 712ms/epoch - 142ms/step\n","Epoch 224/1000\n","5/5 - 1s - loss: 9.5035e-06 - accuracy: 1.0000 - val_loss: 0.1520 - val_accuracy: 0.9836 - 735ms/epoch - 147ms/step\n","Epoch 225/1000\n","5/5 - 1s - loss: 9.4234e-06 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 226/1000\n","5/5 - 1s - loss: 9.3455e-06 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9836 - 601ms/epoch - 120ms/step\n","Epoch 227/1000\n","5/5 - 1s - loss: 9.2646e-06 - accuracy: 1.0000 - val_loss: 0.1523 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 228/1000\n","5/5 - 1s - loss: 9.1899e-06 - accuracy: 1.0000 - val_loss: 0.1525 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 229/1000\n","5/5 - 1s - loss: 9.1110e-06 - accuracy: 1.0000 - val_loss: 0.1526 - val_accuracy: 0.9836 - 616ms/epoch - 123ms/step\n","Epoch 230/1000\n","5/5 - 1s - loss: 9.0336e-06 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 231/1000\n","5/5 - 1s - loss: 8.9595e-06 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 232/1000\n","5/5 - 1s - loss: 8.8869e-06 - accuracy: 1.0000 - val_loss: 0.1529 - val_accuracy: 0.9836 - 606ms/epoch - 121ms/step\n","Epoch 233/1000\n","5/5 - 1s - loss: 8.8125e-06 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 234/1000\n","5/5 - 1s - loss: 8.7436e-06 - accuracy: 1.0000 - val_loss: 0.1532 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 235/1000\n","5/5 - 1s - loss: 8.6716e-06 - accuracy: 1.0000 - val_loss: 0.1533 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 236/1000\n","5/5 - 1s - loss: 8.5990e-06 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 237/1000\n","5/5 - 1s - loss: 8.5309e-06 - accuracy: 1.0000 - val_loss: 0.1535 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 238/1000\n","5/5 - 1s - loss: 8.4614e-06 - accuracy: 1.0000 - val_loss: 0.1536 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 239/1000\n","5/5 - 1s - loss: 8.3930e-06 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 240/1000\n","5/5 - 1s - loss: 8.3236e-06 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9836 - 666ms/epoch - 133ms/step\n","Epoch 241/1000\n","5/5 - 1s - loss: 8.2571e-06 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 242/1000\n","5/5 - 1s - loss: 8.1988e-06 - accuracy: 1.0000 - val_loss: 0.1541 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 243/1000\n","5/5 - 1s - loss: 8.1240e-06 - accuracy: 1.0000 - val_loss: 0.1542 - val_accuracy: 0.9836 - 711ms/epoch - 142ms/step\n","Epoch 244/1000\n","5/5 - 1s - loss: 8.0620e-06 - accuracy: 1.0000 - val_loss: 0.1543 - val_accuracy: 0.9836 - 606ms/epoch - 121ms/step\n","Epoch 245/1000\n","5/5 - 1s - loss: 7.9974e-06 - accuracy: 1.0000 - val_loss: 0.1545 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 246/1000\n","5/5 - 1s - loss: 7.9325e-06 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 247/1000\n","5/5 - 1s - loss: 7.8723e-06 - accuracy: 1.0000 - val_loss: 0.1547 - val_accuracy: 0.9836 - 609ms/epoch - 122ms/step\n","Epoch 248/1000\n","5/5 - 1s - loss: 7.8083e-06 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 249/1000\n","5/5 - 1s - loss: 7.7464e-06 - accuracy: 1.0000 - val_loss: 0.1549 - val_accuracy: 0.9836 - 632ms/epoch - 126ms/step\n","Epoch 250/1000\n","5/5 - 1s - loss: 7.6856e-06 - accuracy: 1.0000 - val_loss: 0.1550 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 251/1000\n","5/5 - 1s - loss: 7.6283e-06 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 252/1000\n","5/5 - 1s - loss: 7.5676e-06 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 253/1000\n","5/5 - 1s - loss: 7.5088e-06 - accuracy: 1.0000 - val_loss: 0.1554 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 254/1000\n","5/5 - 1s - loss: 7.4485e-06 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 255/1000\n","5/5 - 1s - loss: 7.3923e-06 - accuracy: 1.0000 - val_loss: 0.1556 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 256/1000\n","5/5 - 1s - loss: 7.3372e-06 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 257/1000\n","5/5 - 1s - loss: 7.2781e-06 - accuracy: 1.0000 - val_loss: 0.1558 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 258/1000\n","5/5 - 1s - loss: 7.2225e-06 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 259/1000\n","5/5 - 1s - loss: 7.1684e-06 - accuracy: 1.0000 - val_loss: 0.1560 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 260/1000\n","5/5 - 1s - loss: 7.1107e-06 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9836 - 607ms/epoch - 121ms/step\n","Epoch 261/1000\n","5/5 - 1s - loss: 7.0588e-06 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9836 - 572ms/epoch - 114ms/step\n","Epoch 262/1000\n","5/5 - 1s - loss: 7.0026e-06 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 263/1000\n","5/5 - 1s - loss: 6.9503e-06 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 264/1000\n","5/5 - 1s - loss: 6.8962e-06 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 265/1000\n","5/5 - 1s - loss: 6.8420e-06 - accuracy: 1.0000 - val_loss: 0.1567 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 266/1000\n","5/5 - 1s - loss: 6.7929e-06 - accuracy: 1.0000 - val_loss: 0.1568 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 267/1000\n","5/5 - 1s - loss: 6.7412e-06 - accuracy: 1.0000 - val_loss: 0.1569 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 268/1000\n","5/5 - 1s - loss: 6.6869e-06 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 269/1000\n","5/5 - 1s - loss: 6.6383e-06 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 270/1000\n","5/5 - 1s - loss: 6.5883e-06 - accuracy: 1.0000 - val_loss: 0.1572 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 271/1000\n","5/5 - 1s - loss: 6.5383e-06 - accuracy: 1.0000 - val_loss: 0.1573 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 272/1000\n","5/5 - 1s - loss: 6.4912e-06 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 273/1000\n","5/5 - 1s - loss: 6.4421e-06 - accuracy: 1.0000 - val_loss: 0.1575 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 274/1000\n","5/5 - 1s - loss: 6.3945e-06 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 275/1000\n","5/5 - 1s - loss: 6.3466e-06 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 276/1000\n","5/5 - 1s - loss: 6.2989e-06 - accuracy: 1.0000 - val_loss: 0.1578 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 277/1000\n","5/5 - 1s - loss: 6.2538e-06 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 278/1000\n","5/5 - 1s - loss: 6.2069e-06 - accuracy: 1.0000 - val_loss: 0.1580 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 279/1000\n","5/5 - 1s - loss: 6.1632e-06 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 280/1000\n","5/5 - 1s - loss: 6.1150e-06 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 281/1000\n","5/5 - 1s - loss: 6.0731e-06 - accuracy: 1.0000 - val_loss: 0.1584 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 282/1000\n","5/5 - 1s - loss: 6.0269e-06 - accuracy: 1.0000 - val_loss: 0.1585 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 283/1000\n","5/5 - 1s - loss: 5.9820e-06 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 284/1000\n","5/5 - 1s - loss: 5.9379e-06 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 285/1000\n","5/5 - 1s - loss: 5.8967e-06 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 286/1000\n","5/5 - 1s - loss: 5.8528e-06 - accuracy: 1.0000 - val_loss: 0.1589 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 287/1000\n","5/5 - 1s - loss: 5.8101e-06 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 288/1000\n","5/5 - 1s - loss: 5.7674e-06 - accuracy: 1.0000 - val_loss: 0.1591 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 289/1000\n","5/5 - 1s - loss: 5.7260e-06 - accuracy: 1.0000 - val_loss: 0.1592 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 290/1000\n","5/5 - 1s - loss: 5.6849e-06 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 291/1000\n","5/5 - 1s - loss: 5.6431e-06 - accuracy: 1.0000 - val_loss: 0.1594 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 292/1000\n","5/5 - 1s - loss: 5.6033e-06 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 293/1000\n","5/5 - 1s - loss: 5.5633e-06 - accuracy: 1.0000 - val_loss: 0.1596 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 294/1000\n","5/5 - 1s - loss: 5.5230e-06 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 295/1000\n","5/5 - 1s - loss: 5.4848e-06 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 296/1000\n","5/5 - 1s - loss: 5.4453e-06 - accuracy: 1.0000 - val_loss: 0.1599 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 297/1000\n","5/5 - 1s - loss: 5.4062e-06 - accuracy: 1.0000 - val_loss: 0.1600 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 298/1000\n","5/5 - 1s - loss: 5.3680e-06 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 299/1000\n","5/5 - 1s - loss: 5.3310e-06 - accuracy: 1.0000 - val_loss: 0.1602 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 300/1000\n","5/5 - 1s - loss: 5.2924e-06 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 301/1000\n","5/5 - 1s - loss: 5.2553e-06 - accuracy: 1.0000 - val_loss: 0.1604 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 302/1000\n","5/5 - 1s - loss: 5.2168e-06 - accuracy: 1.0000 - val_loss: 0.1605 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 303/1000\n","5/5 - 1s - loss: 5.1827e-06 - accuracy: 1.0000 - val_loss: 0.1606 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 304/1000\n","5/5 - 1s - loss: 5.1447e-06 - accuracy: 1.0000 - val_loss: 0.1607 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 305/1000\n","5/5 - 1s - loss: 5.1088e-06 - accuracy: 1.0000 - val_loss: 0.1608 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 306/1000\n","5/5 - 1s - loss: 5.0727e-06 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 307/1000\n","5/5 - 1s - loss: 5.0370e-06 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 308/1000\n","5/5 - 1s - loss: 5.0021e-06 - accuracy: 1.0000 - val_loss: 0.1610 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 309/1000\n","5/5 - 1s - loss: 4.9675e-06 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 310/1000\n","5/5 - 1s - loss: 4.9312e-06 - accuracy: 1.0000 - val_loss: 0.1612 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 311/1000\n","5/5 - 1s - loss: 4.8985e-06 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 312/1000\n","5/5 - 1s - loss: 4.8648e-06 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 313/1000\n","5/5 - 1s - loss: 4.8302e-06 - accuracy: 1.0000 - val_loss: 0.1615 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 314/1000\n","5/5 - 1s - loss: 4.7976e-06 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 315/1000\n","5/5 - 1s - loss: 4.7630e-06 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 316/1000\n","5/5 - 1s - loss: 4.7301e-06 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 317/1000\n","5/5 - 1s - loss: 4.7003e-06 - accuracy: 1.0000 - val_loss: 0.1619 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 318/1000\n","5/5 - 1s - loss: 4.6651e-06 - accuracy: 1.0000 - val_loss: 0.1620 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 319/1000\n","5/5 - 1s - loss: 4.6342e-06 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 320/1000\n","5/5 - 1s - loss: 4.6016e-06 - accuracy: 1.0000 - val_loss: 0.1622 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 321/1000\n","5/5 - 1s - loss: 4.5715e-06 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 322/1000\n","5/5 - 1s - loss: 4.5392e-06 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 323/1000\n","5/5 - 1s - loss: 4.5075e-06 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 324/1000\n","5/5 - 1s - loss: 4.4776e-06 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 325/1000\n","5/5 - 1s - loss: 4.4470e-06 - accuracy: 1.0000 - val_loss: 0.1627 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 326/1000\n","5/5 - 1s - loss: 4.4174e-06 - accuracy: 1.0000 - val_loss: 0.1627 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 327/1000\n","5/5 - 1s - loss: 4.3859e-06 - accuracy: 1.0000 - val_loss: 0.1628 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 328/1000\n","5/5 - 1s - loss: 4.3590e-06 - accuracy: 1.0000 - val_loss: 0.1629 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 329/1000\n","5/5 - 1s - loss: 4.3286e-06 - accuracy: 1.0000 - val_loss: 0.1630 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 330/1000\n","5/5 - 1s - loss: 4.2987e-06 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 331/1000\n","5/5 - 1s - loss: 4.2705e-06 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 332/1000\n","5/5 - 1s - loss: 4.2408e-06 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 333/1000\n","5/5 - 1s - loss: 4.2130e-06 - accuracy: 1.0000 - val_loss: 0.1634 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 334/1000\n","5/5 - 1s - loss: 4.1855e-06 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 335/1000\n","5/5 - 1s - loss: 4.1564e-06 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 336/1000\n","5/5 - 1s - loss: 4.1288e-06 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 337/1000\n","5/5 - 1s - loss: 4.1019e-06 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 338/1000\n","5/5 - 1s - loss: 4.0746e-06 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 339/1000\n","5/5 - 1s - loss: 4.0474e-06 - accuracy: 1.0000 - val_loss: 0.1639 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 340/1000\n","5/5 - 1s - loss: 4.0218e-06 - accuracy: 1.0000 - val_loss: 0.1640 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 341/1000\n","5/5 - 1s - loss: 3.9935e-06 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 342/1000\n","5/5 - 1s - loss: 3.9667e-06 - accuracy: 1.0000 - val_loss: 0.1642 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 343/1000\n","5/5 - 1s - loss: 3.9416e-06 - accuracy: 1.0000 - val_loss: 0.1643 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 344/1000\n","5/5 - 1s - loss: 3.9150e-06 - accuracy: 1.0000 - val_loss: 0.1643 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 345/1000\n","5/5 - 1s - loss: 3.8893e-06 - accuracy: 1.0000 - val_loss: 0.1644 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 346/1000\n","5/5 - 1s - loss: 3.8641e-06 - accuracy: 1.0000 - val_loss: 0.1645 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 347/1000\n","5/5 - 1s - loss: 3.8387e-06 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 348/1000\n","5/5 - 1s - loss: 3.8124e-06 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 349/1000\n","5/5 - 1s - loss: 3.7885e-06 - accuracy: 1.0000 - val_loss: 0.1648 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 350/1000\n","5/5 - 1s - loss: 3.7642e-06 - accuracy: 1.0000 - val_loss: 0.1649 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 351/1000\n","5/5 - 1s - loss: 3.7390e-06 - accuracy: 1.0000 - val_loss: 0.1649 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 352/1000\n","5/5 - 1s - loss: 3.7148e-06 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 353/1000\n","5/5 - 1s - loss: 3.6908e-06 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 354/1000\n","5/5 - 1s - loss: 3.6667e-06 - accuracy: 1.0000 - val_loss: 0.1652 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 355/1000\n","5/5 - 1s - loss: 3.6422e-06 - accuracy: 1.0000 - val_loss: 0.1653 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 356/1000\n","5/5 - 1s - loss: 3.6191e-06 - accuracy: 1.0000 - val_loss: 0.1654 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 357/1000\n","5/5 - 1s - loss: 3.5959e-06 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 358/1000\n","5/5 - 1s - loss: 3.5723e-06 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 359/1000\n","5/5 - 1s - loss: 3.5492e-06 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 360/1000\n","5/5 - 1s - loss: 3.5269e-06 - accuracy: 1.0000 - val_loss: 0.1657 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 361/1000\n","5/5 - 1s - loss: 3.5036e-06 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 362/1000\n","5/5 - 1s - loss: 3.4807e-06 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.9836 - 596ms/epoch - 119ms/step\n","Epoch 363/1000\n","5/5 - 1s - loss: 3.4588e-06 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 364/1000\n","5/5 - 1s - loss: 3.4375e-06 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 365/1000\n","5/5 - 1s - loss: 3.4150e-06 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 366/1000\n","5/5 - 1s - loss: 3.3928e-06 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 367/1000\n","5/5 - 1s - loss: 3.3708e-06 - accuracy: 1.0000 - val_loss: 0.1663 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 368/1000\n","5/5 - 1s - loss: 3.3493e-06 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 369/1000\n","5/5 - 1s - loss: 3.3290e-06 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 370/1000\n","5/5 - 1s - loss: 3.3061e-06 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 371/1000\n","5/5 - 1s - loss: 3.2860e-06 - accuracy: 1.0000 - val_loss: 0.1666 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 372/1000\n","5/5 - 1s - loss: 3.2638e-06 - accuracy: 1.0000 - val_loss: 0.1667 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 373/1000\n","5/5 - 1s - loss: 3.2431e-06 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 374/1000\n","5/5 - 1s - loss: 3.2228e-06 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 375/1000\n","5/5 - 1s - loss: 3.2025e-06 - accuracy: 1.0000 - val_loss: 0.1670 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 376/1000\n","5/5 - 1s - loss: 3.1814e-06 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 377/1000\n","5/5 - 1s - loss: 3.1612e-06 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 378/1000\n","5/5 - 1s - loss: 3.1409e-06 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 379/1000\n","5/5 - 1s - loss: 3.1213e-06 - accuracy: 1.0000 - val_loss: 0.1673 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 380/1000\n","5/5 - 1s - loss: 3.1005e-06 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 381/1000\n","5/5 - 1s - loss: 3.0824e-06 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 382/1000\n","5/5 - 1s - loss: 3.0619e-06 - accuracy: 1.0000 - val_loss: 0.1676 - val_accuracy: 0.9836 - 607ms/epoch - 121ms/step\n","Epoch 383/1000\n","5/5 - 1s - loss: 3.0434e-06 - accuracy: 1.0000 - val_loss: 0.1676 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 384/1000\n","5/5 - 1s - loss: 3.0230e-06 - accuracy: 1.0000 - val_loss: 0.1677 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 385/1000\n","5/5 - 1s - loss: 3.0051e-06 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 386/1000\n","5/5 - 1s - loss: 2.9850e-06 - accuracy: 1.0000 - val_loss: 0.1679 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 387/1000\n","5/5 - 1s - loss: 2.9676e-06 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 388/1000\n","5/5 - 1s - loss: 2.9478e-06 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 389/1000\n","5/5 - 1s - loss: 2.9296e-06 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 390/1000\n","5/5 - 1s - loss: 2.9117e-06 - accuracy: 1.0000 - val_loss: 0.1682 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 391/1000\n","5/5 - 1s - loss: 2.8935e-06 - accuracy: 1.0000 - val_loss: 0.1683 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 392/1000\n","5/5 - 1s - loss: 2.8759e-06 - accuracy: 1.0000 - val_loss: 0.1684 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 393/1000\n","5/5 - 1s - loss: 2.8572e-06 - accuracy: 1.0000 - val_loss: 0.1684 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 394/1000\n","5/5 - 1s - loss: 2.8397e-06 - accuracy: 1.0000 - val_loss: 0.1685 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 395/1000\n","5/5 - 1s - loss: 2.8219e-06 - accuracy: 1.0000 - val_loss: 0.1686 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 396/1000\n","5/5 - 1s - loss: 2.8044e-06 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 397/1000\n","5/5 - 1s - loss: 2.7875e-06 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 398/1000\n","5/5 - 1s - loss: 2.7702e-06 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 399/1000\n","5/5 - 1s - loss: 2.7538e-06 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 400/1000\n","5/5 - 1s - loss: 2.7360e-06 - accuracy: 1.0000 - val_loss: 0.1690 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 401/1000\n","5/5 - 1s - loss: 2.7194e-06 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 402/1000\n","5/5 - 1s - loss: 2.7028e-06 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9836 - 606ms/epoch - 121ms/step\n","Epoch 403/1000\n","5/5 - 1s - loss: 2.6854e-06 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 404/1000\n","5/5 - 1s - loss: 2.6698e-06 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9836 - 605ms/epoch - 121ms/step\n","Epoch 405/1000\n","5/5 - 1s - loss: 2.6531e-06 - accuracy: 1.0000 - val_loss: 0.1694 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 406/1000\n","5/5 - 1s - loss: 2.6368e-06 - accuracy: 1.0000 - val_loss: 0.1695 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 407/1000\n","5/5 - 1s - loss: 2.6205e-06 - accuracy: 1.0000 - val_loss: 0.1695 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 408/1000\n","5/5 - 1s - loss: 2.6041e-06 - accuracy: 1.0000 - val_loss: 0.1696 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 409/1000\n","5/5 - 1s - loss: 2.5886e-06 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 410/1000\n","5/5 - 1s - loss: 2.5730e-06 - accuracy: 1.0000 - val_loss: 0.1698 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 411/1000\n","5/5 - 1s - loss: 2.5574e-06 - accuracy: 1.0000 - val_loss: 0.1699 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 412/1000\n","5/5 - 1s - loss: 2.5416e-06 - accuracy: 1.0000 - val_loss: 0.1699 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 413/1000\n","5/5 - 1s - loss: 2.5263e-06 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9836 - 764ms/epoch - 153ms/step\n","Epoch 414/1000\n","5/5 - 1s - loss: 2.5115e-06 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9836 - 650ms/epoch - 130ms/step\n","Epoch 415/1000\n","5/5 - 1s - loss: 2.4965e-06 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 416/1000\n","5/5 - 1s - loss: 2.4808e-06 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9836 - 731ms/epoch - 146ms/step\n","Epoch 417/1000\n","5/5 - 1s - loss: 2.4661e-06 - accuracy: 1.0000 - val_loss: 0.1703 - val_accuracy: 0.9836 - 635ms/epoch - 127ms/step\n","Epoch 418/1000\n","5/5 - 1s - loss: 2.4513e-06 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9836 - 601ms/epoch - 120ms/step\n","Epoch 419/1000\n","5/5 - 1s - loss: 2.4364e-06 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9836 - 618ms/epoch - 124ms/step\n","Epoch 420/1000\n","5/5 - 1s - loss: 2.4221e-06 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 421/1000\n","5/5 - 1s - loss: 2.4070e-06 - accuracy: 1.0000 - val_loss: 0.1706 - val_accuracy: 0.9836 - 721ms/epoch - 144ms/step\n","Epoch 422/1000\n","5/5 - 1s - loss: 2.3929e-06 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9836 - 716ms/epoch - 143ms/step\n","Epoch 423/1000\n","5/5 - 1s - loss: 2.3789e-06 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 424/1000\n","5/5 - 1s - loss: 2.3635e-06 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 425/1000\n","5/5 - 1s - loss: 2.3499e-06 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 426/1000\n","5/5 - 1s - loss: 2.3357e-06 - accuracy: 1.0000 - val_loss: 0.1710 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 427/1000\n","5/5 - 1s - loss: 2.3219e-06 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 428/1000\n","5/5 - 1s - loss: 2.3078e-06 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 429/1000\n","5/5 - 1s - loss: 2.2943e-06 - accuracy: 1.0000 - val_loss: 0.1712 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 430/1000\n","5/5 - 1s - loss: 2.2803e-06 - accuracy: 1.0000 - val_loss: 0.1713 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 431/1000\n","5/5 - 1s - loss: 2.2665e-06 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 432/1000\n","5/5 - 1s - loss: 2.2533e-06 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 433/1000\n","5/5 - 1s - loss: 2.2400e-06 - accuracy: 1.0000 - val_loss: 0.1715 - val_accuracy: 0.9836 - 539ms/epoch - 108ms/step\n","Epoch 434/1000\n","5/5 - 1s - loss: 2.2268e-06 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 435/1000\n","5/5 - 1s - loss: 2.2133e-06 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 436/1000\n","5/5 - 1s - loss: 2.2004e-06 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 437/1000\n","5/5 - 1s - loss: 2.1871e-06 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9836 - 674ms/epoch - 135ms/step\n","Epoch 438/1000\n","5/5 - 1s - loss: 2.1744e-06 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 439/1000\n","5/5 - 1s - loss: 2.1615e-06 - accuracy: 1.0000 - val_loss: 0.1720 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 440/1000\n","5/5 - 1s - loss: 2.1490e-06 - accuracy: 1.0000 - val_loss: 0.1720 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 441/1000\n","5/5 - 1s - loss: 2.1363e-06 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9836 - 617ms/epoch - 123ms/step\n","Epoch 442/1000\n","5/5 - 1s - loss: 2.1235e-06 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9836 - 605ms/epoch - 121ms/step\n","Epoch 443/1000\n","5/5 - 1s - loss: 2.1109e-06 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 444/1000\n","5/5 - 1s - loss: 2.0986e-06 - accuracy: 1.0000 - val_loss: 0.1723 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 445/1000\n","5/5 - 1s - loss: 2.0867e-06 - accuracy: 1.0000 - val_loss: 0.1724 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 446/1000\n","5/5 - 1s - loss: 2.0746e-06 - accuracy: 1.0000 - val_loss: 0.1725 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 447/1000\n","5/5 - 1s - loss: 2.0625e-06 - accuracy: 1.0000 - val_loss: 0.1725 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 448/1000\n","5/5 - 1s - loss: 2.0504e-06 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 449/1000\n","5/5 - 1s - loss: 2.0384e-06 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 450/1000\n","5/5 - 1s - loss: 2.0266e-06 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 451/1000\n","5/5 - 1s - loss: 2.0151e-06 - accuracy: 1.0000 - val_loss: 0.1728 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 452/1000\n","5/5 - 1s - loss: 2.0032e-06 - accuracy: 1.0000 - val_loss: 0.1729 - val_accuracy: 0.9836 - 567ms/epoch - 113ms/step\n","Epoch 453/1000\n","5/5 - 1s - loss: 1.9915e-06 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 454/1000\n","5/5 - 1s - loss: 1.9800e-06 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 455/1000\n","5/5 - 1s - loss: 1.9685e-06 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 456/1000\n","5/5 - 1s - loss: 1.9574e-06 - accuracy: 1.0000 - val_loss: 0.1732 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 457/1000\n","5/5 - 1s - loss: 1.9456e-06 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 458/1000\n","5/5 - 1s - loss: 1.9346e-06 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 459/1000\n","5/5 - 1s - loss: 1.9232e-06 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 460/1000\n","5/5 - 1s - loss: 1.9120e-06 - accuracy: 1.0000 - val_loss: 0.1735 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 461/1000\n","5/5 - 1s - loss: 1.9010e-06 - accuracy: 1.0000 - val_loss: 0.1735 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 462/1000\n","5/5 - 1s - loss: 1.8903e-06 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 463/1000\n","5/5 - 1s - loss: 1.8793e-06 - accuracy: 1.0000 - val_loss: 0.1737 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 464/1000\n","5/5 - 1s - loss: 1.8688e-06 - accuracy: 1.0000 - val_loss: 0.1738 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 465/1000\n","5/5 - 1s - loss: 1.8576e-06 - accuracy: 1.0000 - val_loss: 0.1738 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 466/1000\n","5/5 - 1s - loss: 1.8475e-06 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 467/1000\n","5/5 - 1s - loss: 1.8366e-06 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 468/1000\n","5/5 - 1s - loss: 1.8262e-06 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 469/1000\n","5/5 - 1s - loss: 1.8156e-06 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 470/1000\n","5/5 - 1s - loss: 1.8057e-06 - accuracy: 1.0000 - val_loss: 0.1742 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 471/1000\n","5/5 - 1s - loss: 1.7949e-06 - accuracy: 1.0000 - val_loss: 0.1742 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 472/1000\n","5/5 - 1s - loss: 1.7848e-06 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 473/1000\n","5/5 - 1s - loss: 1.7748e-06 - accuracy: 1.0000 - val_loss: 0.1744 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 474/1000\n","5/5 - 1s - loss: 1.7648e-06 - accuracy: 1.0000 - val_loss: 0.1745 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 475/1000\n","5/5 - 1s - loss: 1.7549e-06 - accuracy: 1.0000 - val_loss: 0.1745 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 476/1000\n","5/5 - 1s - loss: 1.7445e-06 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 477/1000\n","5/5 - 1s - loss: 1.7348e-06 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 478/1000\n","5/5 - 1s - loss: 1.7251e-06 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 479/1000\n","5/5 - 1s - loss: 1.7156e-06 - accuracy: 1.0000 - val_loss: 0.1748 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 480/1000\n","5/5 - 1s - loss: 1.7055e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 481/1000\n","5/5 - 1s - loss: 1.6960e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 482/1000\n","5/5 - 1s - loss: 1.6865e-06 - accuracy: 1.0000 - val_loss: 0.1750 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 483/1000\n","5/5 - 1s - loss: 1.6773e-06 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 484/1000\n","5/5 - 1s - loss: 1.6679e-06 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 485/1000\n","5/5 - 1s - loss: 1.6585e-06 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 486/1000\n","5/5 - 1s - loss: 1.6492e-06 - accuracy: 1.0000 - val_loss: 0.1753 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 487/1000\n","5/5 - 1s - loss: 1.6399e-06 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 488/1000\n","5/5 - 1s - loss: 1.6311e-06 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 489/1000\n","5/5 - 1s - loss: 1.6218e-06 - accuracy: 1.0000 - val_loss: 0.1755 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 490/1000\n","5/5 - 1s - loss: 1.6129e-06 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 491/1000\n","5/5 - 1s - loss: 1.6040e-06 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9836 - 572ms/epoch - 114ms/step\n","Epoch 492/1000\n","5/5 - 1s - loss: 1.5949e-06 - accuracy: 1.0000 - val_loss: 0.1757 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 493/1000\n","5/5 - 1s - loss: 1.5863e-06 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 494/1000\n","5/5 - 1s - loss: 1.5772e-06 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 495/1000\n","5/5 - 1s - loss: 1.5688e-06 - accuracy: 1.0000 - val_loss: 0.1759 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 496/1000\n","5/5 - 1s - loss: 1.5599e-06 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 497/1000\n","5/5 - 1s - loss: 1.5513e-06 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 498/1000\n","5/5 - 1s - loss: 1.5430e-06 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 499/1000\n","5/5 - 1s - loss: 1.5343e-06 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 500/1000\n","5/5 - 1s - loss: 1.5262e-06 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 501/1000\n","5/5 - 1s - loss: 1.5174e-06 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 502/1000\n","5/5 - 1s - loss: 1.5094e-06 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 503/1000\n","5/5 - 1s - loss: 1.5010e-06 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 504/1000\n","5/5 - 1s - loss: 1.4932e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 505/1000\n","5/5 - 1s - loss: 1.4847e-06 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 506/1000\n","5/5 - 1s - loss: 1.4767e-06 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 507/1000\n","5/5 - 1s - loss: 1.4686e-06 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 508/1000\n","5/5 - 1s - loss: 1.4607e-06 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 509/1000\n","5/5 - 1s - loss: 1.4529e-06 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 510/1000\n","5/5 - 1s - loss: 1.4452e-06 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 511/1000\n","5/5 - 1s - loss: 1.4369e-06 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 512/1000\n","5/5 - 1s - loss: 1.4293e-06 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 513/1000\n","5/5 - 1s - loss: 1.4217e-06 - accuracy: 1.0000 - val_loss: 0.1771 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 514/1000\n","5/5 - 1s - loss: 1.4135e-06 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 515/1000\n","5/5 - 1s - loss: 1.4061e-06 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 516/1000\n","5/5 - 1s - loss: 1.3986e-06 - accuracy: 1.0000 - val_loss: 0.1773 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 517/1000\n","5/5 - 1s - loss: 1.3908e-06 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 518/1000\n","5/5 - 1s - loss: 1.3833e-06 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 519/1000\n","5/5 - 1s - loss: 1.3761e-06 - accuracy: 1.0000 - val_loss: 0.1775 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 520/1000\n","5/5 - 1s - loss: 1.3689e-06 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 521/1000\n","5/5 - 1s - loss: 1.3612e-06 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 522/1000\n","5/5 - 1s - loss: 1.3538e-06 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 523/1000\n","5/5 - 1s - loss: 1.3465e-06 - accuracy: 1.0000 - val_loss: 0.1778 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 524/1000\n","5/5 - 1s - loss: 1.3395e-06 - accuracy: 1.0000 - val_loss: 0.1778 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 525/1000\n","5/5 - 1s - loss: 1.3323e-06 - accuracy: 1.0000 - val_loss: 0.1779 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 526/1000\n","5/5 - 1s - loss: 1.3252e-06 - accuracy: 1.0000 - val_loss: 0.1780 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 527/1000\n","5/5 - 1s - loss: 1.3181e-06 - accuracy: 1.0000 - val_loss: 0.1780 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 528/1000\n","5/5 - 1s - loss: 1.3109e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 529/1000\n","5/5 - 1s - loss: 1.3041e-06 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 530/1000\n","5/5 - 1s - loss: 1.2970e-06 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 531/1000\n","5/5 - 1s - loss: 1.2902e-06 - accuracy: 1.0000 - val_loss: 0.1783 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 532/1000\n","5/5 - 1s - loss: 1.2832e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 533/1000\n","5/5 - 1s - loss: 1.2767e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 534/1000\n","5/5 - 1s - loss: 1.2699e-06 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 535/1000\n","5/5 - 1s - loss: 1.2630e-06 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 536/1000\n","5/5 - 1s - loss: 1.2566e-06 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 537/1000\n","5/5 - 1s - loss: 1.2499e-06 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 538/1000\n","5/5 - 1s - loss: 1.2432e-06 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 539/1000\n","5/5 - 1s - loss: 1.2367e-06 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 540/1000\n","5/5 - 1s - loss: 1.2302e-06 - accuracy: 1.0000 - val_loss: 0.1789 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 541/1000\n","5/5 - 1s - loss: 1.2238e-06 - accuracy: 1.0000 - val_loss: 0.1789 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 542/1000\n","5/5 - 1s - loss: 1.2175e-06 - accuracy: 1.0000 - val_loss: 0.1790 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 543/1000\n","5/5 - 1s - loss: 1.2110e-06 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 544/1000\n","5/5 - 1s - loss: 1.2048e-06 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 545/1000\n","5/5 - 1s - loss: 1.1986e-06 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 546/1000\n","5/5 - 1s - loss: 1.1922e-06 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 547/1000\n","5/5 - 1s - loss: 1.1861e-06 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 548/1000\n","5/5 - 1s - loss: 1.1796e-06 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 549/1000\n","5/5 - 1s - loss: 1.1738e-06 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 550/1000\n","5/5 - 1s - loss: 1.1677e-06 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 551/1000\n","5/5 - 1s - loss: 1.1616e-06 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 552/1000\n","5/5 - 1s - loss: 1.1560e-06 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 553/1000\n","5/5 - 1s - loss: 1.1497e-06 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 554/1000\n","5/5 - 1s - loss: 1.1438e-06 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 555/1000\n","5/5 - 1s - loss: 1.1380e-06 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 556/1000\n","5/5 - 1s - loss: 1.1321e-06 - accuracy: 1.0000 - val_loss: 0.1799 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 557/1000\n","5/5 - 1s - loss: 1.1264e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 558/1000\n","5/5 - 1s - loss: 1.1206e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 559/1000\n","5/5 - 1s - loss: 1.1147e-06 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 560/1000\n","5/5 - 1s - loss: 1.1090e-06 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 561/1000\n","5/5 - 1s - loss: 1.1036e-06 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 562/1000\n","5/5 - 1s - loss: 1.0978e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 563/1000\n","5/5 - 1s - loss: 1.0922e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 564/1000\n","5/5 - 1s - loss: 1.0867e-06 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9836 - 607ms/epoch - 121ms/step\n","Epoch 565/1000\n","5/5 - 1s - loss: 1.0814e-06 - accuracy: 1.0000 - val_loss: 0.1805 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 566/1000\n","5/5 - 1s - loss: 1.0755e-06 - accuracy: 1.0000 - val_loss: 0.1805 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 567/1000\n","5/5 - 1s - loss: 1.0704e-06 - accuracy: 1.0000 - val_loss: 0.1806 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 568/1000\n","5/5 - 1s - loss: 1.0649e-06 - accuracy: 1.0000 - val_loss: 0.1807 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 569/1000\n","5/5 - 1s - loss: 1.0599e-06 - accuracy: 1.0000 - val_loss: 0.1807 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 570/1000\n","5/5 - 1s - loss: 1.0541e-06 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 571/1000\n","5/5 - 1s - loss: 1.0488e-06 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 572/1000\n","5/5 - 1s - loss: 1.0437e-06 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 573/1000\n","5/5 - 1s - loss: 1.0386e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 574/1000\n","5/5 - 1s - loss: 1.0333e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 575/1000\n","5/5 - 1s - loss: 1.0277e-06 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 576/1000\n","5/5 - 1s - loss: 1.0228e-06 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 577/1000\n","5/5 - 1s - loss: 1.0180e-06 - accuracy: 1.0000 - val_loss: 0.1812 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 578/1000\n","5/5 - 1s - loss: 1.0124e-06 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 579/1000\n","5/5 - 1s - loss: 1.0074e-06 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 580/1000\n","5/5 - 1s - loss: 1.0026e-06 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 581/1000\n","5/5 - 1s - loss: 9.9731e-07 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 582/1000\n","5/5 - 1s - loss: 9.9274e-07 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 583/1000\n","5/5 - 1s - loss: 9.8761e-07 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 584/1000\n","5/5 - 1s - loss: 9.8251e-07 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 585/1000\n","5/5 - 1s - loss: 9.7772e-07 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 586/1000\n","5/5 - 1s - loss: 9.7270e-07 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 587/1000\n","5/5 - 1s - loss: 9.6827e-07 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 588/1000\n","5/5 - 1s - loss: 9.6331e-07 - accuracy: 1.0000 - val_loss: 0.1819 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 589/1000\n","5/5 - 1s - loss: 9.5831e-07 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 590/1000\n","5/5 - 1s - loss: 9.5369e-07 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 591/1000\n","5/5 - 1s - loss: 9.4896e-07 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 592/1000\n","5/5 - 1s - loss: 9.4431e-07 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 593/1000\n","5/5 - 1s - loss: 9.3934e-07 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 594/1000\n","5/5 - 1s - loss: 9.3498e-07 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 595/1000\n","5/5 - 1s - loss: 9.3020e-07 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 596/1000\n","5/5 - 1s - loss: 9.2567e-07 - accuracy: 1.0000 - val_loss: 0.1824 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 597/1000\n","5/5 - 1s - loss: 9.2107e-07 - accuracy: 1.0000 - val_loss: 0.1824 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 598/1000\n","5/5 - 1s - loss: 9.1666e-07 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 599/1000\n","5/5 - 1s - loss: 9.1207e-07 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 600/1000\n","5/5 - 1s - loss: 9.0753e-07 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 601/1000\n","5/5 - 1s - loss: 9.0324e-07 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 602/1000\n","5/5 - 1s - loss: 8.9886e-07 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 603/1000\n","5/5 - 1s - loss: 8.9431e-07 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 604/1000\n","5/5 - 1s - loss: 8.9009e-07 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 605/1000\n","5/5 - 1s - loss: 8.8561e-07 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 606/1000\n","5/5 - 1s - loss: 8.8158e-07 - accuracy: 1.0000 - val_loss: 0.1830 - val_accuracy: 0.9836 - 600ms/epoch - 120ms/step\n","Epoch 607/1000\n","5/5 - 1s - loss: 8.7730e-07 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 608/1000\n","5/5 - 1s - loss: 8.7307e-07 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 609/1000\n","5/5 - 1s - loss: 8.6880e-07 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 610/1000\n","5/5 - 1s - loss: 8.6457e-07 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 611/1000\n","5/5 - 1s - loss: 8.6040e-07 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 612/1000\n","5/5 - 1s - loss: 8.5635e-07 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 613/1000\n","5/5 - 1s - loss: 8.5223e-07 - accuracy: 1.0000 - val_loss: 0.1834 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 614/1000\n","5/5 - 1s - loss: 8.4812e-07 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 615/1000\n","5/5 - 1s - loss: 8.4421e-07 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 616/1000\n","5/5 - 1s - loss: 8.3997e-07 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9836 - 586ms/epoch - 117ms/step\n","Epoch 617/1000\n","5/5 - 1s - loss: 8.3599e-07 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 618/1000\n","5/5 - 1s - loss: 8.3212e-07 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 619/1000\n","5/5 - 1s - loss: 8.2799e-07 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9836 - 683ms/epoch - 137ms/step\n","Epoch 620/1000\n","5/5 - 1s - loss: 8.2410e-07 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 621/1000\n","5/5 - 1s - loss: 8.2009e-07 - accuracy: 1.0000 - val_loss: 0.1839 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 622/1000\n","5/5 - 1s - loss: 8.1625e-07 - accuracy: 1.0000 - val_loss: 0.1839 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 623/1000\n","5/5 - 1s - loss: 8.1244e-07 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 624/1000\n","5/5 - 1s - loss: 8.0853e-07 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 625/1000\n","5/5 - 1s - loss: 8.0452e-07 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 626/1000\n","5/5 - 1s - loss: 8.0091e-07 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 627/1000\n","5/5 - 1s - loss: 7.9700e-07 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 628/1000\n","5/5 - 1s - loss: 7.9325e-07 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 629/1000\n","5/5 - 1s - loss: 7.8953e-07 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 630/1000\n","5/5 - 1s - loss: 7.8564e-07 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 631/1000\n","5/5 - 1s - loss: 7.8203e-07 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 632/1000\n","5/5 - 1s - loss: 7.7852e-07 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 633/1000\n","5/5 - 1s - loss: 7.7467e-07 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 634/1000\n","5/5 - 1s - loss: 7.7093e-07 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 635/1000\n","5/5 - 1s - loss: 7.6758e-07 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 636/1000\n","5/5 - 1s - loss: 7.6383e-07 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 637/1000\n","5/5 - 1s - loss: 7.6027e-07 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 638/1000\n","5/5 - 1s - loss: 7.5670e-07 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 639/1000\n","5/5 - 1s - loss: 7.5322e-07 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 640/1000\n","5/5 - 1s - loss: 7.4975e-07 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 641/1000\n","5/5 - 1s - loss: 7.4618e-07 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 642/1000\n","5/5 - 1s - loss: 7.4262e-07 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 643/1000\n","5/5 - 1s - loss: 7.3931e-07 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 644/1000\n","5/5 - 1s - loss: 7.3584e-07 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9836 - 567ms/epoch - 113ms/step\n","Epoch 645/1000\n","5/5 - 1s - loss: 7.3258e-07 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 646/1000\n","5/5 - 1s - loss: 7.2920e-07 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 647/1000\n","5/5 - 1s - loss: 7.2571e-07 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 648/1000\n","5/5 - 1s - loss: 7.2241e-07 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 649/1000\n","5/5 - 1s - loss: 7.1906e-07 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 650/1000\n","5/5 - 1s - loss: 7.1578e-07 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 651/1000\n","5/5 - 1s - loss: 7.1265e-07 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 652/1000\n","5/5 - 1s - loss: 7.0922e-07 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 653/1000\n","5/5 - 1s - loss: 7.0611e-07 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 654/1000\n","5/5 - 1s - loss: 7.0275e-07 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 655/1000\n","5/5 - 1s - loss: 6.9974e-07 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 656/1000\n","5/5 - 1s - loss: 6.9639e-07 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 657/1000\n","5/5 - 1s - loss: 6.9319e-07 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 658/1000\n","5/5 - 1s - loss: 6.9014e-07 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 659/1000\n","5/5 - 1s - loss: 6.8702e-07 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9836 - 637ms/epoch - 127ms/step\n","Epoch 660/1000\n","5/5 - 1s - loss: 6.8384e-07 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 661/1000\n","5/5 - 1s - loss: 6.8065e-07 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 662/1000\n","5/5 - 1s - loss: 6.7774e-07 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 663/1000\n","5/5 - 1s - loss: 6.7463e-07 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 664/1000\n","5/5 - 1s - loss: 6.7162e-07 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 665/1000\n","5/5 - 1s - loss: 6.6860e-07 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 666/1000\n","5/5 - 1s - loss: 6.6563e-07 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 667/1000\n","5/5 - 1s - loss: 6.6258e-07 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 668/1000\n","5/5 - 1s - loss: 6.5963e-07 - accuracy: 1.0000 - val_loss: 0.1866 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 669/1000\n","5/5 - 1s - loss: 6.5672e-07 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 670/1000\n","5/5 - 1s - loss: 6.5390e-07 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 671/1000\n","5/5 - 1s - loss: 6.5088e-07 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 672/1000\n","5/5 - 1s - loss: 6.4792e-07 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 673/1000\n","5/5 - 1s - loss: 6.4520e-07 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 674/1000\n","5/5 - 1s - loss: 6.4222e-07 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 675/1000\n","5/5 - 1s - loss: 6.3946e-07 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 676/1000\n","5/5 - 1s - loss: 6.3653e-07 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 677/1000\n","5/5 - 1s - loss: 6.3371e-07 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 678/1000\n","5/5 - 1s - loss: 6.3101e-07 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 679/1000\n","5/5 - 1s - loss: 6.2816e-07 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 680/1000\n","5/5 - 1s - loss: 6.2528e-07 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9836 - 537ms/epoch - 107ms/step\n","Epoch 681/1000\n","5/5 - 1s - loss: 6.2250e-07 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 682/1000\n","5/5 - 1s - loss: 6.1982e-07 - accuracy: 1.0000 - val_loss: 0.1874 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 683/1000\n","5/5 - 1s - loss: 6.1698e-07 - accuracy: 1.0000 - val_loss: 0.1874 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 684/1000\n","5/5 - 1s - loss: 6.1431e-07 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 685/1000\n","5/5 - 1s - loss: 6.1160e-07 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 686/1000\n","5/5 - 1s - loss: 6.0887e-07 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 687/1000\n","5/5 - 1s - loss: 6.0615e-07 - accuracy: 1.0000 - val_loss: 0.1877 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 688/1000\n","5/5 - 1s - loss: 6.0354e-07 - accuracy: 1.0000 - val_loss: 0.1877 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 689/1000\n","5/5 - 1s - loss: 6.0085e-07 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 690/1000\n","5/5 - 1s - loss: 5.9811e-07 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 691/1000\n","5/5 - 1s - loss: 5.9565e-07 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 692/1000\n","5/5 - 1s - loss: 5.9281e-07 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 693/1000\n","5/5 - 1s - loss: 5.9025e-07 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 694/1000\n","5/5 - 1s - loss: 5.8773e-07 - accuracy: 1.0000 - val_loss: 0.1881 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 695/1000\n","5/5 - 1s - loss: 5.8501e-07 - accuracy: 1.0000 - val_loss: 0.1881 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 696/1000\n","5/5 - 1s - loss: 5.8247e-07 - accuracy: 1.0000 - val_loss: 0.1882 - val_accuracy: 0.9836 - 1s/epoch - 249ms/step\n","Epoch 697/1000\n","5/5 - 1s - loss: 5.8005e-07 - accuracy: 1.0000 - val_loss: 0.1882 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 698/1000\n","5/5 - 1s - loss: 5.7730e-07 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9836 - 1s/epoch - 219ms/step\n","Epoch 699/1000\n","5/5 - 1s - loss: 5.7483e-07 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9836 - 934ms/epoch - 187ms/step\n","Epoch 700/1000\n","5/5 - 1s - loss: 5.7229e-07 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9836 - 1s/epoch - 210ms/step\n","Epoch 701/1000\n","5/5 - 1s - loss: 5.6975e-07 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9836 - 651ms/epoch - 130ms/step\n","Epoch 702/1000\n","5/5 - 1s - loss: 5.6730e-07 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9836 - 698ms/epoch - 140ms/step\n","Epoch 703/1000\n","5/5 - 1s - loss: 5.6489e-07 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 704/1000\n","5/5 - 1s - loss: 5.6235e-07 - accuracy: 1.0000 - val_loss: 0.1886 - val_accuracy: 0.9836 - 677ms/epoch - 135ms/step\n","Epoch 705/1000\n","5/5 - 1s - loss: 5.5994e-07 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 706/1000\n","5/5 - 1s - loss: 5.5753e-07 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 707/1000\n","5/5 - 1s - loss: 5.5495e-07 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 708/1000\n","5/5 - 1s - loss: 5.5273e-07 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 709/1000\n","5/5 - 1s - loss: 5.5017e-07 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 710/1000\n","5/5 - 1s - loss: 5.4787e-07 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9836 - 656ms/epoch - 131ms/step\n","Epoch 711/1000\n","5/5 - 1s - loss: 5.4562e-07 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9836 - 760ms/epoch - 152ms/step\n","Epoch 712/1000\n","5/5 - 1s - loss: 5.4319e-07 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9836 - 657ms/epoch - 131ms/step\n","Epoch 713/1000\n","5/5 - 1s - loss: 5.4089e-07 - accuracy: 1.0000 - val_loss: 0.1891 - val_accuracy: 0.9836 - 672ms/epoch - 134ms/step\n","Epoch 714/1000\n","5/5 - 1s - loss: 5.3857e-07 - accuracy: 1.0000 - val_loss: 0.1891 - val_accuracy: 0.9836 - 680ms/epoch - 136ms/step\n","Epoch 715/1000\n","5/5 - 1s - loss: 5.3636e-07 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 716/1000\n","5/5 - 1s - loss: 5.3399e-07 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9836 - 616ms/epoch - 123ms/step\n","Epoch 717/1000\n","5/5 - 1s - loss: 5.3172e-07 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 718/1000\n","5/5 - 1s - loss: 5.2953e-07 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 719/1000\n","5/5 - 1s - loss: 5.2730e-07 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 720/1000\n","5/5 - 1s - loss: 5.2510e-07 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9836 - 677ms/epoch - 135ms/step\n","Epoch 721/1000\n","5/5 - 1s - loss: 5.2284e-07 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9836 - 639ms/epoch - 128ms/step\n","Epoch 722/1000\n","5/5 - 1s - loss: 5.2068e-07 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 723/1000\n","5/5 - 1s - loss: 5.1860e-07 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9836 - 719ms/epoch - 144ms/step\n","Epoch 724/1000\n","5/5 - 1s - loss: 5.1635e-07 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9836 - 602ms/epoch - 120ms/step\n","Epoch 725/1000\n","5/5 - 1s - loss: 5.1414e-07 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 726/1000\n","5/5 - 1s - loss: 5.1200e-07 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 727/1000\n","5/5 - 1s - loss: 5.1006e-07 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 728/1000\n","5/5 - 1s - loss: 5.0781e-07 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 729/1000\n","5/5 - 1s - loss: 5.0567e-07 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 730/1000\n","5/5 - 1s - loss: 5.0358e-07 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 731/1000\n","5/5 - 1s - loss: 5.0148e-07 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 732/1000\n","5/5 - 1s - loss: 4.9944e-07 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 733/1000\n","5/5 - 1s - loss: 4.9735e-07 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 734/1000\n","5/5 - 1s - loss: 4.9521e-07 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9836 - 632ms/epoch - 126ms/step\n","Epoch 735/1000\n","5/5 - 1s - loss: 4.9305e-07 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 736/1000\n","5/5 - 1s - loss: 4.9102e-07 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 737/1000\n","5/5 - 1s - loss: 4.8909e-07 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 738/1000\n","5/5 - 1s - loss: 4.8690e-07 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 739/1000\n","5/5 - 1s - loss: 4.8484e-07 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 740/1000\n","5/5 - 1s - loss: 4.8287e-07 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 741/1000\n","5/5 - 1s - loss: 4.8080e-07 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 742/1000\n","5/5 - 1s - loss: 4.7883e-07 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9836 - 662ms/epoch - 132ms/step\n","Epoch 743/1000\n","5/5 - 1s - loss: 4.7678e-07 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9836 - 651ms/epoch - 130ms/step\n","Epoch 744/1000\n","5/5 - 1s - loss: 4.7484e-07 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 745/1000\n","5/5 - 1s - loss: 4.7286e-07 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 746/1000\n","5/5 - 1s - loss: 4.7089e-07 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 747/1000\n","5/5 - 1s - loss: 4.6888e-07 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 748/1000\n","5/5 - 1s - loss: 4.6703e-07 - accuracy: 1.0000 - val_loss: 0.1910 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 749/1000\n","5/5 - 1s - loss: 4.6493e-07 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9836 - 870ms/epoch - 174ms/step\n","Epoch 750/1000\n","5/5 - 1s - loss: 4.6298e-07 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9836 - 887ms/epoch - 177ms/step\n","Epoch 751/1000\n","5/5 - 1s - loss: 4.6122e-07 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9836 - 838ms/epoch - 168ms/step\n","Epoch 752/1000\n","5/5 - 1s - loss: 4.5916e-07 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9836 - 710ms/epoch - 142ms/step\n","Epoch 753/1000\n","5/5 - 1s - loss: 4.5728e-07 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9836 - 1s/epoch - 205ms/step\n","Epoch 754/1000\n","5/5 - 1s - loss: 4.5526e-07 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9836 - 983ms/epoch - 197ms/step\n","Epoch 755/1000\n","5/5 - 1s - loss: 4.5344e-07 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9836 - 728ms/epoch - 146ms/step\n","Epoch 756/1000\n","5/5 - 1s - loss: 4.5149e-07 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9836 - 697ms/epoch - 139ms/step\n","Epoch 757/1000\n","5/5 - 1s - loss: 4.4968e-07 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 758/1000\n","5/5 - 1s - loss: 4.4774e-07 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9836 - 780ms/epoch - 156ms/step\n","Epoch 759/1000\n","5/5 - 1s - loss: 4.4582e-07 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9836 - 816ms/epoch - 163ms/step\n","Epoch 760/1000\n","5/5 - 1s - loss: 4.4397e-07 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9836 - 696ms/epoch - 139ms/step\n","Epoch 761/1000\n","5/5 - 1s - loss: 4.4215e-07 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9836 - 812ms/epoch - 162ms/step\n","Epoch 762/1000\n","5/5 - 1s - loss: 4.4025e-07 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9836 - 723ms/epoch - 145ms/step\n","Epoch 763/1000\n","5/5 - 1s - loss: 4.3841e-07 - accuracy: 1.0000 - val_loss: 0.1918 - val_accuracy: 0.9836 - 905ms/epoch - 181ms/step\n","Epoch 764/1000\n","5/5 - 1s - loss: 4.3672e-07 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.9836 - 935ms/epoch - 187ms/step\n","Epoch 765/1000\n","5/5 - 1s - loss: 4.3479e-07 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.9836 - 775ms/epoch - 155ms/step\n","Epoch 766/1000\n","5/5 - 1s - loss: 4.3289e-07 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9836 - 695ms/epoch - 139ms/step\n","Epoch 767/1000\n","5/5 - 1s - loss: 4.3114e-07 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9836 - 867ms/epoch - 173ms/step\n","Epoch 768/1000\n","5/5 - 1s - loss: 4.2933e-07 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9836 - 890ms/epoch - 178ms/step\n","Epoch 769/1000\n","5/5 - 1s - loss: 4.2759e-07 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9836 - 758ms/epoch - 152ms/step\n","Epoch 770/1000\n","5/5 - 1s - loss: 4.2572e-07 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9836 - 813ms/epoch - 163ms/step\n","Epoch 771/1000\n","5/5 - 1s - loss: 4.2405e-07 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9836 - 860ms/epoch - 172ms/step\n","Epoch 772/1000\n","5/5 - 1s - loss: 4.2226e-07 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9836 - 762ms/epoch - 152ms/step\n","Epoch 773/1000\n","5/5 - 1s - loss: 4.2055e-07 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9836 - 885ms/epoch - 177ms/step\n","Epoch 774/1000\n","5/5 - 1s - loss: 4.1880e-07 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9836 - 811ms/epoch - 162ms/step\n","Epoch 775/1000\n","5/5 - 1s - loss: 4.1711e-07 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9836 - 817ms/epoch - 163ms/step\n","Epoch 776/1000\n","5/5 - 1s - loss: 4.1533e-07 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9836 - 1s/epoch - 213ms/step\n","Epoch 777/1000\n","5/5 - 1s - loss: 4.1373e-07 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9836 - 880ms/epoch - 176ms/step\n","Epoch 778/1000\n","5/5 - 1s - loss: 4.1204e-07 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9836 - 1s/epoch - 242ms/step\n","Epoch 779/1000\n","5/5 - 1s - loss: 4.1030e-07 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9836 - 1s/epoch - 266ms/step\n","Epoch 780/1000\n","5/5 - 1s - loss: 4.0863e-07 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9836 - 965ms/epoch - 193ms/step\n","Epoch 781/1000\n","5/5 - 1s - loss: 4.0694e-07 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9836 - 896ms/epoch - 179ms/step\n","Epoch 782/1000\n","5/5 - 1s - loss: 4.0545e-07 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9836 - 965ms/epoch - 193ms/step\n","Epoch 783/1000\n","5/5 - 1s - loss: 4.0371e-07 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 784/1000\n","5/5 - 1s - loss: 4.0209e-07 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9836 - 738ms/epoch - 148ms/step\n","Epoch 785/1000\n","5/5 - 1s - loss: 4.0047e-07 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9836 - 752ms/epoch - 150ms/step\n","Epoch 786/1000\n","5/5 - 1s - loss: 3.9896e-07 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9836 - 849ms/epoch - 170ms/step\n","Epoch 787/1000\n","5/5 - 1s - loss: 3.9731e-07 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9836 - 941ms/epoch - 188ms/step\n","Epoch 788/1000\n","5/5 - 1s - loss: 3.9576e-07 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9836 - 831ms/epoch - 166ms/step\n","Epoch 789/1000\n","5/5 - 1s - loss: 3.9413e-07 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9836 - 717ms/epoch - 143ms/step\n","Epoch 790/1000\n","5/5 - 1s - loss: 3.9271e-07 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 791/1000\n","5/5 - 1s - loss: 3.9113e-07 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9836 - 1s/epoch - 227ms/step\n","Epoch 792/1000\n","5/5 - 1s - loss: 3.8957e-07 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9836 - 1s/epoch - 200ms/step\n","Epoch 793/1000\n","5/5 - 1s - loss: 3.8802e-07 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 794/1000\n","5/5 - 1s - loss: 3.8659e-07 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 795/1000\n","5/5 - 1s - loss: 3.8501e-07 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 796/1000\n","5/5 - 1s - loss: 3.8343e-07 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9836 - 808ms/epoch - 162ms/step\n","Epoch 797/1000\n","5/5 - 1s - loss: 3.8196e-07 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9836 - 803ms/epoch - 161ms/step\n","Epoch 798/1000\n","5/5 - 1s - loss: 3.8050e-07 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9836 - 824ms/epoch - 165ms/step\n","Epoch 799/1000\n","5/5 - 1s - loss: 3.7897e-07 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9836 - 760ms/epoch - 152ms/step\n","Epoch 800/1000\n","5/5 - 1s - loss: 3.7751e-07 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 801/1000\n","5/5 - 1s - loss: 3.7603e-07 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9836 - 890ms/epoch - 178ms/step\n","Epoch 802/1000\n","5/5 - 1s - loss: 3.7443e-07 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9836 - 927ms/epoch - 185ms/step\n","Epoch 803/1000\n","5/5 - 1s - loss: 3.7306e-07 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9836 - 947ms/epoch - 189ms/step\n","Epoch 804/1000\n","5/5 - 1s - loss: 3.7165e-07 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9836 - 814ms/epoch - 163ms/step\n","Epoch 805/1000\n","5/5 - 1s - loss: 3.7015e-07 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9836 - 705ms/epoch - 141ms/step\n","Epoch 806/1000\n","5/5 - 1s - loss: 3.6868e-07 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 807/1000\n","5/5 - 1s - loss: 3.6726e-07 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9836 - 666ms/epoch - 133ms/step\n","Epoch 808/1000\n","5/5 - 1s - loss: 3.6586e-07 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 809/1000\n","5/5 - 1s - loss: 3.6441e-07 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9836 - 715ms/epoch - 143ms/step\n","Epoch 810/1000\n","5/5 - 1s - loss: 3.6302e-07 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9836 - 668ms/epoch - 134ms/step\n","Epoch 811/1000\n","5/5 - 1s - loss: 3.6165e-07 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9836 - 699ms/epoch - 140ms/step\n","Epoch 812/1000\n","5/5 - 1s - loss: 3.6022e-07 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 813/1000\n","5/5 - 1s - loss: 3.5892e-07 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9836 - 713ms/epoch - 143ms/step\n","Epoch 814/1000\n","5/5 - 1s - loss: 3.5743e-07 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9836 - 717ms/epoch - 143ms/step\n","Epoch 815/1000\n","5/5 - 1s - loss: 3.5608e-07 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9836 - 824ms/epoch - 165ms/step\n","Epoch 816/1000\n","5/5 - 1s - loss: 3.5471e-07 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9836 - 893ms/epoch - 179ms/step\n","Epoch 817/1000\n","5/5 - 1s - loss: 3.5331e-07 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9836 - 952ms/epoch - 190ms/step\n","Epoch 818/1000\n","5/5 - 1s - loss: 3.5198e-07 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9836 - 848ms/epoch - 170ms/step\n","Epoch 819/1000\n","5/5 - 1s - loss: 3.5064e-07 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9836 - 835ms/epoch - 167ms/step\n","Epoch 820/1000\n","5/5 - 1s - loss: 3.4936e-07 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9836 - 983ms/epoch - 197ms/step\n","Epoch 821/1000\n","5/5 - 1s - loss: 3.4800e-07 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9836 - 1s/epoch - 221ms/step\n","Epoch 822/1000\n","5/5 - 1s - loss: 3.4669e-07 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9836 - 950ms/epoch - 190ms/step\n","Epoch 823/1000\n","5/5 - 1s - loss: 3.4538e-07 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9836 - 925ms/epoch - 185ms/step\n","Epoch 824/1000\n","5/5 - 1s - loss: 3.4408e-07 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9836 - 923ms/epoch - 185ms/step\n","Epoch 825/1000\n","5/5 - 1s - loss: 3.4270e-07 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9836 - 874ms/epoch - 175ms/step\n","Epoch 826/1000\n","5/5 - 1s - loss: 3.4136e-07 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9836 - 906ms/epoch - 181ms/step\n","Epoch 827/1000\n","5/5 - 1s - loss: 3.4008e-07 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9836 - 924ms/epoch - 185ms/step\n","Epoch 828/1000\n","5/5 - 1s - loss: 3.3880e-07 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 829/1000\n","5/5 - 1s - loss: 3.3752e-07 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9836 - 873ms/epoch - 175ms/step\n","Epoch 830/1000\n","5/5 - 1s - loss: 3.3620e-07 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9836 - 1s/epoch - 265ms/step\n","Epoch 831/1000\n","5/5 - 1s - loss: 3.3490e-07 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 832/1000\n","5/5 - 1s - loss: 3.3364e-07 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9836 - 1s/epoch - 217ms/step\n","Epoch 833/1000\n","5/5 - 1s - loss: 3.3240e-07 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9836 - 973ms/epoch - 195ms/step\n","Epoch 834/1000\n","5/5 - 1s - loss: 3.3102e-07 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9836 - 1s/epoch - 263ms/step\n","Epoch 835/1000\n","5/5 - 1s - loss: 3.2983e-07 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 836/1000\n","5/5 - 1s - loss: 3.2848e-07 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 837/1000\n","5/5 - 1s - loss: 3.2726e-07 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9836 - 1s/epoch - 229ms/step\n","Epoch 838/1000\n","5/5 - 1s - loss: 3.2603e-07 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9836 - 870ms/epoch - 174ms/step\n","Epoch 839/1000\n","5/5 - 1s - loss: 3.2478e-07 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9836 - 893ms/epoch - 179ms/step\n","Epoch 840/1000\n","5/5 - 1s - loss: 3.2346e-07 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9836 - 824ms/epoch - 165ms/step\n","Epoch 841/1000\n","5/5 - 1s - loss: 3.2228e-07 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9836 - 824ms/epoch - 165ms/step\n","Epoch 842/1000\n","5/5 - 1s - loss: 3.2095e-07 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9836 - 869ms/epoch - 174ms/step\n","Epoch 843/1000\n","5/5 - 1s - loss: 3.1982e-07 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9836 - 866ms/epoch - 173ms/step\n","Epoch 844/1000\n","5/5 - 1s - loss: 3.1856e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9836 - 837ms/epoch - 167ms/step\n","Epoch 845/1000\n","5/5 - 1s - loss: 3.1736e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 846/1000\n","5/5 - 1s - loss: 3.1606e-07 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9836 - 947ms/epoch - 189ms/step\n","Epoch 847/1000\n","5/5 - 1s - loss: 3.1499e-07 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9836 - 1s/epoch - 207ms/step\n","Epoch 848/1000\n","5/5 - 1s - loss: 3.1363e-07 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9836 - 975ms/epoch - 195ms/step\n","Epoch 849/1000\n","5/5 - 1s - loss: 3.1244e-07 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9836 - 1s/epoch - 202ms/step\n","Epoch 850/1000\n","5/5 - 1s - loss: 3.1126e-07 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9836 - 900ms/epoch - 180ms/step\n","Epoch 851/1000\n","5/5 - 1s - loss: 3.1003e-07 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 852/1000\n","5/5 - 1s - loss: 3.0885e-07 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 853/1000\n","5/5 - 1s - loss: 3.0775e-07 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9836 - 994ms/epoch - 199ms/step\n","Epoch 854/1000\n","5/5 - 1s - loss: 3.0650e-07 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9836 - 1s/epoch - 252ms/step\n","Epoch 855/1000\n","5/5 - 1s - loss: 3.0534e-07 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 856/1000\n","5/5 - 1s - loss: 3.0420e-07 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9836 - 1s/epoch - 205ms/step\n","Epoch 857/1000\n","5/5 - 1s - loss: 3.0298e-07 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9836 - 873ms/epoch - 175ms/step\n","Epoch 858/1000\n","5/5 - 1s - loss: 3.0183e-07 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9836 - 967ms/epoch - 193ms/step\n","Epoch 859/1000\n","5/5 - 1s - loss: 3.0068e-07 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 860/1000\n","5/5 - 1s - loss: 2.9950e-07 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9836 - 795ms/epoch - 159ms/step\n","Epoch 861/1000\n","5/5 - 1s - loss: 2.9836e-07 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 862/1000\n","5/5 - 1s - loss: 2.9718e-07 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9836 - 861ms/epoch - 172ms/step\n","Epoch 863/1000\n","5/5 - 1s - loss: 2.9606e-07 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9836 - 779ms/epoch - 156ms/step\n","Epoch 864/1000\n","5/5 - 1s - loss: 2.9493e-07 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 865/1000\n","5/5 - 1s - loss: 2.9383e-07 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9836 - 773ms/epoch - 155ms/step\n","Epoch 866/1000\n","5/5 - 1s - loss: 2.9266e-07 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 867/1000\n","5/5 - 1s - loss: 2.9151e-07 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 868/1000\n","5/5 - 1s - loss: 2.9037e-07 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 869/1000\n","5/5 - 1s - loss: 2.8933e-07 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 870/1000\n","5/5 - 1s - loss: 2.8814e-07 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9836 - 662ms/epoch - 132ms/step\n","Epoch 871/1000\n","5/5 - 1s - loss: 2.8705e-07 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 872/1000\n","5/5 - 1s - loss: 2.8598e-07 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 873/1000\n","5/5 - 1s - loss: 2.8484e-07 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9836 - 770ms/epoch - 154ms/step\n","Epoch 874/1000\n","5/5 - 1s - loss: 2.8385e-07 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9836 - 908ms/epoch - 182ms/step\n","Epoch 875/1000\n","5/5 - 1s - loss: 2.8270e-07 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9836 - 861ms/epoch - 172ms/step\n","Epoch 876/1000\n","5/5 - 1s - loss: 2.8163e-07 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9836 - 826ms/epoch - 165ms/step\n","Epoch 877/1000\n","5/5 - 1s - loss: 2.8057e-07 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9836 - 775ms/epoch - 155ms/step\n","Epoch 878/1000\n","5/5 - 1s - loss: 2.7948e-07 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9836 - 734ms/epoch - 147ms/step\n","Epoch 879/1000\n","5/5 - 1s - loss: 2.7850e-07 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9836 - 814ms/epoch - 163ms/step\n","Epoch 880/1000\n","5/5 - 1s - loss: 2.7740e-07 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9836 - 914ms/epoch - 183ms/step\n","Epoch 881/1000\n","5/5 - 1s - loss: 2.7640e-07 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9836 - 829ms/epoch - 166ms/step\n","Epoch 882/1000\n","5/5 - 1s - loss: 2.7538e-07 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9836 - 894ms/epoch - 179ms/step\n","Epoch 883/1000\n","5/5 - 1s - loss: 2.7434e-07 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9836 - 976ms/epoch - 195ms/step\n","Epoch 884/1000\n","5/5 - 1s - loss: 2.7343e-07 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 885/1000\n","5/5 - 1s - loss: 2.7234e-07 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9836 - 1s/epoch - 210ms/step\n","Epoch 886/1000\n","5/5 - 1s - loss: 2.7133e-07 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9836 - 960ms/epoch - 192ms/step\n","Epoch 887/1000\n","5/5 - 1s - loss: 2.7039e-07 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9836 - 972ms/epoch - 194ms/step\n","Epoch 888/1000\n","5/5 - 1s - loss: 2.6941e-07 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9836 - 884ms/epoch - 177ms/step\n","Epoch 889/1000\n","5/5 - 1s - loss: 2.6847e-07 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9836 - 690ms/epoch - 138ms/step\n","Epoch 890/1000\n","5/5 - 1s - loss: 2.6743e-07 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9836 - 777ms/epoch - 155ms/step\n","Epoch 891/1000\n","5/5 - 1s - loss: 2.6647e-07 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9836 - 825ms/epoch - 165ms/step\n","Epoch 892/1000\n","5/5 - 1s - loss: 2.6552e-07 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9836 - 937ms/epoch - 187ms/step\n","Epoch 893/1000\n","5/5 - 1s - loss: 2.6463e-07 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9836 - 851ms/epoch - 170ms/step\n","Epoch 894/1000\n","5/5 - 1s - loss: 2.6357e-07 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9836 - 844ms/epoch - 169ms/step\n","Epoch 895/1000\n","5/5 - 1s - loss: 2.6267e-07 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 896/1000\n","5/5 - 1s - loss: 2.6174e-07 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9836 - 908ms/epoch - 182ms/step\n","Epoch 897/1000\n","5/5 - 1s - loss: 2.6088e-07 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9836 - 899ms/epoch - 180ms/step\n","Epoch 898/1000\n","5/5 - 1s - loss: 2.5989e-07 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9836 - 882ms/epoch - 176ms/step\n","Epoch 899/1000\n","5/5 - 1s - loss: 2.5899e-07 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9836 - 879ms/epoch - 176ms/step\n","Epoch 900/1000\n","5/5 - 1s - loss: 2.5811e-07 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9836 - 872ms/epoch - 174ms/step\n","Epoch 901/1000\n","5/5 - 1s - loss: 2.5715e-07 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9836 - 819ms/epoch - 164ms/step\n","Epoch 902/1000\n","5/5 - 1s - loss: 2.5626e-07 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9836 - 856ms/epoch - 171ms/step\n","Epoch 903/1000\n","5/5 - 1s - loss: 2.5536e-07 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9836 - 921ms/epoch - 184ms/step\n","Epoch 904/1000\n","5/5 - 1s - loss: 2.5453e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 993ms/epoch - 199ms/step\n","Epoch 905/1000\n","5/5 - 1s - loss: 2.5357e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 928ms/epoch - 186ms/step\n","Epoch 906/1000\n","5/5 - 1s - loss: 2.5274e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 969ms/epoch - 194ms/step\n","Epoch 907/1000\n","5/5 - 1s - loss: 2.5184e-07 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9836 - 1s/epoch - 285ms/step\n","Epoch 908/1000\n","5/5 - 2s - loss: 2.5099e-07 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9836 - 2s/epoch - 316ms/step\n","Epoch 909/1000\n","5/5 - 1s - loss: 2.5011e-07 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9836 - 990ms/epoch - 198ms/step\n","Epoch 910/1000\n","5/5 - 1s - loss: 2.4929e-07 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9836 - 1s/epoch - 224ms/step\n","Epoch 911/1000\n","5/5 - 1s - loss: 2.4839e-07 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 912/1000\n","5/5 - 1s - loss: 2.4753e-07 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9836 - 1s/epoch - 208ms/step\n","Epoch 913/1000\n","5/5 - 1s - loss: 2.4671e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 914/1000\n","5/5 - 1s - loss: 2.4582e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 915/1000\n","5/5 - 1s - loss: 2.4502e-07 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9836 - 1s/epoch - 238ms/step\n","Epoch 916/1000\n","5/5 - 1s - loss: 2.4411e-07 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9836 - 879ms/epoch - 176ms/step\n","Epoch 917/1000\n","5/5 - 1s - loss: 2.4327e-07 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9836 - 844ms/epoch - 169ms/step\n","Epoch 918/1000\n","5/5 - 1s - loss: 2.4247e-07 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9836 - 992ms/epoch - 198ms/step\n","Epoch 919/1000\n","5/5 - 1s - loss: 2.4162e-07 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9836 - 819ms/epoch - 164ms/step\n","Epoch 920/1000\n","5/5 - 1s - loss: 2.4077e-07 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9836 - 890ms/epoch - 178ms/step\n","Epoch 921/1000\n","5/5 - 1s - loss: 2.3999e-07 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9836 - 891ms/epoch - 178ms/step\n","Epoch 922/1000\n","5/5 - 1s - loss: 2.3916e-07 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9836 - 886ms/epoch - 177ms/step\n","Epoch 923/1000\n","5/5 - 1s - loss: 2.3830e-07 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9836 - 954ms/epoch - 191ms/step\n","Epoch 924/1000\n","5/5 - 1s - loss: 2.3751e-07 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9836 - 844ms/epoch - 169ms/step\n","Epoch 925/1000\n","5/5 - 1s - loss: 2.3668e-07 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9836 - 821ms/epoch - 164ms/step\n","Epoch 926/1000\n","5/5 - 1s - loss: 2.3586e-07 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9836 - 933ms/epoch - 187ms/step\n","Epoch 927/1000\n","5/5 - 1s - loss: 2.3505e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 928/1000\n","5/5 - 1s - loss: 2.3426e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9836 - 768ms/epoch - 154ms/step\n","Epoch 929/1000\n","5/5 - 1s - loss: 2.3346e-07 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 930/1000\n","5/5 - 1s - loss: 2.3264e-07 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9836 - 734ms/epoch - 147ms/step\n","Epoch 931/1000\n","5/5 - 1s - loss: 2.3181e-07 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9836 - 767ms/epoch - 153ms/step\n","Epoch 932/1000\n","5/5 - 1s - loss: 2.3107e-07 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9836 - 818ms/epoch - 164ms/step\n","Epoch 933/1000\n","5/5 - 1s - loss: 2.3025e-07 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9836 - 742ms/epoch - 148ms/step\n","Epoch 934/1000\n","5/5 - 1s - loss: 2.2950e-07 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 935/1000\n","5/5 - 1s - loss: 2.2872e-07 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9836 - 821ms/epoch - 164ms/step\n","Epoch 936/1000\n","5/5 - 1s - loss: 2.2790e-07 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9836 - 722ms/epoch - 144ms/step\n","Epoch 937/1000\n","5/5 - 1s - loss: 2.2711e-07 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9836 - 741ms/epoch - 148ms/step\n","Epoch 938/1000\n","5/5 - 1s - loss: 2.2634e-07 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 939/1000\n","5/5 - 1s - loss: 2.2559e-07 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9836 - 725ms/epoch - 145ms/step\n","Epoch 940/1000\n","5/5 - 1s - loss: 2.2476e-07 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 941/1000\n","5/5 - 1s - loss: 2.2399e-07 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9836 - 760ms/epoch - 152ms/step\n","Epoch 942/1000\n","5/5 - 1s - loss: 2.2324e-07 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9836 - 727ms/epoch - 145ms/step\n","Epoch 943/1000\n","5/5 - 1s - loss: 2.2249e-07 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9836 - 838ms/epoch - 168ms/step\n","Epoch 944/1000\n","5/5 - 1s - loss: 2.2166e-07 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9836 - 938ms/epoch - 188ms/step\n","Epoch 945/1000\n","5/5 - 1s - loss: 2.2092e-07 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9836 - 1s/epoch - 219ms/step\n","Epoch 946/1000\n","5/5 - 1s - loss: 2.2016e-07 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9836 - 1s/epoch - 214ms/step\n","Epoch 947/1000\n","5/5 - 1s - loss: 2.1937e-07 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9836 - 713ms/epoch - 143ms/step\n","Epoch 948/1000\n","5/5 - 1s - loss: 2.1860e-07 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9836 - 772ms/epoch - 154ms/step\n","Epoch 949/1000\n","5/5 - 1s - loss: 2.1787e-07 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9836 - 904ms/epoch - 181ms/step\n","Epoch 950/1000\n","5/5 - 1s - loss: 2.1717e-07 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9836 - 982ms/epoch - 196ms/step\n","Epoch 951/1000\n","5/5 - 1s - loss: 2.1635e-07 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9836 - 812ms/epoch - 162ms/step\n","Epoch 952/1000\n","5/5 - 1s - loss: 2.1558e-07 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9836 - 899ms/epoch - 180ms/step\n","Epoch 953/1000\n","5/5 - 1s - loss: 2.1487e-07 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9836 - 840ms/epoch - 168ms/step\n","Epoch 954/1000\n","5/5 - 1s - loss: 2.1410e-07 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9836 - 863ms/epoch - 173ms/step\n","Epoch 955/1000\n","5/5 - 1s - loss: 2.1336e-07 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9836 - 784ms/epoch - 157ms/step\n","Epoch 956/1000\n","5/5 - 1s - loss: 2.1263e-07 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9836 - 753ms/epoch - 151ms/step\n","Epoch 957/1000\n","5/5 - 1s - loss: 2.1191e-07 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9836 - 720ms/epoch - 144ms/step\n","Epoch 958/1000\n","5/5 - 1s - loss: 2.1115e-07 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9836 - 746ms/epoch - 149ms/step\n","Epoch 959/1000\n","5/5 - 1s - loss: 2.1038e-07 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9836 - 711ms/epoch - 142ms/step\n","Epoch 960/1000\n","5/5 - 1s - loss: 2.0967e-07 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9836 - 754ms/epoch - 151ms/step\n","Epoch 961/1000\n","5/5 - 1s - loss: 2.0891e-07 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 962/1000\n","5/5 - 1s - loss: 2.0820e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 913ms/epoch - 183ms/step\n","Epoch 963/1000\n","5/5 - 1s - loss: 2.0745e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 1s/epoch - 235ms/step\n","Epoch 964/1000\n","5/5 - 1s - loss: 2.0675e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 900ms/epoch - 180ms/step\n","Epoch 965/1000\n","5/5 - 1s - loss: 2.0604e-07 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9836 - 805ms/epoch - 161ms/step\n","Epoch 966/1000\n","5/5 - 1s - loss: 2.0529e-07 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9836 - 902ms/epoch - 180ms/step\n","Epoch 967/1000\n","5/5 - 1s - loss: 2.0458e-07 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9836 - 935ms/epoch - 187ms/step\n","Epoch 968/1000\n","5/5 - 1s - loss: 2.0383e-07 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9836 - 1s/epoch - 209ms/step\n","Epoch 969/1000\n","5/5 - 1s - loss: 2.0316e-07 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9836 - 934ms/epoch - 187ms/step\n","Epoch 970/1000\n","5/5 - 1s - loss: 2.0243e-07 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9836 - 1s/epoch - 201ms/step\n","Epoch 971/1000\n","5/5 - 1s - loss: 2.0173e-07 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9836 - 871ms/epoch - 174ms/step\n","Epoch 972/1000\n","5/5 - 1s - loss: 2.0106e-07 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9836 - 778ms/epoch - 156ms/step\n","Epoch 973/1000\n","5/5 - 1s - loss: 2.0033e-07 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9836 - 921ms/epoch - 184ms/step\n","Epoch 974/1000\n","5/5 - 1s - loss: 1.9963e-07 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9836 - 763ms/epoch - 153ms/step\n","Epoch 975/1000\n","5/5 - 1s - loss: 1.9891e-07 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 976/1000\n","5/5 - 1s - loss: 1.9827e-07 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9836 - 750ms/epoch - 150ms/step\n","Epoch 977/1000\n","5/5 - 1s - loss: 1.9755e-07 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9836 - 787ms/epoch - 157ms/step\n","Epoch 978/1000\n","5/5 - 1s - loss: 1.9685e-07 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9836 - 782ms/epoch - 156ms/step\n","Epoch 979/1000\n","5/5 - 1s - loss: 1.9620e-07 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9836 - 960ms/epoch - 192ms/step\n","Epoch 980/1000\n","5/5 - 1s - loss: 1.9551e-07 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9836 - 1s/epoch - 218ms/step\n","Epoch 981/1000\n","5/5 - 1s - loss: 1.9482e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 1s/epoch - 212ms/step\n","Epoch 982/1000\n","5/5 - 1s - loss: 1.9413e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 815ms/epoch - 163ms/step\n","Epoch 983/1000\n","5/5 - 1s - loss: 1.9345e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 754ms/epoch - 151ms/step\n","Epoch 984/1000\n","5/5 - 1s - loss: 1.9275e-07 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 985/1000\n","5/5 - 1s - loss: 1.9207e-07 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 986/1000\n","5/5 - 1s - loss: 1.9143e-07 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9836 - 680ms/epoch - 136ms/step\n","Epoch 987/1000\n","5/5 - 1s - loss: 1.9074e-07 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 988/1000\n","5/5 - 1s - loss: 1.9007e-07 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 989/1000\n","5/5 - 1s - loss: 1.8940e-07 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9836 - 802ms/epoch - 160ms/step\n","Epoch 990/1000\n","5/5 - 1s - loss: 1.8871e-07 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9836 - 761ms/epoch - 152ms/step\n","Epoch 991/1000\n","5/5 - 1s - loss: 1.8806e-07 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9836 - 990ms/epoch - 198ms/step\n","Epoch 992/1000\n","5/5 - 1s - loss: 1.8740e-07 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9836 - 934ms/epoch - 187ms/step\n","Epoch 993/1000\n","5/5 - 1s - loss: 1.8678e-07 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9836 - 823ms/epoch - 165ms/step\n","Epoch 994/1000\n","5/5 - 1s - loss: 1.8607e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 923ms/epoch - 185ms/step\n","Epoch 995/1000\n","5/5 - 1s - loss: 1.8543e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 783ms/epoch - 157ms/step\n","Epoch 996/1000\n","5/5 - 1s - loss: 1.8475e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 780ms/epoch - 156ms/step\n","Epoch 997/1000\n","5/5 - 1s - loss: 1.8412e-07 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9836 - 725ms/epoch - 145ms/step\n","Epoch 998/1000\n","5/5 - 1s - loss: 1.8344e-07 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9836 - 753ms/epoch - 151ms/step\n","Epoch 999/1000\n","5/5 - 1s - loss: 1.8287e-07 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9836 - 742ms/epoch - 148ms/step\n","Epoch 1000/1000\n","5/5 - 1s - loss: 1.8214e-07 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9836 - 765ms/epoch - 153ms/step\n"]}],"source":["LSTM_history = LSTM_model.fit(training_padded,\n"," training_labels,\n"," epochs=num_epochs,\n"," validation_data = (testing_padded, testing_labels),\n"," verbose=2, callbacks=[early_stop])"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Train GRU**"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"vaYQU0YGS_1F"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/1000\n","5/5 - 5s - loss: 2.8701e-06 - accuracy: 1.0000 - val_loss: 0.1698 - val_accuracy: 0.9836 - 5s/epoch - 1s/step\n","Epoch 2/1000\n","5/5 - 1s - loss: 2.8513e-06 - accuracy: 1.0000 - val_loss: 0.1699 - val_accuracy: 0.9836 - 635ms/epoch - 127ms/step\n","Epoch 3/1000\n","5/5 - 1s - loss: 2.8378e-06 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 4/1000\n","5/5 - 1s - loss: 2.8216e-06 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9836 - 635ms/epoch - 127ms/step\n","Epoch 5/1000\n","5/5 - 1s - loss: 2.8052e-06 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9836 - 697ms/epoch - 139ms/step\n","Epoch 6/1000\n","5/5 - 1s - loss: 2.7889e-06 - accuracy: 1.0000 - val_loss: 0.1703 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 7/1000\n","5/5 - 1s - loss: 2.7742e-06 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 8/1000\n","5/5 - 1s - loss: 2.7568e-06 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 9/1000\n","5/5 - 1s - loss: 2.7428e-06 - accuracy: 1.0000 - val_loss: 0.1706 - val_accuracy: 0.9836 - 736ms/epoch - 147ms/step\n","Epoch 10/1000\n","5/5 - 1s - loss: 2.7277e-06 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9836 - 759ms/epoch - 152ms/step\n","Epoch 11/1000\n","5/5 - 1s - loss: 2.7118e-06 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.9836 - 731ms/epoch - 146ms/step\n","Epoch 12/1000\n","5/5 - 1s - loss: 2.6965e-06 - accuracy: 1.0000 - val_loss: 0.1710 - val_accuracy: 0.9836 - 758ms/epoch - 152ms/step\n","Epoch 13/1000\n","5/5 - 1s - loss: 2.6809e-06 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9836 - 637ms/epoch - 127ms/step\n","Epoch 14/1000\n","5/5 - 1s - loss: 2.6660e-06 - accuracy: 1.0000 - val_loss: 0.1712 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 15/1000\n","5/5 - 1s - loss: 2.6511e-06 - accuracy: 1.0000 - val_loss: 0.1713 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 16/1000\n","5/5 - 1s - loss: 2.6373e-06 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 17/1000\n","5/5 - 1s - loss: 2.6224e-06 - accuracy: 1.0000 - val_loss: 0.1715 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 18/1000\n","5/5 - 1s - loss: 2.6074e-06 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 19/1000\n","5/5 - 1s - loss: 2.5923e-06 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9836 - 634ms/epoch - 127ms/step\n","Epoch 20/1000\n","5/5 - 1s - loss: 2.5782e-06 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 21/1000\n","5/5 - 1s - loss: 2.5645e-06 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 22/1000\n","5/5 - 1s - loss: 2.5508e-06 - accuracy: 1.0000 - val_loss: 0.1720 - val_accuracy: 0.9836 - 618ms/epoch - 124ms/step\n","Epoch 23/1000\n","5/5 - 1s - loss: 2.5375e-06 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 24/1000\n","5/5 - 1s - loss: 2.5228e-06 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 25/1000\n","5/5 - 1s - loss: 2.5096e-06 - accuracy: 1.0000 - val_loss: 0.1723 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 26/1000\n","5/5 - 1s - loss: 2.4961e-06 - accuracy: 1.0000 - val_loss: 0.1724 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 27/1000\n","5/5 - 1s - loss: 2.4822e-06 - accuracy: 1.0000 - val_loss: 0.1725 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 28/1000\n","5/5 - 1s - loss: 2.4706e-06 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 29/1000\n","5/5 - 1s - loss: 2.4561e-06 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 30/1000\n","5/5 - 1s - loss: 2.4437e-06 - accuracy: 1.0000 - val_loss: 0.1728 - val_accuracy: 0.9836 - 675ms/epoch - 135ms/step\n","Epoch 31/1000\n","5/5 - 1s - loss: 2.4312e-06 - accuracy: 1.0000 - val_loss: 0.1729 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 32/1000\n","5/5 - 1s - loss: 2.4173e-06 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9836 - 619ms/epoch - 124ms/step\n","Epoch 33/1000\n","5/5 - 1s - loss: 2.4048e-06 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 34/1000\n","5/5 - 1s - loss: 2.3921e-06 - accuracy: 1.0000 - val_loss: 0.1732 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 35/1000\n","5/5 - 1s - loss: 2.3797e-06 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9836 - 605ms/epoch - 121ms/step\n","Epoch 36/1000\n","5/5 - 1s - loss: 2.3686e-06 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 37/1000\n","5/5 - 1s - loss: 2.3550e-06 - accuracy: 1.0000 - val_loss: 0.1735 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 38/1000\n","5/5 - 1s - loss: 2.3426e-06 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 39/1000\n","5/5 - 1s - loss: 2.3308e-06 - accuracy: 1.0000 - val_loss: 0.1737 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 40/1000\n","5/5 - 1s - loss: 2.3200e-06 - accuracy: 1.0000 - val_loss: 0.1738 - val_accuracy: 0.9836 - 619ms/epoch - 124ms/step\n","Epoch 41/1000\n","5/5 - 1s - loss: 2.3050e-06 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 42/1000\n","5/5 - 1s - loss: 2.2940e-06 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 43/1000\n","5/5 - 1s - loss: 2.2837e-06 - accuracy: 1.0000 - val_loss: 0.1742 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 44/1000\n","5/5 - 1s - loss: 2.2716e-06 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 45/1000\n","5/5 - 1s - loss: 2.2580e-06 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 46/1000\n","5/5 - 1s - loss: 2.2468e-06 - accuracy: 1.0000 - val_loss: 0.1744 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 47/1000\n","5/5 - 1s - loss: 2.2370e-06 - accuracy: 1.0000 - val_loss: 0.1745 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 48/1000\n","5/5 - 1s - loss: 2.2256e-06 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 49/1000\n","5/5 - 1s - loss: 2.2133e-06 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 50/1000\n","5/5 - 1s - loss: 2.2033e-06 - accuracy: 1.0000 - val_loss: 0.1748 - val_accuracy: 0.9836 - 723ms/epoch - 145ms/step\n","Epoch 51/1000\n","5/5 - 1s - loss: 2.1909e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 52/1000\n","5/5 - 1s - loss: 2.1804e-06 - accuracy: 1.0000 - val_loss: 0.1750 - val_accuracy: 0.9836 - 629ms/epoch - 126ms/step\n","Epoch 53/1000\n","5/5 - 1s - loss: 2.1699e-06 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 54/1000\n","5/5 - 1s - loss: 2.1595e-06 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9836 - 875ms/epoch - 175ms/step\n","Epoch 55/1000\n","5/5 - 1s - loss: 2.1493e-06 - accuracy: 1.0000 - val_loss: 0.1753 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 56/1000\n","5/5 - 1s - loss: 2.1391e-06 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9836 - 818ms/epoch - 164ms/step\n","Epoch 57/1000\n","5/5 - 1s - loss: 2.1283e-06 - accuracy: 1.0000 - val_loss: 0.1755 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 58/1000\n","5/5 - 1s - loss: 2.1178e-06 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9836 - 827ms/epoch - 165ms/step\n","Epoch 59/1000\n","5/5 - 1s - loss: 2.1076e-06 - accuracy: 1.0000 - val_loss: 0.1757 - val_accuracy: 0.9836 - 803ms/epoch - 161ms/step\n","Epoch 60/1000\n","5/5 - 1s - loss: 2.0974e-06 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 61/1000\n","5/5 - 1s - loss: 2.0872e-06 - accuracy: 1.0000 - val_loss: 0.1759 - val_accuracy: 0.9836 - 597ms/epoch - 119ms/step\n","Epoch 62/1000\n","5/5 - 1s - loss: 2.0771e-06 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9836 - 727ms/epoch - 145ms/step\n","Epoch 63/1000\n","5/5 - 1s - loss: 2.0671e-06 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 64/1000\n","5/5 - 1s - loss: 2.0565e-06 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9836 - 645ms/epoch - 129ms/step\n","Epoch 65/1000\n","5/5 - 1s - loss: 2.0458e-06 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 66/1000\n","5/5 - 1s - loss: 2.0377e-06 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 67/1000\n","5/5 - 1s - loss: 2.0261e-06 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 68/1000\n","5/5 - 1s - loss: 2.0169e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 69/1000\n","5/5 - 1s - loss: 2.0078e-06 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 70/1000\n","5/5 - 1s - loss: 1.9975e-06 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 71/1000\n","5/5 - 1s - loss: 1.9884e-06 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 72/1000\n","5/5 - 1s - loss: 1.9784e-06 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 73/1000\n","5/5 - 1s - loss: 1.9703e-06 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9836 - 634ms/epoch - 127ms/step\n","Epoch 74/1000\n","5/5 - 1s - loss: 1.9605e-06 - accuracy: 1.0000 - val_loss: 0.1771 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 75/1000\n","5/5 - 1s - loss: 1.9506e-06 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 76/1000\n","5/5 - 1s - loss: 1.9430e-06 - accuracy: 1.0000 - val_loss: 0.1773 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 77/1000\n","5/5 - 1s - loss: 1.9331e-06 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 78/1000\n","5/5 - 1s - loss: 1.9236e-06 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9836 - 541ms/epoch - 108ms/step\n","Epoch 79/1000\n","5/5 - 1s - loss: 1.9152e-06 - accuracy: 1.0000 - val_loss: 0.1775 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 80/1000\n","5/5 - 1s - loss: 1.9058e-06 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9836 - 540ms/epoch - 108ms/step\n","Epoch 81/1000\n","5/5 - 1s - loss: 1.8972e-06 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 82/1000\n","5/5 - 1s - loss: 1.8879e-06 - accuracy: 1.0000 - val_loss: 0.1778 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 83/1000\n","5/5 - 1s - loss: 1.8801e-06 - accuracy: 1.0000 - val_loss: 0.1779 - val_accuracy: 0.9836 - 736ms/epoch - 147ms/step\n","Epoch 84/1000\n","5/5 - 1s - loss: 1.8704e-06 - accuracy: 1.0000 - val_loss: 0.1780 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 85/1000\n","5/5 - 1s - loss: 1.8624e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 86/1000\n","5/5 - 1s - loss: 1.8546e-06 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 87/1000\n","5/5 - 1s - loss: 1.8453e-06 - accuracy: 1.0000 - val_loss: 0.1783 - val_accuracy: 0.9836 - 642ms/epoch - 128ms/step\n","Epoch 88/1000\n","5/5 - 1s - loss: 1.8371e-06 - accuracy: 1.0000 - val_loss: 0.1783 - val_accuracy: 0.9836 - 693ms/epoch - 139ms/step\n","Epoch 89/1000\n","5/5 - 1s - loss: 1.8290e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 90/1000\n","5/5 - 1s - loss: 1.8199e-06 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 91/1000\n","5/5 - 1s - loss: 1.8126e-06 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9836 - 632ms/epoch - 126ms/step\n","Epoch 92/1000\n","5/5 - 1s - loss: 1.8031e-06 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9836 - 717ms/epoch - 143ms/step\n","Epoch 93/1000\n","5/5 - 1s - loss: 1.7955e-06 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 94/1000\n","5/5 - 1s - loss: 1.7871e-06 - accuracy: 1.0000 - val_loss: 0.1789 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 95/1000\n","5/5 - 1s - loss: 1.7789e-06 - accuracy: 1.0000 - val_loss: 0.1790 - val_accuracy: 0.9836 - 777ms/epoch - 155ms/step\n","Epoch 96/1000\n","5/5 - 1s - loss: 1.7705e-06 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9836 - 652ms/epoch - 130ms/step\n","Epoch 97/1000\n","5/5 - 1s - loss: 1.7630e-06 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9836 - 878ms/epoch - 176ms/step\n","Epoch 98/1000\n","5/5 - 1s - loss: 1.7546e-06 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 99/1000\n","5/5 - 1s - loss: 1.7470e-06 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9836 - 633ms/epoch - 127ms/step\n","Epoch 100/1000\n","5/5 - 1s - loss: 1.7391e-06 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 101/1000\n","5/5 - 1s - loss: 1.7311e-06 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 102/1000\n","5/5 - 1s - loss: 1.7232e-06 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 103/1000\n","5/5 - 1s - loss: 1.7156e-06 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 104/1000\n","5/5 - 1s - loss: 1.7083e-06 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 105/1000\n","5/5 - 1s - loss: 1.7008e-06 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 106/1000\n","5/5 - 1s - loss: 1.6926e-06 - accuracy: 1.0000 - val_loss: 0.1799 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 107/1000\n","5/5 - 1s - loss: 1.6857e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9836 - 715ms/epoch - 143ms/step\n","Epoch 108/1000\n","5/5 - 1s - loss: 1.6777e-06 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 109/1000\n","5/5 - 1s - loss: 1.6705e-06 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9836 - 692ms/epoch - 138ms/step\n","Epoch 110/1000\n","5/5 - 1s - loss: 1.6632e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9836 - 680ms/epoch - 136ms/step\n","Epoch 111/1000\n","5/5 - 1s - loss: 1.6553e-06 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9836 - 705ms/epoch - 141ms/step\n","Epoch 112/1000\n","5/5 - 1s - loss: 1.6486e-06 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9836 - 635ms/epoch - 127ms/step\n","Epoch 113/1000\n","5/5 - 1s - loss: 1.6414e-06 - accuracy: 1.0000 - val_loss: 0.1805 - val_accuracy: 0.9836 - 906ms/epoch - 181ms/step\n","Epoch 114/1000\n","5/5 - 1s - loss: 1.6340e-06 - accuracy: 1.0000 - val_loss: 0.1806 - val_accuracy: 0.9836 - 803ms/epoch - 161ms/step\n","Epoch 115/1000\n","5/5 - 1s - loss: 1.6270e-06 - accuracy: 1.0000 - val_loss: 0.1807 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 116/1000\n","5/5 - 1s - loss: 1.6198e-06 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9836 - 696ms/epoch - 139ms/step\n","Epoch 117/1000\n","5/5 - 1s - loss: 1.6130e-06 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 118/1000\n","5/5 - 1s - loss: 1.6060e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 119/1000\n","5/5 - 1s - loss: 1.5992e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9836 - 540ms/epoch - 108ms/step\n","Epoch 120/1000\n","5/5 - 1s - loss: 1.5919e-06 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9836 - 1s/epoch - 299ms/step\n","Epoch 121/1000\n","5/5 - 1s - loss: 1.5852e-06 - accuracy: 1.0000 - val_loss: 0.1812 - val_accuracy: 0.9836 - 815ms/epoch - 163ms/step\n","Epoch 122/1000\n","5/5 - 1s - loss: 1.5785e-06 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9836 - 817ms/epoch - 163ms/step\n","Epoch 123/1000\n","5/5 - 1s - loss: 1.5716e-06 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9836 - 759ms/epoch - 152ms/step\n","Epoch 124/1000\n","5/5 - 1s - loss: 1.5650e-06 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9836 - 658ms/epoch - 132ms/step\n","Epoch 125/1000\n","5/5 - 1s - loss: 1.5585e-06 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 126/1000\n","5/5 - 1s - loss: 1.5518e-06 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 127/1000\n","5/5 - 1s - loss: 1.5455e-06 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 128/1000\n","5/5 - 1s - loss: 1.5383e-06 - accuracy: 1.0000 - val_loss: 0.1818 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 129/1000\n","5/5 - 1s - loss: 1.5319e-06 - accuracy: 1.0000 - val_loss: 0.1819 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 130/1000\n","5/5 - 1s - loss: 1.5261e-06 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9836 - 609ms/epoch - 122ms/step\n","Epoch 131/1000\n","5/5 - 1s - loss: 1.5195e-06 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 132/1000\n","5/5 - 1s - loss: 1.5132e-06 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 133/1000\n","5/5 - 1s - loss: 1.5067e-06 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9836 - 609ms/epoch - 122ms/step\n","Epoch 134/1000\n","5/5 - 1s - loss: 1.5005e-06 - accuracy: 1.0000 - val_loss: 0.1823 - val_accuracy: 0.9836 - 697ms/epoch - 139ms/step\n","Epoch 135/1000\n","5/5 - 1s - loss: 1.4943e-06 - accuracy: 1.0000 - val_loss: 0.1824 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 136/1000\n","5/5 - 1s - loss: 1.4882e-06 - accuracy: 1.0000 - val_loss: 0.1824 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 137/1000\n","5/5 - 1s - loss: 1.4820e-06 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 138/1000\n","5/5 - 1s - loss: 1.4761e-06 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 139/1000\n","5/5 - 1s - loss: 1.4701e-06 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9836 - 616ms/epoch - 123ms/step\n","Epoch 140/1000\n","5/5 - 1s - loss: 1.4638e-06 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 141/1000\n","5/5 - 1s - loss: 1.4577e-06 - accuracy: 1.0000 - val_loss: 0.1828 - val_accuracy: 0.9836 - 616ms/epoch - 123ms/step\n","Epoch 142/1000\n","5/5 - 1s - loss: 1.4519e-06 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 143/1000\n","5/5 - 1s - loss: 1.4462e-06 - accuracy: 1.0000 - val_loss: 0.1830 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 144/1000\n","5/5 - 1s - loss: 1.4401e-06 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9836 - 618ms/epoch - 124ms/step\n","Epoch 145/1000\n","5/5 - 1s - loss: 1.4342e-06 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 146/1000\n","5/5 - 1s - loss: 1.4286e-06 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 147/1000\n","5/5 - 1s - loss: 1.4223e-06 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 148/1000\n","5/5 - 1s - loss: 1.4162e-06 - accuracy: 1.0000 - val_loss: 0.1834 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 149/1000\n","5/5 - 1s - loss: 1.4109e-06 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9836 - 715ms/epoch - 143ms/step\n","Epoch 150/1000\n","5/5 - 1s - loss: 1.4055e-06 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9836 - 604ms/epoch - 121ms/step\n","Epoch 151/1000\n","5/5 - 1s - loss: 1.3991e-06 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9836 - 631ms/epoch - 126ms/step\n","Epoch 152/1000\n","5/5 - 1s - loss: 1.3938e-06 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 153/1000\n","5/5 - 1s - loss: 1.3885e-06 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 154/1000\n","5/5 - 1s - loss: 1.3819e-06 - accuracy: 1.0000 - val_loss: 0.1839 - val_accuracy: 0.9836 - 693ms/epoch - 139ms/step\n","Epoch 155/1000\n","5/5 - 1s - loss: 1.3765e-06 - accuracy: 1.0000 - val_loss: 0.1839 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 156/1000\n","5/5 - 1s - loss: 1.3715e-06 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9836 - 698ms/epoch - 140ms/step\n","Epoch 157/1000\n","5/5 - 1s - loss: 1.3655e-06 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9836 - 738ms/epoch - 148ms/step\n","Epoch 158/1000\n","5/5 - 1s - loss: 1.3599e-06 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9836 - 715ms/epoch - 143ms/step\n","Epoch 159/1000\n","5/5 - 1s - loss: 1.3548e-06 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9836 - 1s/epoch - 227ms/step\n","Epoch 160/1000\n","5/5 - 1s - loss: 1.3493e-06 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9836 - 962ms/epoch - 192ms/step\n","Epoch 161/1000\n","5/5 - 1s - loss: 1.3435e-06 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 162/1000\n","5/5 - 1s - loss: 1.3389e-06 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9836 - 683ms/epoch - 137ms/step\n","Epoch 163/1000\n","5/5 - 1s - loss: 1.3331e-06 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9836 - 820ms/epoch - 164ms/step\n","Epoch 164/1000\n","5/5 - 1s - loss: 1.3276e-06 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9836 - 859ms/epoch - 172ms/step\n","Epoch 165/1000\n","5/5 - 1s - loss: 1.3228e-06 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9836 - 729ms/epoch - 146ms/step\n","Epoch 166/1000\n","5/5 - 1s - loss: 1.3171e-06 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9836 - 786ms/epoch - 157ms/step\n","Epoch 167/1000\n","5/5 - 1s - loss: 1.3119e-06 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9836 - 911ms/epoch - 182ms/step\n","Epoch 168/1000\n","5/5 - 1s - loss: 1.3068e-06 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9836 - 809ms/epoch - 162ms/step\n","Epoch 169/1000\n","5/5 - 1s - loss: 1.3018e-06 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9836 - 1s/epoch - 203ms/step\n","Epoch 170/1000\n","5/5 - 1s - loss: 1.2967e-06 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9836 - 778ms/epoch - 156ms/step\n","Epoch 171/1000\n","5/5 - 1s - loss: 1.2921e-06 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9836 - 812ms/epoch - 162ms/step\n","Epoch 172/1000\n","5/5 - 1s - loss: 1.2864e-06 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9836 - 968ms/epoch - 194ms/step\n","Epoch 173/1000\n","5/5 - 1s - loss: 1.2814e-06 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9836 - 901ms/epoch - 180ms/step\n","Epoch 174/1000\n","5/5 - 1s - loss: 1.2771e-06 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9836 - 879ms/epoch - 176ms/step\n","Epoch 175/1000\n","5/5 - 1s - loss: 1.2715e-06 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 176/1000\n","5/5 - 1s - loss: 1.2670e-06 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9836 - 802ms/epoch - 160ms/step\n","Epoch 177/1000\n","5/5 - 1s - loss: 1.2615e-06 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9836 - 602ms/epoch - 120ms/step\n","Epoch 178/1000\n","5/5 - 1s - loss: 1.2571e-06 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9836 - 928ms/epoch - 186ms/step\n","Epoch 179/1000\n","5/5 - 1s - loss: 1.2525e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9836 - 795ms/epoch - 159ms/step\n","Epoch 180/1000\n","5/5 - 1s - loss: 1.2468e-06 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9836 - 804ms/epoch - 161ms/step\n","Epoch 181/1000\n","5/5 - 1s - loss: 1.2426e-06 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9836 - 747ms/epoch - 149ms/step\n","Epoch 182/1000\n","5/5 - 1s - loss: 1.2377e-06 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 0.9836 - 879ms/epoch - 176ms/step\n","Epoch 183/1000\n","5/5 - 1s - loss: 1.2328e-06 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 184/1000\n","5/5 - 1s - loss: 1.2281e-06 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9836 - 835ms/epoch - 167ms/step\n","Epoch 185/1000\n","5/5 - 1s - loss: 1.2234e-06 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9836 - 888ms/epoch - 178ms/step\n","Epoch 186/1000\n","5/5 - 1s - loss: 1.2187e-06 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9836 - 884ms/epoch - 177ms/step\n","Epoch 187/1000\n","5/5 - 1s - loss: 1.2141e-06 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9836 - 825ms/epoch - 165ms/step\n","Epoch 188/1000\n","5/5 - 1s - loss: 1.2096e-06 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9836 - 763ms/epoch - 153ms/step\n","Epoch 189/1000\n","5/5 - 1s - loss: 1.2048e-06 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9836 - 723ms/epoch - 145ms/step\n","Epoch 190/1000\n","5/5 - 1s - loss: 1.2001e-06 - accuracy: 1.0000 - val_loss: 0.1866 - val_accuracy: 0.9836 - 683ms/epoch - 137ms/step\n","Epoch 191/1000\n","5/5 - 1s - loss: 1.1953e-06 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9836 - 854ms/epoch - 171ms/step\n","Epoch 192/1000\n","5/5 - 1s - loss: 1.1911e-06 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 193/1000\n","5/5 - 1s - loss: 1.1861e-06 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 194/1000\n","5/5 - 1s - loss: 1.1820e-06 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9836 - 696ms/epoch - 139ms/step\n","Epoch 195/1000\n","5/5 - 1s - loss: 1.1773e-06 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9836 - 767ms/epoch - 153ms/step\n","Epoch 196/1000\n","5/5 - 1s - loss: 1.1728e-06 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 197/1000\n","5/5 - 1s - loss: 1.1682e-06 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9836 - 1s/epoch - 214ms/step\n","Epoch 198/1000\n","5/5 - 1s - loss: 1.1638e-06 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9836 - 768ms/epoch - 154ms/step\n","Epoch 199/1000\n","5/5 - 1s - loss: 1.1597e-06 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 200/1000\n","5/5 - 1s - loss: 1.1552e-06 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 201/1000\n","5/5 - 1s - loss: 1.1508e-06 - accuracy: 1.0000 - val_loss: 0.1874 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 202/1000\n","5/5 - 1s - loss: 1.1463e-06 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9836 - 794ms/epoch - 159ms/step\n","Epoch 203/1000\n","5/5 - 1s - loss: 1.1421e-06 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9836 - 908ms/epoch - 182ms/step\n","Epoch 204/1000\n","5/5 - 1s - loss: 1.1381e-06 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 205/1000\n","5/5 - 1s - loss: 1.1333e-06 - accuracy: 1.0000 - val_loss: 0.1877 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 206/1000\n","5/5 - 1s - loss: 1.1293e-06 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9836 - 610ms/epoch - 122ms/step\n","Epoch 207/1000\n","5/5 - 1s - loss: 1.1248e-06 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9836 - 977ms/epoch - 195ms/step\n","Epoch 208/1000\n","5/5 - 1s - loss: 1.1208e-06 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9836 - 830ms/epoch - 166ms/step\n","Epoch 209/1000\n","5/5 - 1s - loss: 1.1168e-06 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 210/1000\n","5/5 - 1s - loss: 1.1121e-06 - accuracy: 1.0000 - val_loss: 0.1881 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 211/1000\n","5/5 - 1s - loss: 1.1083e-06 - accuracy: 1.0000 - val_loss: 0.1881 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 212/1000\n","5/5 - 1s - loss: 1.1039e-06 - accuracy: 1.0000 - val_loss: 0.1882 - val_accuracy: 0.9836 - 627ms/epoch - 125ms/step\n","Epoch 213/1000\n","5/5 - 1s - loss: 1.1001e-06 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9836 - 619ms/epoch - 124ms/step\n","Epoch 214/1000\n","5/5 - 1s - loss: 1.0955e-06 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9836 - 538ms/epoch - 108ms/step\n","Epoch 215/1000\n","5/5 - 1s - loss: 1.0916e-06 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9836 - 505ms/epoch - 101ms/step\n","Epoch 216/1000\n","5/5 - 0s - loss: 1.0877e-06 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9836 - 493ms/epoch - 99ms/step\n","Epoch 217/1000\n","5/5 - 1s - loss: 1.0835e-06 - accuracy: 1.0000 - val_loss: 0.1886 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 218/1000\n","5/5 - 1s - loss: 1.0796e-06 - accuracy: 1.0000 - val_loss: 0.1886 - val_accuracy: 0.9836 - 723ms/epoch - 145ms/step\n","Epoch 219/1000\n","5/5 - 1s - loss: 1.0755e-06 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 220/1000\n","5/5 - 1s - loss: 1.0717e-06 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9836 - 637ms/epoch - 127ms/step\n","Epoch 221/1000\n","5/5 - 1s - loss: 1.0675e-06 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9836 - 629ms/epoch - 126ms/step\n","Epoch 222/1000\n","5/5 - 1s - loss: 1.0636e-06 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9836 - 675ms/epoch - 135ms/step\n","Epoch 223/1000\n","5/5 - 1s - loss: 1.0599e-06 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9836 - 605ms/epoch - 121ms/step\n","Epoch 224/1000\n","5/5 - 1s - loss: 1.0560e-06 - accuracy: 1.0000 - val_loss: 0.1891 - val_accuracy: 0.9836 - 528ms/epoch - 106ms/step\n","Epoch 225/1000\n","5/5 - 0s - loss: 1.0519e-06 - accuracy: 1.0000 - val_loss: 0.1891 - val_accuracy: 0.9836 - 494ms/epoch - 99ms/step\n","Epoch 226/1000\n","5/5 - 1s - loss: 1.0481e-06 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9836 - 531ms/epoch - 106ms/step\n","Epoch 227/1000\n","5/5 - 1s - loss: 1.0440e-06 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9836 - 534ms/epoch - 107ms/step\n","Epoch 228/1000\n","5/5 - 1s - loss: 1.0405e-06 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 229/1000\n","5/5 - 1s - loss: 1.0368e-06 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9836 - 798ms/epoch - 160ms/step\n","Epoch 230/1000\n","5/5 - 1s - loss: 1.0330e-06 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 231/1000\n","5/5 - 1s - loss: 1.0289e-06 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 232/1000\n","5/5 - 1s - loss: 1.0253e-06 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9836 - 576ms/epoch - 115ms/step\n","Epoch 233/1000\n","5/5 - 1s - loss: 1.0214e-06 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 234/1000\n","5/5 - 1s - loss: 1.0177e-06 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 235/1000\n","5/5 - 1s - loss: 1.0139e-06 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9836 - 509ms/epoch - 102ms/step\n","Epoch 236/1000\n","5/5 - 1s - loss: 1.0102e-06 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9836 - 501ms/epoch - 100ms/step\n","Epoch 237/1000\n","5/5 - 1s - loss: 1.0067e-06 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9836 - 516ms/epoch - 103ms/step\n","Epoch 238/1000\n","5/5 - 1s - loss: 1.0029e-06 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 239/1000\n","5/5 - 1s - loss: 9.9944e-07 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9836 - 757ms/epoch - 151ms/step\n","Epoch 240/1000\n","5/5 - 1s - loss: 9.9558e-07 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9836 - 707ms/epoch - 141ms/step\n","Epoch 241/1000\n","5/5 - 1s - loss: 9.9233e-07 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9836 - 777ms/epoch - 155ms/step\n","Epoch 242/1000\n","5/5 - 1s - loss: 9.8842e-07 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9836 - 811ms/epoch - 162ms/step\n","Epoch 243/1000\n","5/5 - 1s - loss: 9.8482e-07 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9836 - 672ms/epoch - 134ms/step\n","Epoch 244/1000\n","5/5 - 1s - loss: 9.8121e-07 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9836 - 806ms/epoch - 161ms/step\n","Epoch 245/1000\n","5/5 - 1s - loss: 9.7800e-07 - accuracy: 1.0000 - val_loss: 0.1905 - val_accuracy: 0.9836 - 930ms/epoch - 186ms/step\n","Epoch 246/1000\n","5/5 - 1s - loss: 9.7449e-07 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9836 - 858ms/epoch - 172ms/step\n","Epoch 247/1000\n","5/5 - 1s - loss: 9.7071e-07 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9836 - 845ms/epoch - 169ms/step\n","Epoch 248/1000\n","5/5 - 1s - loss: 9.6742e-07 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9836 - 675ms/epoch - 135ms/step\n","Epoch 249/1000\n","5/5 - 1s - loss: 9.6408e-07 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9836 - 719ms/epoch - 144ms/step\n","Epoch 250/1000\n","5/5 - 1s - loss: 9.6067e-07 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9836 - 785ms/epoch - 157ms/step\n","Epoch 251/1000\n","5/5 - 1s - loss: 9.5704e-07 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9836 - 800ms/epoch - 160ms/step\n","Epoch 252/1000\n","5/5 - 1s - loss: 9.5389e-07 - accuracy: 1.0000 - val_loss: 0.1910 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 253/1000\n","5/5 - 1s - loss: 9.5042e-07 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 254/1000\n","5/5 - 1s - loss: 9.4690e-07 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9836 - 834ms/epoch - 167ms/step\n","Epoch 255/1000\n","5/5 - 1s - loss: 9.4384e-07 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9836 - 628ms/epoch - 126ms/step\n","Epoch 256/1000\n","5/5 - 1s - loss: 9.4043e-07 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9836 - 673ms/epoch - 135ms/step\n","Epoch 257/1000\n","5/5 - 1s - loss: 9.3708e-07 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 258/1000\n","5/5 - 1s - loss: 9.3366e-07 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 259/1000\n","5/5 - 1s - loss: 9.3052e-07 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 260/1000\n","5/5 - 1s - loss: 9.2705e-07 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9836 - 602ms/epoch - 120ms/step\n","Epoch 261/1000\n","5/5 - 1s - loss: 9.2411e-07 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 262/1000\n","5/5 - 1s - loss: 9.2082e-07 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 263/1000\n","5/5 - 1s - loss: 9.1749e-07 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9836 - 742ms/epoch - 148ms/step\n","Epoch 264/1000\n","5/5 - 1s - loss: 9.1436e-07 - accuracy: 1.0000 - val_loss: 0.1918 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 265/1000\n","5/5 - 1s - loss: 9.1111e-07 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 266/1000\n","5/5 - 1s - loss: 9.0804e-07 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.9836 - 741ms/epoch - 148ms/step\n","Epoch 267/1000\n","5/5 - 1s - loss: 9.0466e-07 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9836 - 637ms/epoch - 127ms/step\n","Epoch 268/1000\n","5/5 - 1s - loss: 9.0169e-07 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 269/1000\n","5/5 - 1s - loss: 8.9854e-07 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9836 - 896ms/epoch - 179ms/step\n","Epoch 270/1000\n","5/5 - 1s - loss: 8.9515e-07 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9836 - 740ms/epoch - 148ms/step\n","Epoch 271/1000\n","5/5 - 1s - loss: 8.9216e-07 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9836 - 797ms/epoch - 159ms/step\n","Epoch 272/1000\n","5/5 - 1s - loss: 8.8903e-07 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9836 - 707ms/epoch - 141ms/step\n","Epoch 273/1000\n","5/5 - 1s - loss: 8.8599e-07 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 274/1000\n","5/5 - 1s - loss: 8.8278e-07 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9836 - 790ms/epoch - 158ms/step\n","Epoch 275/1000\n","5/5 - 1s - loss: 8.7973e-07 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9836 - 862ms/epoch - 172ms/step\n","Epoch 276/1000\n","5/5 - 1s - loss: 8.7681e-07 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9836 - 712ms/epoch - 142ms/step\n","Epoch 277/1000\n","5/5 - 1s - loss: 8.7382e-07 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9836 - 658ms/epoch - 132ms/step\n","Epoch 278/1000\n","5/5 - 1s - loss: 8.7068e-07 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 279/1000\n","5/5 - 1s - loss: 8.6768e-07 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 280/1000\n","5/5 - 1s - loss: 8.6470e-07 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 281/1000\n","5/5 - 1s - loss: 8.6177e-07 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 282/1000\n","5/5 - 1s - loss: 8.5856e-07 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9836 - 606ms/epoch - 121ms/step\n","Epoch 283/1000\n","5/5 - 1s - loss: 8.5559e-07 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9836 - 820ms/epoch - 164ms/step\n","Epoch 284/1000\n","5/5 - 1s - loss: 8.5301e-07 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9836 - 847ms/epoch - 169ms/step\n","Epoch 285/1000\n","5/5 - 1s - loss: 8.4973e-07 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9836 - 706ms/epoch - 141ms/step\n","Epoch 286/1000\n","5/5 - 1s - loss: 8.4729e-07 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9836 - 767ms/epoch - 153ms/step\n","Epoch 287/1000\n","5/5 - 1s - loss: 8.4383e-07 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9836 - 784ms/epoch - 157ms/step\n","Epoch 288/1000\n","5/5 - 1s - loss: 8.4103e-07 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9836 - 665ms/epoch - 133ms/step\n","Epoch 289/1000\n","5/5 - 1s - loss: 8.3826e-07 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 290/1000\n","5/5 - 1s - loss: 8.3526e-07 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 291/1000\n","5/5 - 1s - loss: 8.3235e-07 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 292/1000\n","5/5 - 1s - loss: 8.2978e-07 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 293/1000\n","5/5 - 1s - loss: 8.2657e-07 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 294/1000\n","5/5 - 1s - loss: 8.2386e-07 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 295/1000\n","5/5 - 1s - loss: 8.2107e-07 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9836 - 727ms/epoch - 145ms/step\n","Epoch 296/1000\n","5/5 - 1s - loss: 8.1831e-07 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 297/1000\n","5/5 - 1s - loss: 8.1530e-07 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9836 - 760ms/epoch - 152ms/step\n","Epoch 298/1000\n","5/5 - 1s - loss: 8.1257e-07 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9836 - 721ms/epoch - 144ms/step\n","Epoch 299/1000\n","5/5 - 1s - loss: 8.0985e-07 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9836 - 860ms/epoch - 172ms/step\n","Epoch 300/1000\n","5/5 - 1s - loss: 8.0721e-07 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9836 - 725ms/epoch - 145ms/step\n","Epoch 301/1000\n","5/5 - 1s - loss: 8.0445e-07 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9836 - 754ms/epoch - 151ms/step\n","Epoch 302/1000\n","5/5 - 1s - loss: 8.0165e-07 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 303/1000\n","5/5 - 1s - loss: 7.9897e-07 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9836 - 515ms/epoch - 103ms/step\n","Epoch 304/1000\n","5/5 - 1s - loss: 7.9616e-07 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9836 - 961ms/epoch - 192ms/step\n","Epoch 305/1000\n","5/5 - 1s - loss: 7.9332e-07 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9836 - 998ms/epoch - 200ms/step\n","Epoch 306/1000\n","5/5 - 1s - loss: 7.9107e-07 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9836 - 690ms/epoch - 138ms/step\n","Epoch 307/1000\n","5/5 - 1s - loss: 7.8801e-07 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9836 - 703ms/epoch - 141ms/step\n","Epoch 308/1000\n","5/5 - 1s - loss: 7.8573e-07 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9836 - 876ms/epoch - 175ms/step\n","Epoch 309/1000\n","5/5 - 1s - loss: 7.8271e-07 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9836 - 873ms/epoch - 175ms/step\n","Epoch 310/1000\n","5/5 - 1s - loss: 7.8007e-07 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 311/1000\n","5/5 - 1s - loss: 7.7738e-07 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9836 - 812ms/epoch - 162ms/step\n","Epoch 312/1000\n","5/5 - 1s - loss: 7.7476e-07 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9836 - 761ms/epoch - 152ms/step\n","Epoch 313/1000\n","5/5 - 1s - loss: 7.7242e-07 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9836 - 854ms/epoch - 171ms/step\n","Epoch 314/1000\n","5/5 - 1s - loss: 7.6950e-07 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9836 - 838ms/epoch - 168ms/step\n","Epoch 315/1000\n","5/5 - 1s - loss: 7.6689e-07 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9836 - 740ms/epoch - 148ms/step\n","Epoch 316/1000\n","5/5 - 1s - loss: 7.6419e-07 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9836 - 776ms/epoch - 155ms/step\n","Epoch 317/1000\n","5/5 - 1s - loss: 7.6188e-07 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 318/1000\n","5/5 - 1s - loss: 7.5921e-07 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9836 - 849ms/epoch - 170ms/step\n","Epoch 319/1000\n","5/5 - 1s - loss: 7.5668e-07 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9836 - 806ms/epoch - 161ms/step\n","Epoch 320/1000\n","5/5 - 1s - loss: 7.5406e-07 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9836 - 743ms/epoch - 149ms/step\n","Epoch 321/1000\n","5/5 - 1s - loss: 7.5153e-07 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9836 - 755ms/epoch - 151ms/step\n","Epoch 322/1000\n","5/5 - 1s - loss: 7.4903e-07 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9836 - 845ms/epoch - 169ms/step\n","Epoch 323/1000\n","5/5 - 1s - loss: 7.4630e-07 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 324/1000\n","5/5 - 1s - loss: 7.4408e-07 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9836 - 664ms/epoch - 133ms/step\n","Epoch 325/1000\n","5/5 - 1s - loss: 7.4153e-07 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 326/1000\n","5/5 - 1s - loss: 7.3902e-07 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 327/1000\n","5/5 - 1s - loss: 7.3664e-07 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9836 - 650ms/epoch - 130ms/step\n","Epoch 328/1000\n","5/5 - 1s - loss: 7.3413e-07 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9836 - 645ms/epoch - 129ms/step\n","Epoch 329/1000\n","5/5 - 1s - loss: 7.3155e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 330/1000\n","5/5 - 1s - loss: 7.2908e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 331/1000\n","5/5 - 1s - loss: 7.2670e-07 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 332/1000\n","5/5 - 1s - loss: 7.2410e-07 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 333/1000\n","5/5 - 1s - loss: 7.2189e-07 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9836 - 792ms/epoch - 158ms/step\n","Epoch 334/1000\n","5/5 - 1s - loss: 7.1940e-07 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 335/1000\n","5/5 - 1s - loss: 7.1702e-07 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 336/1000\n","5/5 - 1s - loss: 7.1456e-07 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9836 - 982ms/epoch - 196ms/step\n","Epoch 337/1000\n","5/5 - 1s - loss: 7.1231e-07 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9836 - 936ms/epoch - 187ms/step\n","Epoch 338/1000\n","5/5 - 1s - loss: 7.1003e-07 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9836 - 982ms/epoch - 196ms/step\n","Epoch 339/1000\n","5/5 - 1s - loss: 7.0750e-07 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9836 - 857ms/epoch - 171ms/step\n","Epoch 340/1000\n","5/5 - 1s - loss: 7.0529e-07 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 341/1000\n","5/5 - 1s - loss: 7.0283e-07 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9836 - 847ms/epoch - 169ms/step\n","Epoch 342/1000\n","5/5 - 1s - loss: 7.0063e-07 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9836 - 819ms/epoch - 164ms/step\n","Epoch 343/1000\n","5/5 - 1s - loss: 6.9828e-07 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9836 - 971ms/epoch - 194ms/step\n","Epoch 344/1000\n","5/5 - 1s - loss: 6.9588e-07 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 345/1000\n","5/5 - 1s - loss: 6.9354e-07 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9836 - 818ms/epoch - 164ms/step\n","Epoch 346/1000\n","5/5 - 1s - loss: 6.9136e-07 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 347/1000\n","5/5 - 1s - loss: 6.8910e-07 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 348/1000\n","5/5 - 1s - loss: 6.8676e-07 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 349/1000\n","5/5 - 1s - loss: 6.8451e-07 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9836 - 631ms/epoch - 126ms/step\n","Epoch 350/1000\n","5/5 - 1s - loss: 6.8236e-07 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9836 - 681ms/epoch - 136ms/step\n","Epoch 351/1000\n","5/5 - 1s - loss: 6.8007e-07 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 352/1000\n","5/5 - 1s - loss: 6.7797e-07 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9836 - 681ms/epoch - 136ms/step\n","Epoch 353/1000\n","5/5 - 1s - loss: 6.7565e-07 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9836 - 897ms/epoch - 179ms/step\n","Epoch 354/1000\n","5/5 - 1s - loss: 6.7359e-07 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9836 - 876ms/epoch - 175ms/step\n","Epoch 355/1000\n","5/5 - 1s - loss: 6.7132e-07 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9836 - 759ms/epoch - 152ms/step\n","Epoch 356/1000\n","5/5 - 1s - loss: 6.6922e-07 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9836 - 842ms/epoch - 168ms/step\n","Epoch 357/1000\n","5/5 - 1s - loss: 6.6705e-07 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 358/1000\n","5/5 - 1s - loss: 6.6491e-07 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9836 - 710ms/epoch - 142ms/step\n","Epoch 359/1000\n","5/5 - 1s - loss: 6.6266e-07 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9836 - 742ms/epoch - 148ms/step\n","Epoch 360/1000\n","5/5 - 1s - loss: 6.6053e-07 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9836 - 795ms/epoch - 159ms/step\n","Epoch 361/1000\n","5/5 - 1s - loss: 6.5851e-07 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9836 - 848ms/epoch - 170ms/step\n","Epoch 362/1000\n","5/5 - 1s - loss: 6.5622e-07 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9836 - 769ms/epoch - 154ms/step\n","Epoch 363/1000\n","5/5 - 1s - loss: 6.5425e-07 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9836 - 934ms/epoch - 187ms/step\n","Epoch 364/1000\n","5/5 - 1s - loss: 6.5201e-07 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9836 - 811ms/epoch - 162ms/step\n","Epoch 365/1000\n","5/5 - 1s - loss: 6.4987e-07 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9836 - 837ms/epoch - 167ms/step\n","Epoch 366/1000\n","5/5 - 1s - loss: 6.4783e-07 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9836 - 712ms/epoch - 142ms/step\n","Epoch 367/1000\n","5/5 - 1s - loss: 6.4577e-07 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9836 - 874ms/epoch - 175ms/step\n","Epoch 368/1000\n","5/5 - 1s - loss: 6.4362e-07 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9836 - 1s/epoch - 214ms/step\n","Epoch 369/1000\n","5/5 - 1s - loss: 6.4155e-07 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 370/1000\n","5/5 - 1s - loss: 6.3952e-07 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9836 - 862ms/epoch - 172ms/step\n","Epoch 371/1000\n","5/5 - 1s - loss: 6.3745e-07 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9836 - 1s/epoch - 209ms/step\n","Epoch 372/1000\n","5/5 - 1s - loss: 6.3541e-07 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9836 - 996ms/epoch - 199ms/step\n","Epoch 373/1000\n","5/5 - 1s - loss: 6.3340e-07 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9836 - 1s/epoch - 208ms/step\n","Epoch 374/1000\n","5/5 - 1s - loss: 6.3124e-07 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9836 - 897ms/epoch - 179ms/step\n","Epoch 375/1000\n","5/5 - 1s - loss: 6.2925e-07 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9836 - 876ms/epoch - 175ms/step\n","Epoch 376/1000\n","5/5 - 1s - loss: 6.2728e-07 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 377/1000\n","5/5 - 1s - loss: 6.2513e-07 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9836 - 705ms/epoch - 141ms/step\n","Epoch 378/1000\n","5/5 - 1s - loss: 6.2321e-07 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 379/1000\n","5/5 - 1s - loss: 6.2121e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 380/1000\n","5/5 - 1s - loss: 6.1912e-07 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9836 - 707ms/epoch - 141ms/step\n","Epoch 381/1000\n","5/5 - 1s - loss: 6.1728e-07 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 382/1000\n","5/5 - 1s - loss: 6.1526e-07 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9836 - 703ms/epoch - 141ms/step\n","Epoch 383/1000\n","5/5 - 1s - loss: 6.1320e-07 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 384/1000\n","5/5 - 1s - loss: 6.1125e-07 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 385/1000\n","5/5 - 1s - loss: 6.0918e-07 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9836 - 697ms/epoch - 139ms/step\n","Epoch 386/1000\n","5/5 - 1s - loss: 6.0742e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 387/1000\n","5/5 - 1s - loss: 6.0532e-07 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9836 - 690ms/epoch - 138ms/step\n","Epoch 388/1000\n","5/5 - 1s - loss: 6.0333e-07 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9836 - 846ms/epoch - 169ms/step\n","Epoch 389/1000\n","5/5 - 1s - loss: 6.0154e-07 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9836 - 758ms/epoch - 152ms/step\n","Epoch 390/1000\n","5/5 - 1s - loss: 5.9952e-07 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9836 - 933ms/epoch - 187ms/step\n","Epoch 391/1000\n","5/5 - 1s - loss: 5.9762e-07 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9836 - 865ms/epoch - 173ms/step\n","Epoch 392/1000\n","5/5 - 1s - loss: 5.9556e-07 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 393/1000\n","5/5 - 1s - loss: 5.9363e-07 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 394/1000\n","5/5 - 1s - loss: 5.9178e-07 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 395/1000\n","5/5 - 1s - loss: 5.8988e-07 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 396/1000\n","5/5 - 1s - loss: 5.8797e-07 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9836 - 737ms/epoch - 147ms/step\n","Epoch 397/1000\n","5/5 - 1s - loss: 5.8608e-07 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 398/1000\n","5/5 - 1s - loss: 5.8418e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 399/1000\n","5/5 - 1s - loss: 5.8231e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9836 - 862ms/epoch - 172ms/step\n","Epoch 400/1000\n","5/5 - 1s - loss: 5.8036e-07 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 401/1000\n","5/5 - 1s - loss: 5.7859e-07 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9836 - 659ms/epoch - 132ms/step\n","Epoch 402/1000\n","5/5 - 1s - loss: 5.7669e-07 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9836 - 836ms/epoch - 167ms/step\n","Epoch 403/1000\n","5/5 - 1s - loss: 5.7486e-07 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 404/1000\n","5/5 - 1s - loss: 5.7303e-07 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9836 - 817ms/epoch - 163ms/step\n","Epoch 405/1000\n","5/5 - 1s - loss: 5.7115e-07 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9836 - 756ms/epoch - 151ms/step\n","Epoch 406/1000\n","5/5 - 1s - loss: 5.6936e-07 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 407/1000\n","5/5 - 1s - loss: 5.6750e-07 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9836 - 760ms/epoch - 152ms/step\n","Epoch 408/1000\n","5/5 - 1s - loss: 5.6575e-07 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9836 - 727ms/epoch - 145ms/step\n","Epoch 409/1000\n","5/5 - 1s - loss: 5.6389e-07 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9836 - 780ms/epoch - 156ms/step\n","Epoch 410/1000\n","5/5 - 1s - loss: 5.6205e-07 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9836 - 815ms/epoch - 163ms/step\n","Epoch 411/1000\n","5/5 - 1s - loss: 5.6029e-07 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9836 - 946ms/epoch - 189ms/step\n","Epoch 412/1000\n","5/5 - 1s - loss: 5.5855e-07 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9836 - 750ms/epoch - 150ms/step\n","Epoch 413/1000\n","5/5 - 1s - loss: 5.5678e-07 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9836 - 745ms/epoch - 149ms/step\n","Epoch 414/1000\n","5/5 - 1s - loss: 5.5494e-07 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9836 - 840ms/epoch - 168ms/step\n","Epoch 415/1000\n","5/5 - 1s - loss: 5.5313e-07 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9836 - 765ms/epoch - 153ms/step\n","Epoch 416/1000\n","5/5 - 1s - loss: 5.5143e-07 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 417/1000\n","5/5 - 1s - loss: 5.4976e-07 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9836 - 795ms/epoch - 159ms/step\n","Epoch 418/1000\n","5/5 - 1s - loss: 5.4800e-07 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 419/1000\n","5/5 - 1s - loss: 5.4626e-07 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9836 - 787ms/epoch - 157ms/step\n","Epoch 420/1000\n","5/5 - 1s - loss: 5.4452e-07 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9836 - 805ms/epoch - 161ms/step\n","Epoch 421/1000\n","5/5 - 1s - loss: 5.4281e-07 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 422/1000\n","5/5 - 1s - loss: 5.4113e-07 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 423/1000\n","5/5 - 1s - loss: 5.3946e-07 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9836 - 988ms/epoch - 198ms/step\n","Epoch 424/1000\n","5/5 - 1s - loss: 5.3767e-07 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9836 - 802ms/epoch - 160ms/step\n","Epoch 425/1000\n","5/5 - 1s - loss: 5.3599e-07 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9836 - 757ms/epoch - 151ms/step\n","Epoch 426/1000\n","5/5 - 1s - loss: 5.3428e-07 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9836 - 926ms/epoch - 185ms/step\n","Epoch 427/1000\n","5/5 - 1s - loss: 5.3267e-07 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9836 - 793ms/epoch - 159ms/step\n","Epoch 428/1000\n","5/5 - 1s - loss: 5.3096e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 913ms/epoch - 183ms/step\n","Epoch 429/1000\n","5/5 - 1s - loss: 5.2931e-07 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9836 - 884ms/epoch - 177ms/step\n","Epoch 430/1000\n","5/5 - 1s - loss: 5.2777e-07 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9836 - 811ms/epoch - 162ms/step\n","Epoch 431/1000\n","5/5 - 1s - loss: 5.2611e-07 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9836 - 1s/epoch - 251ms/step\n","Epoch 432/1000\n","5/5 - 1s - loss: 5.2438e-07 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9836 - 822ms/epoch - 164ms/step\n","Epoch 433/1000\n","5/5 - 1s - loss: 5.2272e-07 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9836 - 765ms/epoch - 153ms/step\n","Epoch 434/1000\n","5/5 - 1s - loss: 5.2107e-07 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9836 - 853ms/epoch - 171ms/step\n","Epoch 435/1000\n","5/5 - 1s - loss: 5.1953e-07 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9836 - 849ms/epoch - 170ms/step\n","Epoch 436/1000\n","5/5 - 1s - loss: 5.1786e-07 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9836 - 735ms/epoch - 147ms/step\n","Epoch 437/1000\n","5/5 - 1s - loss: 5.1631e-07 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9836 - 703ms/epoch - 141ms/step\n","Epoch 438/1000\n","5/5 - 1s - loss: 5.1473e-07 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9836 - 750ms/epoch - 150ms/step\n","Epoch 439/1000\n","5/5 - 1s - loss: 5.1302e-07 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9836 - 770ms/epoch - 154ms/step\n","Epoch 440/1000\n","5/5 - 1s - loss: 5.1145e-07 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9836 - 851ms/epoch - 170ms/step\n","Epoch 441/1000\n","5/5 - 1s - loss: 5.0992e-07 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9836 - 706ms/epoch - 141ms/step\n","Epoch 442/1000\n","5/5 - 1s - loss: 5.0820e-07 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9836 - 881ms/epoch - 176ms/step\n","Epoch 443/1000\n","5/5 - 1s - loss: 5.0678e-07 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9836 - 664ms/epoch - 133ms/step\n","Epoch 444/1000\n","5/5 - 1s - loss: 5.0522e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 929ms/epoch - 186ms/step\n","Epoch 445/1000\n","5/5 - 1s - loss: 5.0368e-07 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9836 - 1s/epoch - 201ms/step\n","Epoch 446/1000\n","5/5 - 1s - loss: 5.0197e-07 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9836 - 1s/epoch - 203ms/step\n","Epoch 447/1000\n","5/5 - 1s - loss: 5.0046e-07 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9836 - 743ms/epoch - 149ms/step\n","Epoch 448/1000\n","5/5 - 1s - loss: 4.9889e-07 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9836 - 960ms/epoch - 192ms/step\n","Epoch 449/1000\n","5/5 - 1s - loss: 4.9731e-07 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9836 - 1s/epoch - 278ms/step\n","Epoch 450/1000\n","5/5 - 1s - loss: 4.9571e-07 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9836 - 1s/epoch - 270ms/step\n","Epoch 451/1000\n","5/5 - 1s - loss: 4.9420e-07 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9836 - 1s/epoch - 233ms/step\n","Epoch 452/1000\n","5/5 - 1s - loss: 4.9272e-07 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9836 - 836ms/epoch - 167ms/step\n","Epoch 453/1000\n","5/5 - 1s - loss: 4.9117e-07 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9836 - 878ms/epoch - 176ms/step\n","Epoch 454/1000\n","5/5 - 1s - loss: 4.8959e-07 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9836 - 845ms/epoch - 169ms/step\n","Epoch 455/1000\n","5/5 - 1s - loss: 4.8808e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 804ms/epoch - 161ms/step\n","Epoch 456/1000\n","5/5 - 1s - loss: 4.8656e-07 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9836 - 748ms/epoch - 150ms/step\n","Epoch 457/1000\n","5/5 - 1s - loss: 4.8507e-07 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9836 - 610ms/epoch - 122ms/step\n","Epoch 458/1000\n","5/5 - 1s - loss: 4.8345e-07 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9836 - 804ms/epoch - 161ms/step\n","Epoch 459/1000\n","5/5 - 1s - loss: 4.8203e-07 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9836 - 900ms/epoch - 180ms/step\n","Epoch 460/1000\n","5/5 - 1s - loss: 4.8059e-07 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9836 - 613ms/epoch - 123ms/step\n","Epoch 461/1000\n","5/5 - 1s - loss: 4.7898e-07 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 462/1000\n","5/5 - 1s - loss: 4.7752e-07 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 463/1000\n","5/5 - 1s - loss: 4.7610e-07 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9836 - 901ms/epoch - 180ms/step\n","Epoch 464/1000\n","5/5 - 1s - loss: 4.7456e-07 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9836 - 819ms/epoch - 164ms/step\n","Epoch 465/1000\n","5/5 - 1s - loss: 4.7311e-07 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9836 - 811ms/epoch - 162ms/step\n","Epoch 466/1000\n","5/5 - 1s - loss: 4.7163e-07 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9836 - 1s/epoch - 208ms/step\n","Epoch 467/1000\n","5/5 - 1s - loss: 4.7019e-07 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9836 - 882ms/epoch - 176ms/step\n","Epoch 468/1000\n","5/5 - 1s - loss: 4.6877e-07 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9836 - 1s/epoch - 259ms/step\n","Epoch 469/1000\n","5/5 - 1s - loss: 4.6727e-07 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9836 - 798ms/epoch - 160ms/step\n","Epoch 470/1000\n","5/5 - 1s - loss: 4.6576e-07 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9836 - 767ms/epoch - 153ms/step\n","Epoch 471/1000\n","5/5 - 1s - loss: 4.6441e-07 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9836 - 803ms/epoch - 161ms/step\n","Epoch 472/1000\n","5/5 - 1s - loss: 4.6299e-07 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 473/1000\n","5/5 - 1s - loss: 4.6148e-07 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9836 - 745ms/epoch - 149ms/step\n","Epoch 474/1000\n","5/5 - 1s - loss: 4.6020e-07 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 475/1000\n","5/5 - 1s - loss: 4.5864e-07 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9836 - 714ms/epoch - 143ms/step\n","Epoch 476/1000\n","5/5 - 1s - loss: 4.5727e-07 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9836 - 714ms/epoch - 143ms/step\n","Epoch 477/1000\n","5/5 - 1s - loss: 4.5593e-07 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 478/1000\n","5/5 - 1s - loss: 4.5438e-07 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9836 - 706ms/epoch - 141ms/step\n","Epoch 479/1000\n","5/5 - 1s - loss: 4.5310e-07 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 480/1000\n","5/5 - 1s - loss: 4.5174e-07 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 481/1000\n","5/5 - 1s - loss: 4.5032e-07 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 482/1000\n","5/5 - 1s - loss: 4.4896e-07 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9836 - 719ms/epoch - 144ms/step\n","Epoch 483/1000\n","5/5 - 1s - loss: 4.4762e-07 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9836 - 695ms/epoch - 139ms/step\n","Epoch 484/1000\n","5/5 - 1s - loss: 4.4617e-07 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9836 - 683ms/epoch - 137ms/step\n","Epoch 485/1000\n","5/5 - 1s - loss: 4.4484e-07 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 486/1000\n","5/5 - 1s - loss: 4.4356e-07 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9836 - 693ms/epoch - 139ms/step\n","Epoch 487/1000\n","5/5 - 1s - loss: 4.4211e-07 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 488/1000\n","5/5 - 1s - loss: 4.4081e-07 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9836 - 675ms/epoch - 135ms/step\n","Epoch 489/1000\n","5/5 - 1s - loss: 4.3947e-07 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 490/1000\n","5/5 - 1s - loss: 4.3808e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 491/1000\n","5/5 - 1s - loss: 4.3674e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 492/1000\n","5/5 - 1s - loss: 4.3546e-07 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9836 - 681ms/epoch - 136ms/step\n","Epoch 493/1000\n","5/5 - 1s - loss: 4.3412e-07 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9836 - 657ms/epoch - 131ms/step\n","Epoch 494/1000\n","5/5 - 1s - loss: 4.3279e-07 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 495/1000\n","5/5 - 1s - loss: 4.3141e-07 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9836 - 697ms/epoch - 139ms/step\n","Epoch 496/1000\n","5/5 - 1s - loss: 4.3017e-07 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9836 - 686ms/epoch - 137ms/step\n","Epoch 497/1000\n","5/5 - 1s - loss: 4.2883e-07 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 498/1000\n","5/5 - 1s - loss: 4.2742e-07 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 499/1000\n","5/5 - 1s - loss: 4.2620e-07 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9836 - 721ms/epoch - 144ms/step\n","Epoch 500/1000\n","5/5 - 1s - loss: 4.2487e-07 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9836 - 709ms/epoch - 142ms/step\n","Epoch 501/1000\n","5/5 - 1s - loss: 4.2353e-07 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9836 - 696ms/epoch - 139ms/step\n","Epoch 502/1000\n","5/5 - 1s - loss: 4.2232e-07 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 503/1000\n","5/5 - 1s - loss: 4.2102e-07 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9836 - 674ms/epoch - 135ms/step\n","Epoch 504/1000\n","5/5 - 1s - loss: 4.1974e-07 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9836 - 673ms/epoch - 135ms/step\n","Epoch 505/1000\n","5/5 - 1s - loss: 4.1842e-07 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9836 - 716ms/epoch - 143ms/step\n","Epoch 506/1000\n","5/5 - 1s - loss: 4.1712e-07 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9836 - 729ms/epoch - 146ms/step\n","Epoch 507/1000\n","5/5 - 1s - loss: 4.1587e-07 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9836 - 720ms/epoch - 144ms/step\n","Epoch 508/1000\n","5/5 - 1s - loss: 4.1463e-07 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 509/1000\n","5/5 - 1s - loss: 4.1331e-07 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 510/1000\n","5/5 - 1s - loss: 4.1210e-07 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9836 - 882ms/epoch - 176ms/step\n","Epoch 511/1000\n","5/5 - 1s - loss: 4.1081e-07 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9836 - 931ms/epoch - 186ms/step\n","Epoch 512/1000\n","5/5 - 1s - loss: 4.0958e-07 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9836 - 843ms/epoch - 169ms/step\n","Epoch 513/1000\n","5/5 - 1s - loss: 4.0828e-07 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9836 - 889ms/epoch - 178ms/step\n","Epoch 514/1000\n","5/5 - 1s - loss: 4.0708e-07 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9836 - 774ms/epoch - 155ms/step\n","Epoch 515/1000\n","5/5 - 1s - loss: 4.0580e-07 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 516/1000\n","5/5 - 1s - loss: 4.0459e-07 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9836 - 884ms/epoch - 177ms/step\n","Epoch 517/1000\n","5/5 - 1s - loss: 4.0333e-07 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9836 - 744ms/epoch - 149ms/step\n","Epoch 518/1000\n","5/5 - 1s - loss: 4.0211e-07 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 519/1000\n","5/5 - 1s - loss: 4.0081e-07 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9836 - 676ms/epoch - 135ms/step\n","Epoch 520/1000\n","5/5 - 1s - loss: 3.9967e-07 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9836 - 635ms/epoch - 127ms/step\n","Epoch 521/1000\n","5/5 - 1s - loss: 3.9842e-07 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9836 - 763ms/epoch - 153ms/step\n","Epoch 522/1000\n","5/5 - 1s - loss: 3.9718e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 523/1000\n","5/5 - 1s - loss: 3.9606e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9836 - 780ms/epoch - 156ms/step\n","Epoch 524/1000\n","5/5 - 1s - loss: 3.9475e-07 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9836 - 704ms/epoch - 141ms/step\n","Epoch 525/1000\n","5/5 - 1s - loss: 3.9358e-07 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 526/1000\n","5/5 - 1s - loss: 3.9244e-07 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 527/1000\n","5/5 - 1s - loss: 3.9118e-07 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 528/1000\n","5/5 - 1s - loss: 3.9004e-07 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 529/1000\n","5/5 - 1s - loss: 3.8881e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 530/1000\n","5/5 - 1s - loss: 3.8763e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9836 - 674ms/epoch - 135ms/step\n","Epoch 531/1000\n","5/5 - 1s - loss: 3.8647e-07 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9836 - 807ms/epoch - 161ms/step\n","Epoch 532/1000\n","5/5 - 1s - loss: 3.8530e-07 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 533/1000\n","5/5 - 1s - loss: 3.8416e-07 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9836 - 765ms/epoch - 153ms/step\n","Epoch 534/1000\n","5/5 - 1s - loss: 3.8295e-07 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9836 - 810ms/epoch - 162ms/step\n","Epoch 535/1000\n","5/5 - 1s - loss: 3.8178e-07 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9836 - 940ms/epoch - 188ms/step\n","Epoch 536/1000\n","5/5 - 1s - loss: 3.8064e-07 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9836 - 696ms/epoch - 139ms/step\n","Epoch 537/1000\n","5/5 - 1s - loss: 3.7941e-07 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9836 - 700ms/epoch - 140ms/step\n","Epoch 538/1000\n","5/5 - 1s - loss: 3.7828e-07 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 539/1000\n","5/5 - 1s - loss: 3.7726e-07 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 540/1000\n","5/5 - 1s - loss: 3.7605e-07 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 541/1000\n","5/5 - 1s - loss: 3.7489e-07 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 542/1000\n","5/5 - 1s - loss: 3.7370e-07 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9836 - 594ms/epoch - 119ms/step\n","Epoch 543/1000\n","5/5 - 1s - loss: 3.7255e-07 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 544/1000\n","5/5 - 1s - loss: 3.7150e-07 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9836 - 567ms/epoch - 113ms/step\n","Epoch 545/1000\n","5/5 - 1s - loss: 3.7031e-07 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 546/1000\n","5/5 - 1s - loss: 3.6922e-07 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9836 - 633ms/epoch - 127ms/step\n","Epoch 547/1000\n","5/5 - 1s - loss: 3.6810e-07 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 548/1000\n","5/5 - 1s - loss: 3.6704e-07 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 549/1000\n","5/5 - 1s - loss: 3.6588e-07 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9836 - 629ms/epoch - 126ms/step\n","Epoch 550/1000\n","5/5 - 1s - loss: 3.6479e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 551/1000\n","5/5 - 1s - loss: 3.6368e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 552/1000\n","5/5 - 1s - loss: 3.6262e-07 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9836 - 701ms/epoch - 140ms/step\n","Epoch 553/1000\n","5/5 - 1s - loss: 3.6153e-07 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9836 - 775ms/epoch - 155ms/step\n","Epoch 554/1000\n","5/5 - 1s - loss: 3.6043e-07 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9836 - 915ms/epoch - 183ms/step\n","Epoch 555/1000\n","5/5 - 1s - loss: 3.5941e-07 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9836 - 816ms/epoch - 163ms/step\n","Epoch 556/1000\n","5/5 - 1s - loss: 3.5828e-07 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9836 - 692ms/epoch - 138ms/step\n","Epoch 557/1000\n","5/5 - 1s - loss: 3.5721e-07 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 558/1000\n","5/5 - 1s - loss: 3.5623e-07 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9836 - 652ms/epoch - 130ms/step\n","Epoch 559/1000\n","5/5 - 1s - loss: 3.5516e-07 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9836 - 647ms/epoch - 129ms/step\n","Epoch 560/1000\n","5/5 - 1s - loss: 3.5407e-07 - accuracy: 1.0000 - val_loss: 0.2089 - val_accuracy: 0.9836 - 882ms/epoch - 176ms/step\n","Epoch 561/1000\n","5/5 - 1s - loss: 3.5298e-07 - accuracy: 1.0000 - val_loss: 0.2089 - val_accuracy: 0.9836 - 1s/epoch - 216ms/step\n","Epoch 562/1000\n","5/5 - 1s - loss: 3.5193e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9836 - 1s/epoch - 232ms/step\n","Epoch 563/1000\n","5/5 - 1s - loss: 3.5095e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9836 - 1s/epoch - 223ms/step\n","Epoch 564/1000\n","5/5 - 1s - loss: 3.4988e-07 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9836 - 861ms/epoch - 172ms/step\n","Epoch 565/1000\n","5/5 - 1s - loss: 3.4877e-07 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9836 - 606ms/epoch - 121ms/step\n","Epoch 566/1000\n","5/5 - 1s - loss: 3.4778e-07 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 567/1000\n","5/5 - 1s - loss: 3.4675e-07 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 568/1000\n","5/5 - 1s - loss: 3.4571e-07 - accuracy: 1.0000 - val_loss: 0.2093 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 569/1000\n","5/5 - 1s - loss: 3.4465e-07 - accuracy: 1.0000 - val_loss: 0.2093 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 570/1000\n","5/5 - 1s - loss: 3.4360e-07 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9836 - 618ms/epoch - 124ms/step\n","Epoch 571/1000\n","5/5 - 1s - loss: 3.4255e-07 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 572/1000\n","5/5 - 1s - loss: 3.4156e-07 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 573/1000\n","5/5 - 1s - loss: 3.4052e-07 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 574/1000\n","5/5 - 1s - loss: 3.3954e-07 - accuracy: 1.0000 - val_loss: 0.2096 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 575/1000\n","5/5 - 1s - loss: 3.3846e-07 - accuracy: 1.0000 - val_loss: 0.2096 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 576/1000\n","5/5 - 1s - loss: 3.3749e-07 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 577/1000\n","5/5 - 1s - loss: 3.3646e-07 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 578/1000\n","5/5 - 1s - loss: 3.3551e-07 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9836 - 872ms/epoch - 174ms/step\n","Epoch 579/1000\n","5/5 - 1s - loss: 3.3443e-07 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 580/1000\n","5/5 - 1s - loss: 3.3349e-07 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 581/1000\n","5/5 - 1s - loss: 3.3247e-07 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9836 - 986ms/epoch - 197ms/step\n","Epoch 582/1000\n","5/5 - 1s - loss: 3.3156e-07 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9836 - 1s/epoch - 239ms/step\n","Epoch 583/1000\n","5/5 - 1s - loss: 3.3045e-07 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9836 - 1s/epoch - 202ms/step\n","Epoch 584/1000\n","5/5 - 1s - loss: 3.2953e-07 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9836 - 773ms/epoch - 155ms/step\n","Epoch 585/1000\n","5/5 - 1s - loss: 3.2849e-07 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 586/1000\n","5/5 - 1s - loss: 3.2759e-07 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9836 - 767ms/epoch - 153ms/step\n","Epoch 587/1000\n","5/5 - 1s - loss: 3.2660e-07 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9836 - 739ms/epoch - 148ms/step\n","Epoch 588/1000\n","5/5 - 1s - loss: 3.2565e-07 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9836 - 877ms/epoch - 175ms/step\n","Epoch 589/1000\n","5/5 - 1s - loss: 3.2463e-07 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9836 - 698ms/epoch - 140ms/step\n","Epoch 590/1000\n","5/5 - 1s - loss: 3.2369e-07 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9836 - 716ms/epoch - 143ms/step\n","Epoch 591/1000\n","5/5 - 1s - loss: 3.2271e-07 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9836 - 706ms/epoch - 141ms/step\n","Epoch 592/1000\n","5/5 - 1s - loss: 3.2179e-07 - accuracy: 1.0000 - val_loss: 0.2105 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 593/1000\n","5/5 - 1s - loss: 3.2088e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 594/1000\n","5/5 - 1s - loss: 3.1990e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9836 - 761ms/epoch - 152ms/step\n","Epoch 595/1000\n","5/5 - 1s - loss: 3.1892e-07 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9836 - 713ms/epoch - 143ms/step\n","Epoch 596/1000\n","5/5 - 1s - loss: 3.1800e-07 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9836 - 720ms/epoch - 144ms/step\n","Epoch 597/1000\n","5/5 - 1s - loss: 3.1707e-07 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 598/1000\n","5/5 - 1s - loss: 3.1611e-07 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9836 - 677ms/epoch - 135ms/step\n","Epoch 599/1000\n","5/5 - 1s - loss: 3.1517e-07 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 600/1000\n","5/5 - 1s - loss: 3.1428e-07 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 601/1000\n","5/5 - 1s - loss: 3.1334e-07 - accuracy: 1.0000 - val_loss: 0.2110 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 602/1000\n","5/5 - 1s - loss: 3.1241e-07 - accuracy: 1.0000 - val_loss: 0.2110 - val_accuracy: 0.9836 - 707ms/epoch - 141ms/step\n","Epoch 603/1000\n","5/5 - 1s - loss: 3.1151e-07 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 604/1000\n","5/5 - 1s - loss: 3.1057e-07 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9836 - 633ms/epoch - 127ms/step\n","Epoch 605/1000\n","5/5 - 1s - loss: 3.0969e-07 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9836 - 604ms/epoch - 121ms/step\n","Epoch 606/1000\n","5/5 - 1s - loss: 3.0873e-07 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9836 - 666ms/epoch - 133ms/step\n","Epoch 607/1000\n","5/5 - 1s - loss: 3.0787e-07 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9836 - 975ms/epoch - 195ms/step\n","Epoch 608/1000\n","5/5 - 1s - loss: 3.0694e-07 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9836 - 888ms/epoch - 178ms/step\n","Epoch 609/1000\n","5/5 - 1s - loss: 3.0602e-07 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9836 - 826ms/epoch - 165ms/step\n","Epoch 610/1000\n","5/5 - 1s - loss: 3.0515e-07 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9836 - 786ms/epoch - 157ms/step\n","Epoch 611/1000\n","5/5 - 1s - loss: 3.0421e-07 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9836 - 841ms/epoch - 168ms/step\n","Epoch 612/1000\n","5/5 - 1s - loss: 3.0338e-07 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9836 - 925ms/epoch - 185ms/step\n","Epoch 613/1000\n","5/5 - 1s - loss: 3.0247e-07 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9836 - 854ms/epoch - 171ms/step\n","Epoch 614/1000\n","5/5 - 1s - loss: 3.0156e-07 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9836 - 834ms/epoch - 167ms/step\n","Epoch 615/1000\n","5/5 - 1s - loss: 3.0071e-07 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9836 - 808ms/epoch - 162ms/step\n","Epoch 616/1000\n","5/5 - 1s - loss: 2.9986e-07 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9836 - 712ms/epoch - 142ms/step\n","Epoch 617/1000\n","5/5 - 1s - loss: 2.9894e-07 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 618/1000\n","5/5 - 1s - loss: 2.9806e-07 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 619/1000\n","5/5 - 1s - loss: 2.9722e-07 - accuracy: 1.0000 - val_loss: 0.2119 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 620/1000\n","5/5 - 1s - loss: 2.9631e-07 - accuracy: 1.0000 - val_loss: 0.2119 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 621/1000\n","5/5 - 1s - loss: 2.9550e-07 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 622/1000\n","5/5 - 1s - loss: 2.9460e-07 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9836 - 507ms/epoch - 101ms/step\n","Epoch 623/1000\n","5/5 - 1s - loss: 2.9372e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9836 - 504ms/epoch - 101ms/step\n","Epoch 624/1000\n","5/5 - 1s - loss: 2.9287e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9836 - 572ms/epoch - 114ms/step\n","Epoch 625/1000\n","5/5 - 1s - loss: 2.9206e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 626/1000\n","5/5 - 1s - loss: 2.9116e-07 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9836 - 609ms/epoch - 122ms/step\n","Epoch 627/1000\n","5/5 - 1s - loss: 2.9032e-07 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 628/1000\n","5/5 - 1s - loss: 2.8946e-07 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9836 - 631ms/epoch - 126ms/step\n","Epoch 629/1000\n","5/5 - 1s - loss: 2.8867e-07 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9836 - 583ms/epoch - 117ms/step\n","Epoch 630/1000\n","5/5 - 1s - loss: 2.8787e-07 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 631/1000\n","5/5 - 1s - loss: 2.8696e-07 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9836 - 781ms/epoch - 156ms/step\n","Epoch 632/1000\n","5/5 - 1s - loss: 2.8611e-07 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9836 - 656ms/epoch - 131ms/step\n","Epoch 633/1000\n","5/5 - 1s - loss: 2.8531e-07 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9836 - 620ms/epoch - 124ms/step\n","Epoch 634/1000\n","5/5 - 1s - loss: 2.8440e-07 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 635/1000\n","5/5 - 1s - loss: 2.8362e-07 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 636/1000\n","5/5 - 1s - loss: 2.8279e-07 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 637/1000\n","5/5 - 1s - loss: 2.8193e-07 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9836 - 588ms/epoch - 118ms/step\n","Epoch 638/1000\n","5/5 - 1s - loss: 2.8117e-07 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9836 - 581ms/epoch - 116ms/step\n","Epoch 639/1000\n","5/5 - 1s - loss: 2.8030e-07 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 640/1000\n","5/5 - 1s - loss: 2.7948e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9836 - 528ms/epoch - 106ms/step\n","Epoch 641/1000\n","5/5 - 1s - loss: 2.7866e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9836 - 541ms/epoch - 108ms/step\n","Epoch 642/1000\n","5/5 - 1s - loss: 2.7781e-07 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 643/1000\n","5/5 - 1s - loss: 2.7705e-07 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 644/1000\n","5/5 - 1s - loss: 2.7623e-07 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 645/1000\n","5/5 - 1s - loss: 2.7543e-07 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 646/1000\n","5/5 - 1s - loss: 2.7461e-07 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9836 - 640ms/epoch - 128ms/step\n","Epoch 647/1000\n","5/5 - 1s - loss: 2.7385e-07 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 648/1000\n","5/5 - 1s - loss: 2.7306e-07 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9836 - 531ms/epoch - 106ms/step\n","Epoch 649/1000\n","5/5 - 1s - loss: 2.7226e-07 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9836 - 567ms/epoch - 113ms/step\n","Epoch 650/1000\n","5/5 - 1s - loss: 2.7146e-07 - accuracy: 1.0000 - val_loss: 0.2134 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 651/1000\n","5/5 - 1s - loss: 2.7065e-07 - accuracy: 1.0000 - val_loss: 0.2134 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 652/1000\n","5/5 - 1s - loss: 2.6996e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 653/1000\n","5/5 - 1s - loss: 2.6912e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 654/1000\n","5/5 - 1s - loss: 2.6835e-07 - accuracy: 1.0000 - val_loss: 0.2136 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 655/1000\n","5/5 - 1s - loss: 2.6755e-07 - accuracy: 1.0000 - val_loss: 0.2136 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 656/1000\n","5/5 - 1s - loss: 2.6678e-07 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 657/1000\n","5/5 - 1s - loss: 2.6601e-07 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9836 - 652ms/epoch - 130ms/step\n","Epoch 658/1000\n","5/5 - 1s - loss: 2.6527e-07 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9836 - 601ms/epoch - 120ms/step\n","Epoch 659/1000\n","5/5 - 1s - loss: 2.6450e-07 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 660/1000\n","5/5 - 1s - loss: 2.6371e-07 - accuracy: 1.0000 - val_loss: 0.2139 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 661/1000\n","5/5 - 1s - loss: 2.6290e-07 - accuracy: 1.0000 - val_loss: 0.2139 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 662/1000\n","5/5 - 1s - loss: 2.6216e-07 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 663/1000\n","5/5 - 1s - loss: 2.6138e-07 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 664/1000\n","5/5 - 1s - loss: 2.6066e-07 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9836 - 688ms/epoch - 138ms/step\n","Epoch 665/1000\n","5/5 - 1s - loss: 2.5989e-07 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9836 - 883ms/epoch - 177ms/step\n","Epoch 666/1000\n","5/5 - 1s - loss: 2.5910e-07 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9836 - 711ms/epoch - 142ms/step\n","Epoch 667/1000\n","5/5 - 1s - loss: 2.5839e-07 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 668/1000\n","5/5 - 1s - loss: 2.5762e-07 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9836 - 690ms/epoch - 138ms/step\n","Epoch 669/1000\n","5/5 - 1s - loss: 2.5685e-07 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9836 - 726ms/epoch - 145ms/step\n","Epoch 670/1000\n","5/5 - 1s - loss: 2.5610e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9836 - 669ms/epoch - 134ms/step\n","Epoch 671/1000\n","5/5 - 1s - loss: 2.5542e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9836 - 676ms/epoch - 135ms/step\n","Epoch 672/1000\n","5/5 - 1s - loss: 2.5464e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 673/1000\n","5/5 - 1s - loss: 2.5395e-07 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9836 - 630ms/epoch - 126ms/step\n","Epoch 674/1000\n","5/5 - 1s - loss: 2.5316e-07 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9836 - 625ms/epoch - 125ms/step\n","Epoch 675/1000\n","5/5 - 1s - loss: 2.5242e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 676/1000\n","5/5 - 1s - loss: 2.5172e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9836 - 621ms/epoch - 124ms/step\n","Epoch 677/1000\n","5/5 - 1s - loss: 2.5099e-07 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9836 - 741ms/epoch - 148ms/step\n","Epoch 678/1000\n","5/5 - 1s - loss: 2.5026e-07 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 679/1000\n","5/5 - 1s - loss: 2.4952e-07 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 0.9836 - 672ms/epoch - 134ms/step\n","Epoch 680/1000\n","5/5 - 1s - loss: 2.4883e-07 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 0.9836 - 730ms/epoch - 146ms/step\n","Epoch 681/1000\n","5/5 - 1s - loss: 2.4812e-07 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 682/1000\n","5/5 - 1s - loss: 2.4735e-07 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 683/1000\n","5/5 - 1s - loss: 2.4666e-07 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9836 - 711ms/epoch - 142ms/step\n","Epoch 684/1000\n","5/5 - 1s - loss: 2.4593e-07 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9836 - 699ms/epoch - 140ms/step\n","Epoch 685/1000\n","5/5 - 1s - loss: 2.4521e-07 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9836 - 717ms/epoch - 143ms/step\n","Epoch 686/1000\n","5/5 - 1s - loss: 2.4452e-07 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9836 - 718ms/epoch - 144ms/step\n","Epoch 687/1000\n","5/5 - 1s - loss: 2.4378e-07 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 688/1000\n","5/5 - 1s - loss: 2.4312e-07 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9836 - 539ms/epoch - 108ms/step\n","Epoch 689/1000\n","5/5 - 1s - loss: 2.4235e-07 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 690/1000\n","5/5 - 1s - loss: 2.4171e-07 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 691/1000\n","5/5 - 1s - loss: 2.4098e-07 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9836 - 535ms/epoch - 107ms/step\n","Epoch 692/1000\n","5/5 - 1s - loss: 2.4025e-07 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 693/1000\n","5/5 - 1s - loss: 2.3958e-07 - accuracy: 1.0000 - val_loss: 0.2155 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 694/1000\n","5/5 - 1s - loss: 2.3888e-07 - accuracy: 1.0000 - val_loss: 0.2155 - val_accuracy: 0.9836 - 526ms/epoch - 105ms/step\n","Epoch 695/1000\n","5/5 - 1s - loss: 2.3817e-07 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9836 - 541ms/epoch - 108ms/step\n","Epoch 696/1000\n","5/5 - 1s - loss: 2.3746e-07 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9836 - 662ms/epoch - 132ms/step\n","Epoch 697/1000\n","5/5 - 1s - loss: 2.3680e-07 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9836 - 623ms/epoch - 125ms/step\n","Epoch 698/1000\n","5/5 - 1s - loss: 2.3609e-07 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9836 - 634ms/epoch - 127ms/step\n","Epoch 699/1000\n","5/5 - 1s - loss: 2.3541e-07 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 700/1000\n","5/5 - 1s - loss: 2.3470e-07 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 701/1000\n","5/5 - 1s - loss: 2.3405e-07 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 702/1000\n","5/5 - 1s - loss: 2.3337e-07 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9836 - 689ms/epoch - 138ms/step\n","Epoch 703/1000\n","5/5 - 1s - loss: 2.3269e-07 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9836 - 666ms/epoch - 133ms/step\n","Epoch 704/1000\n","5/5 - 1s - loss: 2.3204e-07 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 705/1000\n","5/5 - 1s - loss: 2.3135e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 706/1000\n","5/5 - 1s - loss: 2.3067e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 707/1000\n","5/5 - 1s - loss: 2.3002e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 708/1000\n","5/5 - 1s - loss: 2.2936e-07 - accuracy: 1.0000 - val_loss: 0.2162 - val_accuracy: 0.9836 - 682ms/epoch - 136ms/step\n","Epoch 709/1000\n","5/5 - 1s - loss: 2.2868e-07 - accuracy: 1.0000 - val_loss: 0.2162 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 710/1000\n","5/5 - 1s - loss: 2.2802e-07 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 711/1000\n","5/5 - 1s - loss: 2.2740e-07 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 712/1000\n","5/5 - 1s - loss: 2.2671e-07 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9836 - 641ms/epoch - 128ms/step\n","Epoch 713/1000\n","5/5 - 1s - loss: 2.2604e-07 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9836 - 671ms/epoch - 134ms/step\n","Epoch 714/1000\n","5/5 - 1s - loss: 2.2541e-07 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 715/1000\n","5/5 - 1s - loss: 2.2473e-07 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9836 - 603ms/epoch - 121ms/step\n","Epoch 716/1000\n","5/5 - 1s - loss: 2.2408e-07 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9836 - 605ms/epoch - 121ms/step\n","Epoch 717/1000\n","5/5 - 1s - loss: 2.2343e-07 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 718/1000\n","5/5 - 1s - loss: 2.2281e-07 - accuracy: 1.0000 - val_loss: 0.2167 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 719/1000\n","5/5 - 1s - loss: 2.2213e-07 - accuracy: 1.0000 - val_loss: 0.2167 - val_accuracy: 0.9836 - 665ms/epoch - 133ms/step\n","Epoch 720/1000\n","5/5 - 1s - loss: 2.2148e-07 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 721/1000\n","5/5 - 1s - loss: 2.2086e-07 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9836 - 522ms/epoch - 104ms/step\n","Epoch 722/1000\n","5/5 - 1s - loss: 2.2024e-07 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9836 - 516ms/epoch - 103ms/step\n","Epoch 723/1000\n","5/5 - 1s - loss: 2.1956e-07 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9836 - 521ms/epoch - 104ms/step\n","Epoch 724/1000\n","5/5 - 1s - loss: 2.1893e-07 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 725/1000\n","5/5 - 1s - loss: 2.1829e-07 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 0.9836 - 909ms/epoch - 182ms/step\n","Epoch 726/1000\n","5/5 - 1s - loss: 2.1767e-07 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9836 - 1s/epoch - 211ms/step\n","Epoch 727/1000\n","5/5 - 1s - loss: 2.1702e-07 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9836 - 817ms/epoch - 163ms/step\n","Epoch 728/1000\n","5/5 - 1s - loss: 2.1640e-07 - accuracy: 1.0000 - val_loss: 0.2172 - val_accuracy: 0.9836 - 840ms/epoch - 168ms/step\n","Epoch 729/1000\n","5/5 - 1s - loss: 2.1579e-07 - accuracy: 1.0000 - val_loss: 0.2172 - val_accuracy: 0.9836 - 1s/epoch - 225ms/step\n","Epoch 730/1000\n","5/5 - 1s - loss: 2.1514e-07 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9836 - 895ms/epoch - 179ms/step\n","Epoch 731/1000\n","5/5 - 1s - loss: 2.1455e-07 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 732/1000\n","5/5 - 1s - loss: 2.1389e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 733/1000\n","5/5 - 1s - loss: 2.1328e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9836 - 941ms/epoch - 188ms/step\n","Epoch 734/1000\n","5/5 - 1s - loss: 2.1265e-07 - accuracy: 1.0000 - val_loss: 0.2175 - val_accuracy: 0.9836 - 972ms/epoch - 194ms/step\n","Epoch 735/1000\n","5/5 - 1s - loss: 2.1203e-07 - accuracy: 1.0000 - val_loss: 0.2175 - val_accuracy: 0.9836 - 768ms/epoch - 154ms/step\n","Epoch 736/1000\n","5/5 - 1s - loss: 2.1144e-07 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9836 - 792ms/epoch - 158ms/step\n","Epoch 737/1000\n","5/5 - 1s - loss: 2.1085e-07 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9836 - 788ms/epoch - 158ms/step\n","Epoch 738/1000\n","5/5 - 1s - loss: 2.1020e-07 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9836 - 770ms/epoch - 154ms/step\n","Epoch 739/1000\n","5/5 - 1s - loss: 2.0958e-07 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9836 - 741ms/epoch - 148ms/step\n","Epoch 740/1000\n","5/5 - 1s - loss: 2.0899e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9836 - 707ms/epoch - 141ms/step\n","Epoch 741/1000\n","5/5 - 1s - loss: 2.0839e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9836 - 815ms/epoch - 163ms/step\n","Epoch 742/1000\n","5/5 - 1s - loss: 2.0778e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9836 - 681ms/epoch - 136ms/step\n","Epoch 743/1000\n","5/5 - 1s - loss: 2.0716e-07 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9836 - 922ms/epoch - 184ms/step\n","Epoch 744/1000\n","5/5 - 1s - loss: 2.0662e-07 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9836 - 963ms/epoch - 193ms/step\n","Epoch 745/1000\n","5/5 - 1s - loss: 2.0599e-07 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9836 - 1s/epoch - 220ms/step\n","Epoch 746/1000\n","5/5 - 1s - loss: 2.0539e-07 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9836 - 733ms/epoch - 147ms/step\n","Epoch 747/1000\n","5/5 - 1s - loss: 2.0480e-07 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 748/1000\n","5/5 - 1s - loss: 2.0421e-07 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 749/1000\n","5/5 - 1s - loss: 2.0364e-07 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 750/1000\n","5/5 - 1s - loss: 2.0303e-07 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 751/1000\n","5/5 - 1s - loss: 2.0247e-07 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 752/1000\n","5/5 - 1s - loss: 2.0190e-07 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 753/1000\n","5/5 - 1s - loss: 2.0129e-07 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 754/1000\n","5/5 - 1s - loss: 2.0070e-07 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 755/1000\n","5/5 - 1s - loss: 2.0017e-07 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9836 - 648ms/epoch - 130ms/step\n","Epoch 756/1000\n","5/5 - 1s - loss: 1.9961e-07 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9836 - 579ms/epoch - 116ms/step\n","Epoch 757/1000\n","5/5 - 1s - loss: 1.9900e-07 - accuracy: 1.0000 - val_loss: 0.2186 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 758/1000\n","5/5 - 1s - loss: 1.9843e-07 - accuracy: 1.0000 - val_loss: 0.2186 - val_accuracy: 0.9836 - 597ms/epoch - 119ms/step\n","Epoch 759/1000\n","5/5 - 1s - loss: 1.9789e-07 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 760/1000\n","5/5 - 1s - loss: 1.9733e-07 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 761/1000\n","5/5 - 1s - loss: 1.9676e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 762/1000\n","5/5 - 1s - loss: 1.9620e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 763/1000\n","5/5 - 1s - loss: 1.9564e-07 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 764/1000\n","5/5 - 1s - loss: 1.9510e-07 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 765/1000\n","5/5 - 1s - loss: 1.9455e-07 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9836 - 575ms/epoch - 115ms/step\n","Epoch 766/1000\n","5/5 - 1s - loss: 1.9401e-07 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 767/1000\n","5/5 - 1s - loss: 1.9347e-07 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 768/1000\n","5/5 - 1s - loss: 1.9291e-07 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 769/1000\n","5/5 - 1s - loss: 1.9240e-07 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 770/1000\n","5/5 - 1s - loss: 1.9187e-07 - accuracy: 1.0000 - val_loss: 0.2192 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 771/1000\n","5/5 - 1s - loss: 1.9134e-07 - accuracy: 1.0000 - val_loss: 0.2192 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 772/1000\n","5/5 - 1s - loss: 1.9076e-07 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 773/1000\n","5/5 - 1s - loss: 1.9026e-07 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 774/1000\n","5/5 - 1s - loss: 1.8973e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 775/1000\n","5/5 - 1s - loss: 1.8916e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 776/1000\n","5/5 - 1s - loss: 1.8866e-07 - accuracy: 1.0000 - val_loss: 0.2195 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 777/1000\n","5/5 - 1s - loss: 1.8812e-07 - accuracy: 1.0000 - val_loss: 0.2195 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 778/1000\n","5/5 - 1s - loss: 1.8761e-07 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 779/1000\n","5/5 - 1s - loss: 1.8706e-07 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 780/1000\n","5/5 - 1s - loss: 1.8654e-07 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 781/1000\n","5/5 - 1s - loss: 1.8604e-07 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9836 - 611ms/epoch - 122ms/step\n","Epoch 782/1000\n","5/5 - 1s - loss: 1.8552e-07 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9836 - 595ms/epoch - 119ms/step\n","Epoch 783/1000\n","5/5 - 1s - loss: 1.8499e-07 - accuracy: 1.0000 - val_loss: 0.2198 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 784/1000\n","5/5 - 1s - loss: 1.8445e-07 - accuracy: 1.0000 - val_loss: 0.2198 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 785/1000\n","5/5 - 1s - loss: 1.8394e-07 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 786/1000\n","5/5 - 1s - loss: 1.8345e-07 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9836 - 565ms/epoch - 113ms/step\n","Epoch 787/1000\n","5/5 - 1s - loss: 1.8293e-07 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 788/1000\n","5/5 - 1s - loss: 1.8242e-07 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9836 - 653ms/epoch - 131ms/step\n","Epoch 789/1000\n","5/5 - 1s - loss: 1.8190e-07 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 0.9836 - 649ms/epoch - 130ms/step\n","Epoch 790/1000\n","5/5 - 1s - loss: 1.8141e-07 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 0.9836 - 639ms/epoch - 128ms/step\n","Epoch 791/1000\n","5/5 - 1s - loss: 1.8092e-07 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9836 - 657ms/epoch - 131ms/step\n","Epoch 792/1000\n","5/5 - 1s - loss: 1.8039e-07 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9836 - 577ms/epoch - 115ms/step\n","Epoch 793/1000\n","5/5 - 1s - loss: 1.7990e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9836 - 569ms/epoch - 114ms/step\n","Epoch 794/1000\n","5/5 - 1s - loss: 1.7940e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 795/1000\n","5/5 - 1s - loss: 1.7892e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 796/1000\n","5/5 - 1s - loss: 1.7842e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9836 - 567ms/epoch - 113ms/step\n","Epoch 797/1000\n","5/5 - 1s - loss: 1.7790e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 798/1000\n","5/5 - 1s - loss: 1.7744e-07 - accuracy: 1.0000 - val_loss: 0.2205 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 799/1000\n","5/5 - 1s - loss: 1.7697e-07 - accuracy: 1.0000 - val_loss: 0.2205 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 800/1000\n","5/5 - 1s - loss: 1.7641e-07 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9836 - 673ms/epoch - 135ms/step\n","Epoch 801/1000\n","5/5 - 1s - loss: 1.7596e-07 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9836 - 564ms/epoch - 113ms/step\n","Epoch 802/1000\n","5/5 - 1s - loss: 1.7547e-07 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 803/1000\n","5/5 - 1s - loss: 1.7500e-07 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 804/1000\n","5/5 - 1s - loss: 1.7449e-07 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 805/1000\n","5/5 - 1s - loss: 1.7401e-07 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 806/1000\n","5/5 - 1s - loss: 1.7351e-07 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9836 - 602ms/epoch - 120ms/step\n","Epoch 807/1000\n","5/5 - 1s - loss: 1.7305e-07 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 808/1000\n","5/5 - 1s - loss: 1.7256e-07 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 809/1000\n","5/5 - 1s - loss: 1.7208e-07 - accuracy: 1.0000 - val_loss: 0.2210 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 810/1000\n","5/5 - 1s - loss: 1.7161e-07 - accuracy: 1.0000 - val_loss: 0.2210 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 811/1000\n","5/5 - 1s - loss: 1.7113e-07 - accuracy: 1.0000 - val_loss: 0.2211 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 812/1000\n","5/5 - 1s - loss: 1.7067e-07 - accuracy: 1.0000 - val_loss: 0.2211 - val_accuracy: 0.9836 - 574ms/epoch - 115ms/step\n","Epoch 813/1000\n","5/5 - 1s - loss: 1.7019e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 814/1000\n","5/5 - 1s - loss: 1.6969e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 815/1000\n","5/5 - 1s - loss: 1.6922e-07 - accuracy: 1.0000 - val_loss: 0.2213 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 816/1000\n","5/5 - 1s - loss: 1.6877e-07 - accuracy: 1.0000 - val_loss: 0.2213 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 817/1000\n","5/5 - 1s - loss: 1.6829e-07 - accuracy: 1.0000 - val_loss: 0.2214 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 818/1000\n","5/5 - 1s - loss: 1.6780e-07 - accuracy: 1.0000 - val_loss: 0.2214 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 819/1000\n","5/5 - 1s - loss: 1.6734e-07 - accuracy: 1.0000 - val_loss: 0.2215 - val_accuracy: 0.9836 - 631ms/epoch - 126ms/step\n","Epoch 820/1000\n","5/5 - 1s - loss: 1.6687e-07 - accuracy: 1.0000 - val_loss: 0.2215 - val_accuracy: 0.9836 - 612ms/epoch - 122ms/step\n","Epoch 821/1000\n","5/5 - 1s - loss: 1.6640e-07 - accuracy: 1.0000 - val_loss: 0.2215 - val_accuracy: 0.9836 - 676ms/epoch - 135ms/step\n","Epoch 822/1000\n","5/5 - 1s - loss: 1.6593e-07 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9836 - 626ms/epoch - 125ms/step\n","Epoch 823/1000\n","5/5 - 1s - loss: 1.6544e-07 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9836 - 643ms/epoch - 129ms/step\n","Epoch 824/1000\n","5/5 - 1s - loss: 1.6498e-07 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9836 - 624ms/epoch - 125ms/step\n","Epoch 825/1000\n","5/5 - 1s - loss: 1.6455e-07 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9836 - 596ms/epoch - 119ms/step\n","Epoch 826/1000\n","5/5 - 1s - loss: 1.6404e-07 - accuracy: 1.0000 - val_loss: 0.2218 - val_accuracy: 0.9836 - 592ms/epoch - 118ms/step\n","Epoch 827/1000\n","5/5 - 1s - loss: 1.6360e-07 - accuracy: 1.0000 - val_loss: 0.2218 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 828/1000\n","5/5 - 1s - loss: 1.6315e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9836 - 604ms/epoch - 121ms/step\n","Epoch 829/1000\n","5/5 - 1s - loss: 1.6267e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9836 - 582ms/epoch - 116ms/step\n","Epoch 830/1000\n","5/5 - 1s - loss: 1.6221e-07 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9836 - 600ms/epoch - 120ms/step\n","Epoch 831/1000\n","5/5 - 1s - loss: 1.6174e-07 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9836 - 578ms/epoch - 116ms/step\n","Epoch 832/1000\n","5/5 - 1s - loss: 1.6132e-07 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9836 - 587ms/epoch - 117ms/step\n","Epoch 833/1000\n","5/5 - 1s - loss: 1.6085e-07 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9836 - 591ms/epoch - 118ms/step\n","Epoch 834/1000\n","5/5 - 1s - loss: 1.6040e-07 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9836 - 590ms/epoch - 118ms/step\n","Epoch 835/1000\n","5/5 - 1s - loss: 1.5994e-07 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9836 - 580ms/epoch - 116ms/step\n","Epoch 836/1000\n","5/5 - 1s - loss: 1.5951e-07 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9836 - 584ms/epoch - 117ms/step\n","Epoch 837/1000\n","5/5 - 1s - loss: 1.5905e-07 - accuracy: 1.0000 - val_loss: 0.2223 - val_accuracy: 0.9836 - 599ms/epoch - 120ms/step\n","Epoch 838/1000\n","5/5 - 1s - loss: 1.5861e-07 - accuracy: 1.0000 - val_loss: 0.2223 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 839/1000\n","5/5 - 1s - loss: 1.5817e-07 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9836 - 685ms/epoch - 137ms/step\n","Epoch 840/1000\n","5/5 - 1s - loss: 1.5770e-07 - accuracy: 1.0000 - val_loss: 0.2224 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 841/1000\n","5/5 - 1s - loss: 1.5728e-07 - accuracy: 1.0000 - val_loss: 0.2225 - val_accuracy: 0.9836 - 678ms/epoch - 136ms/step\n","Epoch 842/1000\n","5/5 - 1s - loss: 1.5680e-07 - accuracy: 1.0000 - val_loss: 0.2225 - val_accuracy: 0.9836 - 687ms/epoch - 137ms/step\n","Epoch 843/1000\n","5/5 - 1s - loss: 1.5638e-07 - accuracy: 1.0000 - val_loss: 0.2226 - val_accuracy: 0.9836 - 668ms/epoch - 134ms/step\n","Epoch 844/1000\n","5/5 - 1s - loss: 1.5592e-07 - accuracy: 1.0000 - val_loss: 0.2226 - val_accuracy: 0.9836 - 677ms/epoch - 135ms/step\n","Epoch 845/1000\n","5/5 - 1s - loss: 1.5550e-07 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9836 - 654ms/epoch - 131ms/step\n","Epoch 846/1000\n","5/5 - 1s - loss: 1.5504e-07 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9836 - 663ms/epoch - 133ms/step\n","Epoch 847/1000\n","5/5 - 1s - loss: 1.5461e-07 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 848/1000\n","5/5 - 1s - loss: 1.5419e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9836 - 698ms/epoch - 140ms/step\n","Epoch 849/1000\n","5/5 - 1s - loss: 1.5374e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9836 - 678ms/epoch - 136ms/step\n","Epoch 850/1000\n","5/5 - 1s - loss: 1.5329e-07 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 851/1000\n","5/5 - 1s - loss: 1.5287e-07 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 852/1000\n","5/5 - 1s - loss: 1.5244e-07 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9836 - 759ms/epoch - 152ms/step\n","Epoch 853/1000\n","5/5 - 1s - loss: 1.5201e-07 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9836 - 659ms/epoch - 132ms/step\n","Epoch 854/1000\n","5/5 - 1s - loss: 1.5159e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9836 - 763ms/epoch - 153ms/step\n","Epoch 855/1000\n","5/5 - 1s - loss: 1.5113e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9836 - 773ms/epoch - 155ms/step\n","Epoch 856/1000\n","5/5 - 1s - loss: 1.5072e-07 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 0.9836 - 724ms/epoch - 145ms/step\n","Epoch 857/1000\n","5/5 - 1s - loss: 1.5030e-07 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 0.9836 - 712ms/epoch - 142ms/step\n","Epoch 858/1000\n","5/5 - 1s - loss: 1.4986e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9836 - 655ms/epoch - 131ms/step\n","Epoch 859/1000\n","5/5 - 1s - loss: 1.4945e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9836 - 656ms/epoch - 131ms/step\n","Epoch 860/1000\n","5/5 - 1s - loss: 1.4902e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9836 - 691ms/epoch - 138ms/step\n","Epoch 861/1000\n","5/5 - 1s - loss: 1.4860e-07 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9836 - 684ms/epoch - 137ms/step\n","Epoch 862/1000\n","5/5 - 1s - loss: 1.4818e-07 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9836 - 660ms/epoch - 132ms/step\n","Epoch 863/1000\n","5/5 - 1s - loss: 1.4776e-07 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 0.9836 - 638ms/epoch - 128ms/step\n","Epoch 864/1000\n","5/5 - 1s - loss: 1.4734e-07 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 865/1000\n","5/5 - 1s - loss: 1.4693e-07 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.9836 - 656ms/epoch - 131ms/step\n","Epoch 866/1000\n","5/5 - 1s - loss: 1.4650e-07 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.9836 - 600ms/epoch - 120ms/step\n","Epoch 867/1000\n","5/5 - 1s - loss: 1.4609e-07 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9836 - 596ms/epoch - 119ms/step\n","Epoch 868/1000\n","5/5 - 1s - loss: 1.4568e-07 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 869/1000\n","5/5 - 1s - loss: 1.4528e-07 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9836 - 615ms/epoch - 123ms/step\n","Epoch 870/1000\n","5/5 - 1s - loss: 1.4485e-07 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 871/1000\n","5/5 - 1s - loss: 1.4443e-07 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9836 - 708ms/epoch - 142ms/step\n","Epoch 872/1000\n","5/5 - 1s - loss: 1.4402e-07 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9836 - 630ms/epoch - 126ms/step\n","Epoch 873/1000\n","5/5 - 1s - loss: 1.4361e-07 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 874/1000\n","5/5 - 1s - loss: 1.4324e-07 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 875/1000\n","5/5 - 1s - loss: 1.4280e-07 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.9836 - 573ms/epoch - 115ms/step\n","Epoch 876/1000\n","5/5 - 1s - loss: 1.4239e-07 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 877/1000\n","5/5 - 1s - loss: 1.4201e-07 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 878/1000\n","5/5 - 1s - loss: 1.4161e-07 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 879/1000\n","5/5 - 1s - loss: 1.4118e-07 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 880/1000\n","5/5 - 1s - loss: 1.4080e-07 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 881/1000\n","5/5 - 1s - loss: 1.4040e-07 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 882/1000\n","5/5 - 1s - loss: 1.3999e-07 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 883/1000\n","5/5 - 1s - loss: 1.3959e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 884/1000\n","5/5 - 1s - loss: 1.3917e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 885/1000\n","5/5 - 1s - loss: 1.3882e-07 - accuracy: 1.0000 - val_loss: 0.2245 - val_accuracy: 0.9836 - 538ms/epoch - 108ms/step\n","Epoch 886/1000\n","5/5 - 1s - loss: 1.3840e-07 - accuracy: 1.0000 - val_loss: 0.2245 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 887/1000\n","5/5 - 1s - loss: 1.3802e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9836 - 544ms/epoch - 109ms/step\n","Epoch 888/1000\n","5/5 - 1s - loss: 1.3761e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9836 - 656ms/epoch - 131ms/step\n","Epoch 889/1000\n","5/5 - 1s - loss: 1.3723e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 890/1000\n","5/5 - 1s - loss: 1.3683e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 891/1000\n","5/5 - 1s - loss: 1.3643e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 892/1000\n","5/5 - 1s - loss: 1.3607e-07 - accuracy: 1.0000 - val_loss: 0.2248 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 893/1000\n","5/5 - 1s - loss: 1.3566e-07 - accuracy: 1.0000 - val_loss: 0.2248 - val_accuracy: 0.9836 - 560ms/epoch - 112ms/step\n","Epoch 894/1000\n","5/5 - 1s - loss: 1.3527e-07 - accuracy: 1.0000 - val_loss: 0.2249 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 895/1000\n","5/5 - 1s - loss: 1.3490e-07 - accuracy: 1.0000 - val_loss: 0.2249 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 896/1000\n","5/5 - 1s - loss: 1.3450e-07 - accuracy: 1.0000 - val_loss: 0.2250 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 897/1000\n","5/5 - 1s - loss: 1.3415e-07 - accuracy: 1.0000 - val_loss: 0.2250 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 898/1000\n","5/5 - 1s - loss: 1.3376e-07 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 899/1000\n","5/5 - 1s - loss: 1.3338e-07 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 900/1000\n","5/5 - 1s - loss: 1.3299e-07 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 901/1000\n","5/5 - 1s - loss: 1.3263e-07 - accuracy: 1.0000 - val_loss: 0.2252 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 902/1000\n","5/5 - 1s - loss: 1.3225e-07 - accuracy: 1.0000 - val_loss: 0.2252 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 903/1000\n","5/5 - 1s - loss: 1.3187e-07 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 904/1000\n","5/5 - 1s - loss: 1.3151e-07 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 905/1000\n","5/5 - 1s - loss: 1.3112e-07 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 906/1000\n","5/5 - 1s - loss: 1.3077e-07 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 907/1000\n","5/5 - 1s - loss: 1.3038e-07 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 908/1000\n","5/5 - 1s - loss: 1.3000e-07 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 909/1000\n","5/5 - 1s - loss: 1.2962e-07 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 910/1000\n","5/5 - 1s - loss: 1.2926e-07 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 911/1000\n","5/5 - 1s - loss: 1.2890e-07 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9836 - 636ms/epoch - 127ms/step\n","Epoch 912/1000\n","5/5 - 1s - loss: 1.2854e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9836 - 568ms/epoch - 114ms/step\n","Epoch 913/1000\n","5/5 - 1s - loss: 1.2816e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 914/1000\n","5/5 - 1s - loss: 1.2779e-07 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 915/1000\n","5/5 - 1s - loss: 1.2744e-07 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 916/1000\n","5/5 - 1s - loss: 1.2705e-07 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 917/1000\n","5/5 - 1s - loss: 1.2670e-07 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9836 - 618ms/epoch - 124ms/step\n","Epoch 918/1000\n","5/5 - 1s - loss: 1.2634e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9836 - 602ms/epoch - 120ms/step\n","Epoch 919/1000\n","5/5 - 1s - loss: 1.2600e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9836 - 763ms/epoch - 153ms/step\n","Epoch 920/1000\n","5/5 - 1s - loss: 1.2561e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9836 - 694ms/epoch - 139ms/step\n","Epoch 921/1000\n","5/5 - 1s - loss: 1.2526e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9836 - 646ms/epoch - 129ms/step\n","Epoch 922/1000\n","5/5 - 1s - loss: 1.2492e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9836 - 608ms/epoch - 122ms/step\n","Epoch 923/1000\n","5/5 - 1s - loss: 1.2457e-07 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9836 - 585ms/epoch - 117ms/step\n","Epoch 924/1000\n","5/5 - 1s - loss: 1.2420e-07 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 925/1000\n","5/5 - 1s - loss: 1.2384e-07 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 926/1000\n","5/5 - 1s - loss: 1.2348e-07 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9836 - 543ms/epoch - 109ms/step\n","Epoch 927/1000\n","5/5 - 1s - loss: 1.2313e-07 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9836 - 570ms/epoch - 114ms/step\n","Epoch 928/1000\n","5/5 - 1s - loss: 1.2279e-07 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 929/1000\n","5/5 - 1s - loss: 1.2241e-07 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 930/1000\n","5/5 - 1s - loss: 1.2208e-07 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9836 - 670ms/epoch - 134ms/step\n","Epoch 931/1000\n","5/5 - 1s - loss: 1.2172e-07 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 932/1000\n","5/5 - 1s - loss: 1.2137e-07 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9836 - 679ms/epoch - 136ms/step\n","Epoch 933/1000\n","5/5 - 1s - loss: 1.2102e-07 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9836 - 664ms/epoch - 133ms/step\n","Epoch 934/1000\n","5/5 - 1s - loss: 1.2069e-07 - accuracy: 1.0000 - val_loss: 0.2267 - val_accuracy: 0.9836 - 644ms/epoch - 129ms/step\n","Epoch 935/1000\n","5/5 - 1s - loss: 1.2033e-07 - accuracy: 1.0000 - val_loss: 0.2267 - val_accuracy: 0.9836 - 667ms/epoch - 133ms/step\n","Epoch 936/1000\n","5/5 - 1s - loss: 1.1997e-07 - accuracy: 1.0000 - val_loss: 0.2267 - val_accuracy: 0.9836 - 702ms/epoch - 140ms/step\n","Epoch 937/1000\n","5/5 - 1s - loss: 1.1965e-07 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9836 - 614ms/epoch - 123ms/step\n","Epoch 938/1000\n","5/5 - 1s - loss: 1.1929e-07 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9836 - 593ms/epoch - 119ms/step\n","Epoch 939/1000\n","5/5 - 1s - loss: 1.1895e-07 - accuracy: 1.0000 - val_loss: 0.2269 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 940/1000\n","5/5 - 1s - loss: 1.1860e-07 - accuracy: 1.0000 - val_loss: 0.2269 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 941/1000\n","5/5 - 1s - loss: 1.1828e-07 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 942/1000\n","5/5 - 1s - loss: 1.1792e-07 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 943/1000\n","5/5 - 1s - loss: 1.1759e-07 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 944/1000\n","5/5 - 1s - loss: 1.1725e-07 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9836 - 559ms/epoch - 112ms/step\n","Epoch 945/1000\n","5/5 - 1s - loss: 1.1691e-07 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 946/1000\n","5/5 - 1s - loss: 1.1659e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 947/1000\n","5/5 - 1s - loss: 1.1624e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 948/1000\n","5/5 - 1s - loss: 1.1589e-07 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 949/1000\n","5/5 - 1s - loss: 1.1559e-07 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 950/1000\n","5/5 - 1s - loss: 1.1524e-07 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 951/1000\n","5/5 - 1s - loss: 1.1491e-07 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 952/1000\n","5/5 - 1s - loss: 1.1458e-07 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 953/1000\n","5/5 - 1s - loss: 1.1424e-07 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 954/1000\n","5/5 - 1s - loss: 1.1393e-07 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 955/1000\n","5/5 - 1s - loss: 1.1360e-07 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 956/1000\n","5/5 - 1s - loss: 1.1326e-07 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 957/1000\n","5/5 - 1s - loss: 1.1293e-07 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 958/1000\n","5/5 - 1s - loss: 1.1260e-07 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9836 - 604ms/epoch - 121ms/step\n","Epoch 959/1000\n","5/5 - 1s - loss: 1.1229e-07 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9836 - 561ms/epoch - 112ms/step\n","Epoch 960/1000\n","5/5 - 1s - loss: 1.1196e-07 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 961/1000\n","5/5 - 1s - loss: 1.1164e-07 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 962/1000\n","5/5 - 1s - loss: 1.1131e-07 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 963/1000\n","5/5 - 1s - loss: 1.1099e-07 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 964/1000\n","5/5 - 1s - loss: 1.1066e-07 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 965/1000\n","5/5 - 1s - loss: 1.1035e-07 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 966/1000\n","5/5 - 1s - loss: 1.1003e-07 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 967/1000\n","5/5 - 1s - loss: 1.0970e-07 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9836 - 557ms/epoch - 111ms/step\n","Epoch 968/1000\n","5/5 - 1s - loss: 1.0938e-07 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 969/1000\n","5/5 - 1s - loss: 1.0908e-07 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 970/1000\n","5/5 - 1s - loss: 1.0876e-07 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 971/1000\n","5/5 - 1s - loss: 1.0843e-07 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9836 - 553ms/epoch - 111ms/step\n","Epoch 972/1000\n","5/5 - 1s - loss: 1.0811e-07 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9836 - 554ms/epoch - 111ms/step\n","Epoch 973/1000\n","5/5 - 1s - loss: 1.0782e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9836 - 566ms/epoch - 113ms/step\n","Epoch 974/1000\n","5/5 - 1s - loss: 1.0749e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9836 - 547ms/epoch - 109ms/step\n","Epoch 975/1000\n","5/5 - 1s - loss: 1.0719e-07 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 976/1000\n","5/5 - 1s - loss: 1.0687e-07 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 977/1000\n","5/5 - 1s - loss: 1.0655e-07 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 978/1000\n","5/5 - 1s - loss: 1.0625e-07 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9836 - 537ms/epoch - 107ms/step\n","Epoch 979/1000\n","5/5 - 1s - loss: 1.0594e-07 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9836 - 548ms/epoch - 110ms/step\n","Epoch 980/1000\n","5/5 - 1s - loss: 1.0564e-07 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9836 - 571ms/epoch - 114ms/step\n","Epoch 981/1000\n","5/5 - 1s - loss: 1.0532e-07 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9836 - 589ms/epoch - 118ms/step\n","Epoch 982/1000\n","5/5 - 1s - loss: 1.0502e-07 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9836 - 551ms/epoch - 110ms/step\n","Epoch 983/1000\n","5/5 - 1s - loss: 1.0470e-07 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9836 - 545ms/epoch - 109ms/step\n","Epoch 984/1000\n","5/5 - 1s - loss: 1.0440e-07 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 985/1000\n","5/5 - 1s - loss: 1.0410e-07 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9836 - 563ms/epoch - 113ms/step\n","Epoch 986/1000\n","5/5 - 1s - loss: 1.0380e-07 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9836 - 607ms/epoch - 121ms/step\n","Epoch 987/1000\n","5/5 - 1s - loss: 1.0348e-07 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9836 - 622ms/epoch - 124ms/step\n","Epoch 988/1000\n","5/5 - 1s - loss: 1.0320e-07 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9836 - 555ms/epoch - 111ms/step\n","Epoch 989/1000\n","5/5 - 1s - loss: 1.0290e-07 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 990/1000\n","5/5 - 1s - loss: 1.0259e-07 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9836 - 550ms/epoch - 110ms/step\n","Epoch 991/1000\n","5/5 - 1s - loss: 1.0231e-07 - accuracy: 1.0000 - val_loss: 0.2292 - val_accuracy: 0.9836 - 549ms/epoch - 110ms/step\n","Epoch 992/1000\n","5/5 - 1s - loss: 1.0201e-07 - accuracy: 1.0000 - val_loss: 0.2292 - val_accuracy: 0.9836 - 661ms/epoch - 132ms/step\n","Epoch 993/1000\n","5/5 - 1s - loss: 1.0172e-07 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9836 - 562ms/epoch - 112ms/step\n","Epoch 994/1000\n","5/5 - 1s - loss: 1.0143e-07 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9836 - 546ms/epoch - 109ms/step\n","Epoch 995/1000\n","5/5 - 1s - loss: 1.0113e-07 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9836 - 552ms/epoch - 110ms/step\n","Epoch 996/1000\n","5/5 - 1s - loss: 1.0084e-07 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 0.9836 - 542ms/epoch - 108ms/step\n","Epoch 997/1000\n","5/5 - 1s - loss: 1.0055e-07 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 0.9836 - 556ms/epoch - 111ms/step\n","Epoch 998/1000\n","5/5 - 1s - loss: 1.0025e-07 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9836 - 558ms/epoch - 112ms/step\n","Epoch 999/1000\n","5/5 - 1s - loss: 9.9967e-08 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9836 - 598ms/epoch - 120ms/step\n","Epoch 1000/1000\n","5/5 - 1s - loss: 9.9679e-08 - accuracy: 1.0000 - val_loss: 0.2296 - val_accuracy: 0.9836 - 754ms/epoch - 151ms/step\n"]}],"source":["GRU_history = GRU_model.fit(training_padded,\n"," training_labels,\n"," epochs=num_epochs, \n"," validation_data = (testing_padded, testing_labels),\n"," verbose=2, callbacks=[early_stop])"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Graphs for Accuracy, Loss of each Model**"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":881},"executionInfo":{"elapsed":1109,"status":"ok","timestamp":1684908028090,"user":{"displayName":"JIMUEL SERVANDIL","userId":"05666250040439956892"},"user_tz":-480},"id":"enjwvuWQzHGp","outputId":"a990eb79-9e10-4db7-c1dd-0d159038e98d"},"outputs":[{"data":{"image/png":"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","text/plain":["
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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# BiLSTM History\n","def plot_graphs(biLSTM_history, string):\n"," plt.plot(biLSTM_history.history[string])\n"," plt.plot(biLSTM_history.history['val_'+string])\n"," plt.xlabel(\"Epochs\")\n"," plt.ylabel(string)\n"," plt.legend([string, 'val_'+string])\n"," plt.show()\n","\n","plot_graphs(biLSTM_history, \"accuracy\")\n","plot_graphs(biLSTM_history, \"loss\")"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":881},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1684908033133,"user":{"displayName":"JIMUEL SERVANDIL","userId":"05666250040439956892"},"user_tz":-480},"id":"kifEPzXdUYgE","outputId":"db379a4b-a628-4cd6-dbb7-23d091772ec7"},"outputs":[{"data":{"image/png":"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","text/plain":["
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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# LSTM History\n","def plot_graphs(LSTM_history, string):\n"," plt.plot(LSTM_history.history[string])\n"," plt.plot(LSTM_history.history['val_'+string])\n"," plt.xlabel(\"Epochs\")\n"," plt.ylabel(string)\n"," plt.legend([string, 'val_'+string])\n"," plt.show()\n","\n","plot_graphs(LSTM_history, \"accuracy\")\n","plot_graphs(LSTM_history, \"loss\")"]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":881},"executionInfo":{"elapsed":2904,"status":"ok","timestamp":1684908038024,"user":{"displayName":"JIMUEL SERVANDIL","userId":"05666250040439956892"},"user_tz":-480},"id":"i5WQJt0pUbeV","outputId":"eee087ce-e551-48f6-c51e-e6da0aed2238"},"outputs":[{"data":{"image/png":"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","text/plain":["
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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# GRU History\n","def plot_graphs(GRU_history, string):\n"," plt.plot(GRU_history.history[string])\n"," plt.plot(GRU_history.history['val_'+string])\n"," plt.xlabel(\"Epochs\")\n"," plt.ylabel(string)\n"," plt.legend([string, 'val_'+string])\n"," plt.show()\n","\n","plot_graphs(GRU_history, \"accuracy\")\n","plot_graphs(GRU_history, \"loss\")"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"urznhB0MztZQ"},"outputs":[],"source":["biLSTM_e = biLSTM_model.layers[0]\n","LSTM_e = LSTM_model.layers[0]\n","GRU_e = GRU_model.layers[0]\n","\n","biLSTM_weights = biLSTM_e.get_weights()[0]\n","LSTM_weights = LSTM_e.get_weights()[0]\n","GRU_weights = GRU_e.get_weights()[0]\n","\n","threshold=0.5"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Save to H5 file**"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"idvAWSEIS_1G"},"outputs":[],"source":["# Save to H5\n","biLSTM_model.save(\"biLSTM_model.h5\")\n","LSTM_model.save(\"LSTM_model.h5\")\n","GRU_model.save(\"GRU_model.h5\")\n","\n","# Load saved model\n","from tensorflow.keras.models import load_model\n","\n","biLSTM_model = load_model(\"biLSTM_model.h5\")\n","LSTM_model = load_model(\"LSTM_model.h5\")\n","GRU_model = load_model(\"GRU_model.h5\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Evaluate, Accuracy and Loss**"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2813,"status":"ok","timestamp":1684908072483,"user":{"displayName":"JIMUEL SERVANDIL","userId":"05666250040439956892"},"user_tz":-480},"id":"3LwjNHFeS_1G","outputId":"e17e593f-6473-4094-d490-ce9eb4ff9a3b"},"outputs":[{"name":"stdout","output_type":"stream","text":["2/2 [==============================] - 3s 148ms/step - loss: 0.2448 - accuracy: 0.9836\n","2/2 [==============================] - 1s 46ms/step - loss: 0.2035 - accuracy: 0.9836\n","2/2 [==============================] - 1s 78ms/step - loss: 0.2296 - accuracy: 0.9836\n","biLSTM Accuracy: 0.9836065769195557\n","LSTM Accuracy: 0.9836065769195557\n","GRU Accuracy: 0.9836065769195557\n"]}],"source":["biLSTM_loss, biLSTM_accuracy = biLSTM_model.evaluate(testing_padded, testing_labels)\n","LSTM_loss, LSTM_accuracy = LSTM_model.evaluate(testing_padded, testing_labels)\n","GRU_loss, GRU_accuracy = GRU_model.evaluate(testing_padded, testing_labels)\n","\n","# Print the accuracy scores\n","print(\"biLSTM Accuracy:\", biLSTM_accuracy)\n","print(\"LSTM Accuracy:\", LSTM_accuracy)\n","print(\"GRU Accuracy:\", GRU_accuracy)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["**Function to classify texts**"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[],"source":["def predict(sentence, tokenizer, max_length, padding_type, trunc_type, biLSTM_model, LSTM_model, GRU_model, threshold):\n"," sequences = tokenizer.texts_to_sequences(sentence)\n"," padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n"," biLSTM_pred = biLSTM_model.predict(padded)\n"," LSTM_pred = LSTM_model.predict(padded)\n"," GRU_pred = GRU_model.predict(padded)\n","\n"," # If greater than threshold positive, otherwise negative\n"," for i, sentence in enumerate(sentence):\n"," biLSTM_label = \"Positive\" if biLSTM_pred[i][0] > threshold else \"Negative\"\n"," LSTM_label = \"Positive\" if LSTM_pred[i][0] > threshold else \"Negative\"\n"," GRU_label = \"Positive\" if GRU_pred[i][0] > threshold else \"Negative\"\n","\n"," # Print Predictions\n"," print(\"Classification\")\n"," print(f\"Sentence: {sentence}\")\n","\n"," print(f\"BILSTM Class: {biLSTM_label}\")\n"," print(f\"BILSTM Probability: {biLSTM_pred[i][0]:.9f}\")\n"," print()\n","\n"," print(f\"LSTM Class: {LSTM_label}\")\n"," print(f\"LSTM Probability: {LSTM_pred[i][0]:.9f}\")\n"," print()\n","\n"," print(f\"GRU Class: {GRU_label}\")\n"," print(f\"GRU Probability: {GRU_pred[i][0]:.9f}\")\n"," print()\n"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6424,"status":"ok","timestamp":1684909135059,"user":{"displayName":"JIMUEL SERVANDIL","userId":"05666250040439956892"},"user_tz":-480},"id":"y1fxWzEhz9u4","outputId":"21a97fe6-90c0-4ce8-dac9-730aa9f4b73d"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 251ms/step\n","1/1 [==============================] - 0s 57ms/step\n","1/1 [==============================] - 0s 66ms/step\n","Classification\n","Sentence: bad service\n","BILSTM Class: Negative\n","BILSTM Probability: 0.000000146\n","\n","LSTM Class: Negative\n","LSTM Probability: 0.000001303\n","\n","GRU Class: Negative\n","GRU Probability: 0.000000409\n","\n"]}],"source":["sentence = input(\"Enter sentences separated by commas: \")\n","sentence = sentence.split(',')\n","\n","predict(sentence, tokenizer, max_length, padding_type, trunc_type, biLSTM_model, LSTM_model, GRU_model, threshold)\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pickle\n","\n","pickle.dump(tokenizer, open('tokenizer.pkl', 'wb'))\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pickle\n","import json\n","\n","\n","# Save the other values to a JSON file\n","data = {\n"," 'max_length': max_length,\n"," 'padding_type': padding_type,\n"," 'trunc_type': trunc_type,\n"," 'threshold': threshold\n","}\n","\n","with open('data.json', 'w') as file:\n"," json.dump(data, file)\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.2"}},"nbformat":4,"nbformat_minor":0}