save whole model

stock_predictor.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOc5/oQ0Z2ie5dOI46PpyV0",
+      "authorship_tag": "ABX9TyO1k4BL8RYqJqf+FTqQWuh+",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -33,7 +33,7 @@
       "source": [
         "from google.colab import drive\n",
         "drive.mount('/content/drive')\n",
-        "project_path = '/content/drive/MyDrive/projects/Stock_Predicter'\n",
+        "project_path = '/content/drive/MyDrive/projects/Stock_Predicter/v1'\n",
         "%cd $project_path"
       ],
       "metadata": {
@@ -41,23 +41,23 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Xr3Qozgfktoc",
-        "outputId": "b4ce9a19-4dc1-43e9-b09e-af91a2b07343"
+        "outputId": "7d93f2be-afa1-402c-ec77-24e5d1cf5f8e"
       },
-      "execution_count": 90,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
-            "/content/drive/MyDrive/projects/Stock_Predicter\n"
+            "Mounted at /content/drive\n",
+            "/content/drive/MyDrive/projects/Stock_Predicter/v1\n"
           ]
         }
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 91,
+      "execution_count": 2,
       "metadata": {
         "id": "e8SQqogMQYLh"
       },
@@ -99,7 +99,7 @@
       "metadata": {
         "id": "O6dtJpJwS5Eg"
       },
-      "execution_count": 92,
+      "execution_count": 3,
       "outputs": []
     },
     {
@@ -113,9 +113,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "LwPyk8Uh-Zz_",
-        "outputId": "2217df50-87e9-48e3-e096-71163331f570"
+        "outputId": "5a636265-2f20-46ad-bc0d-30adbf2b630b"
       },
-      "execution_count": 93,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "stream",
@@ -150,7 +150,7 @@
       "metadata": {
         "id": "Bpym8x-Kxf0p"
       },
-      "execution_count": 94,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -192,7 +192,7 @@
       "metadata": {
         "id": "v9RoqzBvtrOb"
       },
-      "execution_count": 95,
+      "execution_count": 6,
       "outputs": []
     },
     {
@@ -203,10 +203,12 @@
         "  \n",
         "  x_train_list = []\n",
         "  y_train_list = []\n",
+        "  print('shape norm_data')\n",
+        "  print(norm_data.shape)\n",
         "  \n",
         "  for i in range(prediction_days, len(norm_data)):\n",
-        "    x_train_list.append(norm_data[i-prediction_days:i])\n",
-        "    y_train_list.append(norm_data.iloc[i].values[0:4])\n",
+        "    x_train_list.append(norm_data.iloc[i-prediction_days:i])\n",
+        "    y_train_list.append(norm_data.iloc[i-prediction_days+1:i+1,0:4])\n",
         "  \n",
         "  x_train = np.array(x_train_list)\n",
         "  y_train = np.array(y_train_list)\n",
@@ -215,9 +217,40 @@
       "metadata": {
         "id": "jMXkRAYFomHM"
       },
-      "execution_count": 96,
+      "execution_count": 7,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.random.randint(5, size=(5,6))\n",
+        "print(x)\n",
+        "print(x[2:4, 0:4])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TX8fsigbOOzE",
+        "outputId": "a5e4e0f3-8814-4e28-d5e2-33f4e80954b5"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0 3 4 2 4 1]\n",
+            " [4 4 1 0 3 4]\n",
+            " [0 3 3 3 2 0]\n",
+            " [3 1 3 3 0 4]\n",
+            " [1 0 0 1 0 3]]\n",
+            "[[0 3 3 3]\n",
+            " [3 1 3 3]]\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -236,7 +269,7 @@
       "metadata": {
         "id": "YZWMfusT-I7Z"
       },
-      "execution_count": 97,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -249,16 +282,18 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "PeJjDC0VBG_6",
-        "outputId": "aff2cf0b-e630-4727-bbec-3cf5be4e53a0"
+        "outputId": "ec7bf037-3d03-47b5-b97b-43e19b9ac9ea"
       },
-      "execution_count": 98,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "max_value:  10.589996337890625\n",
-            "max_volume:  1460852400.0\n"
+            "max_volume:  1460852400.0\n",
+            "shape norm_data\n",
+            "(2582, 6)\n"
           ]
         }
       ]
@@ -267,72 +302,23 @@
       "cell_type": "code",
       "source": [
         "print(x_train.shape)\n",
-        "x_train[1,499,:]"
+        "print(y_train.shape)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "YkI8vSguuS8A",
-        "outputId": "6e5eeaa2-12de-4dd2-b17c-1d61e415fbd8"
-      },
-      "execution_count": 99,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(2082, 500, 6)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([ 0.00212456,  0.05712934, -0.00212456,  0.04461756, -0.22778379,\n",
-              "        0.09233239])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 99
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "td = data.iloc[498:501]\n",
-        "# print('td:\\n',td)\n",
-        "td0 = create_remove_columns(td)\n",
-        "print('td0:\\n',td0)\n",
-        "print(decoder(y_train[0]))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "QaO34uSds2wJ",
-        "outputId": "af5d9a04-214c-4a2d-c706-5af3a2a1ea5a"
+        "outputId": "51e71e32-7e5b-4686-8e01-872ea97f976f"
       },
-      "execution_count": 100,
+      "execution_count": 11,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "td0:\n",
-            "                 Jump       Open       High        Low      Close  Adj Close  \\\n",
-            "Date                                                                          \n",
-            "2014-12-23  0.000000  28.307501  28.332500  28.115000  28.135000  25.286961   \n",
-            "2014-12-24  0.010000  28.145000  28.177500  28.002501  28.002501  25.167873   \n",
-            "2014-12-26  0.022499  28.025000  28.629999  28.002501  28.497499  25.612770   \n",
-            "\n",
-            "               Volume  \n",
-            "Date                   \n",
-            "2014-12-23  104113600  \n",
-            "2014-12-24   57918400  \n",
-            "2014-12-26  134884000  \n",
-            "[ 0.02249908  0.60499954 -0.02249908  0.47249985]\n"
+            "(2082, 500, 6)\n",
+            "(2082, 500, 4)\n"
           ]
         }
       ]
@@ -360,14 +346,12 @@
       "source": [
         "def create_model():\n",
         "  model = Sequential()\n",
-        "  # model.add(LSTM(units=112, return_sequences=True, input_shape=(x_train.shape[1:])))\n",
-        "  model.add(LSTM(units=1000, return_sequences=True, input_shape=(None,x_train.shape[-1],)))\n",
-        "  model.add(Dropout(0.2))\n",
-        "  model.add(LSTM(units=1000, return_sequences=True))\n",
-        "  model.add(Dropout(0.2))\n",
-        "  model.add(LSTM(units=1000))\n",
-        "  model.add(Dropout(0.2))\n",
-        "  model.add(Dense(units=4))\n",
+        "  model.add(LSTM(units=1024, return_sequences=True, input_shape=(None,x_train.shape[-1],)))\n",
+        "  # model.add(Dropout(0.2))\n",
+        "  model.add(LSTM(units=1024, return_sequences=True))\n",
+        "  # model.add(Dropout(0.2))\n",
+        "  model.add(LSTM(units=1024, return_sequences=True))\n",
+        "  model.add(Dense(4))\n",
         "  return model\n",
         "\n",
         "model = create_model()\n",
@@ -378,35 +362,29 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "GXhYAKzXVfku",
-        "outputId": "bbf96ec2-84fc-4246-9003-32c2f8083bb6"
+        "outputId": "0a471170-8a4b-416e-874c-ec5061a21744"
       },
-      "execution_count": 101,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Model: \"sequential_2\"\n",
+            "Model: \"sequential\"\n",
             "_________________________________________________________________\n",
             " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            " lstm_6 (LSTM)               (None, None, 1000)        4028000   \n",
-            "                                                                 \n",
-            " dropout_6 (Dropout)         (None, None, 1000)        0         \n",
+            " lstm (LSTM)                 (None, None, 1024)        4222976   \n",
             "                                                                 \n",
-            " lstm_7 (LSTM)               (None, None, 1000)        8004000   \n",
+            " lstm_1 (LSTM)               (None, None, 1024)        8392704   \n",
             "                                                                 \n",
-            " dropout_7 (Dropout)         (None, None, 1000)        0         \n",
+            " lstm_2 (LSTM)               (None, None, 1024)        8392704   \n",
             "                                                                 \n",
-            " lstm_8 (LSTM)               (None, 1000)              8004000   \n",
-            "                                                                 \n",
-            " dropout_8 (Dropout)         (None, 1000)              0         \n",
-            "                                                                 \n",
-            " dense_2 (Dense)             (None, 4)                 4004      \n",
+            " dense (Dense)               (None, None, 4)           4100      \n",
             "                                                                 \n",
             "=================================================================\n",
-            "Total params: 20,040,004\n",
-            "Trainable params: 20,040,004\n",
+            "Total params: 21,012,484\n",
+            "Trainable params: 21,012,484\n",
             "Non-trainable params: 0\n",
             "_________________________________________________________________\n",
             "None\n"
@@ -417,12 +395,25 @@
     {
       "cell_type": "code",
       "source": [
-        "model.compile(optimizer='adam', loss='mean_squared_error')"
+        "# model.compile(optimizer='adam', loss='mean_squared_error')\n",
+        "model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())"
       ],
       "metadata": {
         "id": "ZhoWj_XeXQws"
       },
-      "execution_count": 102,
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if False:\n",
+        "  model.load_weights('./training_checkpoints_20230408062729/ckpt_epoch24_loss0.00024580140598118305')"
+      ],
+      "metadata": {
+        "id": "jYCXXkRZraaF"
+      },
+      "execution_count": 14,
       "outputs": []
     },
     {
@@ -445,16 +436,16 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "HDT9XPXHvqyN",
-        "outputId": "6bc3a3e9-a7ae-48ff-e64a-fb529c5e1f75"
+        "outputId": "041baee8-f87c-47aa-af07-8fb6dc56186f"
       },
-      "execution_count": 103,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "(2082, 500, 6)\n",
-            "(2082, 4)\n"
+            "(2082, 500, 4)\n"
           ]
         }
       ]
@@ -463,97 +454,28 @@
       "cell_type": "code",
       "source": [
         "# Change to False to avoid trainging the model\n",
-        "# if False:\n",
-        "if True:\n",
+        "to_train = True\n",
+        "if to_train:\n",
+        "# if True:\n",
         "  # Directory where the checkpoints will be saved\n",
         "  checkpoint_dir = './training_checkpoints_'+dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
         "  # Name of the checkpoint files\n",
-        "  checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_epoch{epoch}_loss{loss}\")\n",
+        "  # checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_epoch{epoch}_loss{loss}\")\n",
+        "  checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
         "  \n",
         "  checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
         "      filepath=checkpoint_prefix,\n",
         "      save_weights_only=True,\n",
         "      monitor=\"loss\", mode=\"min\",\n",
         "      save_best_only=True)\n",
+        "\n",
         "  model.fit(x_train, y_train, epochs=25, batch_size=32, callbacks=[checkpoint_callback])\n"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "9Ccc_Ej2TmYO",
-        "outputId": "4e7fe210-6cbb-4a9d-f856-829cfa6bced5"
+        "id": "9Ccc_Ej2TmYO"
       },
-      "execution_count": 104,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/25\n",
-            "66/66 [==============================] - 58s 773ms/step - loss: 0.0125\n",
-            "Epoch 2/25\n",
-            "66/66 [==============================] - 54s 816ms/step - loss: 0.0115\n",
-            "Epoch 3/25\n",
-            "66/66 [==============================] - 55s 841ms/step - loss: 0.0113\n",
-            "Epoch 4/25\n",
-            "66/66 [==============================] - 56s 845ms/step - loss: 0.0114\n",
-            "Epoch 5/25\n",
-            "66/66 [==============================] - 57s 859ms/step - loss: 0.0113\n",
-            "Epoch 6/25\n",
-            "66/66 [==============================] - 58s 886ms/step - loss: 0.0112\n",
-            "Epoch 7/25\n",
-            "66/66 [==============================] - 59s 889ms/step - loss: 0.0112\n",
-            "Epoch 8/25\n",
-            "66/66 [==============================] - 59s 890ms/step - loss: 0.0111\n",
-            "Epoch 9/25\n",
-            "66/66 [==============================] - 58s 875ms/step - loss: 0.0112\n",
-            "Epoch 10/25\n",
-            "66/66 [==============================] - 58s 880ms/step - loss: 0.0112\n",
-            "Epoch 11/25\n",
-            "66/66 [==============================] - 58s 881ms/step - loss: 0.0111\n",
-            "Epoch 12/25\n",
-            "66/66 [==============================] - 59s 892ms/step - loss: 0.0111\n",
-            "Epoch 13/25\n",
-            "66/66 [==============================] - 59s 895ms/step - loss: 0.0110\n",
-            "Epoch 14/25\n",
-            "66/66 [==============================] - 58s 880ms/step - loss: 0.0111\n",
-            "Epoch 15/25\n",
-            "66/66 [==============================] - 58s 882ms/step - loss: 0.0111\n",
-            "Epoch 16/25\n",
-            "66/66 [==============================] - 59s 896ms/step - loss: 0.0110\n",
-            "Epoch 17/25\n",
-            "66/66 [==============================] - 58s 882ms/step - loss: 0.0112\n",
-            "Epoch 18/25\n",
-            "66/66 [==============================] - 58s 882ms/step - loss: 0.0110\n",
-            "Epoch 19/25\n",
-            "66/66 [==============================] - 58s 882ms/step - loss: 0.0111\n",
-            "Epoch 20/25\n",
-            "24/66 [=========>....................] - ETA: 37s - loss: 0.0099"
-          ]
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-104-78bd1a1c9ef9>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m       \u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"loss\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"min\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m       save_best_only=True)\n\u001b[0;32m---> 14\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint_callback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1683\u001b[0m                         ):\n\u001b[1;32m   1684\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1686\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1687\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    924\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    927\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m       (concrete_function,\n\u001b[1;32m    142\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    144\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1756\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1758\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1759\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    382\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+      "execution_count": 16,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -571,20 +493,32 @@
         "!ls -ld training_checkpoints_*/"
       ],
       "metadata": {
-        "id": "59CDDB0i4yTx"
+        "id": "59CDDB0i4yTx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fa0788e1-b75e-45dc-a95d-ec3e24d00d70"
       },
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "drwx------ 2 root root 4096 Apr  8 07:34 training_checkpoints_20230408073359/\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
       "source": [
-        "test_model = create_model()"
+        "model = create_model()"
       ],
       "metadata": {
         "id": "tpmru7nG9kbW"
       },
-      "execution_count": 105,
+      "execution_count": 18,
       "outputs": []
     },
     {
@@ -594,9 +528,7 @@
         "try:\n",
         "  checkpoint_dir\n",
         "except NameError: \n",
-        "  checkpoint_dir = './training_checkpoints_20230406214431'\n",
-        "\n",
-        "print(checkpoint_dir)\n",
+        "  checkpoint_dir = 'training_checkpoints_20230408073359'\n",
         "\n",
         "def load_weights(epoch=None):\n",
         "  if epoch is None:\n",
@@ -607,27 +539,44 @@
         "        if re.search(f'^ckpt_epoch{epoch}_.*\\.index', entry.name):\n",
         "          weights_file = checkpoint_dir + '/'+ entry.name[:-6]\n",
         "\n",
+        "  print('weights_file')\n",
         "  print(weights_file)\n",
-        "  test_model.load_weights(weights_file)\n",
-        "  return test_model\n",
+        "  model.load_weights(weights_file)\n",
+        "  return model\n",
         "\n",
-        "test_model = load_weights()"
+        "model = load_weights(epoch=None)\n",
+        "model_filepath = 'saved_model'\n",
+        "model.save(model_filepath)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "wQ0JTXsp4VKF",
-        "outputId": "2b25d414-f188-4c14-ef43-f7af5566a3be"
+        "outputId": "57440f25-6468-404c-f0a6-e99f004f9e67"
       },
-      "execution_count": 107,
+      "execution_count": 28,
       "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+          ]
+        },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "./training_checkpoints_20230406230143\n",
-            "./training_checkpoints_20230406230143/ckpt_epoch16_loss0.01097947172820568\n"
+            "weights_file\n",
+            "training_checkpoints_20230408073359/ckpt\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:Found untraced functions such as lstm_cell_3_layer_call_fn, lstm_cell_3_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn, lstm_cell_4_layer_call_and_return_conditional_losses, lstm_cell_5_layer_call_fn while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
           ]
         }
       ]
@@ -635,8 +584,9 @@
     {
       "cell_type": "code",
       "source": [
-        "test_start = dt.datetime(2013,1,1)\n",
-        "end = dt.datetime(2023,4,5)\n",
+        "test_start = dt.datetime(2016,1,1)\n",
+        "test_end = dt.datetime(2023,4,5)\n",
+        "ticker = 'AAPL'\n",
         "\n",
         "yfin.pdr_override()\n",
         "test_data = web.data.get_data_yahoo(ticker, test_start, test_end)"
@@ -646,9 +596,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Mf4q97pfaSCA",
-        "outputId": "7355b53f-c879-4296-d24a-bb8bd739e5d8"
+        "outputId": "5682a5dd-6968-4098-c203-31bc6c14e47c"
       },
-      "execution_count": 114,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "stream",
@@ -659,13 +609,37 @@
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "load_whole_model = False\n",
+        "if load_whole_model:\n",
+        "  model = tf.keras.models.load_model(model_filepath)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uloiDJXRbrJs",
+        "outputId": "a3aeb021-5555-413f-eb40-cce7ebe60ed5"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
         "# def close_tester(model, test_data, scaler=None):\n",
-        "model = test_model\n",
         "scaler = scaler\n",
-        "test_x_train, test_y_train, _, _ = preprocessing(data, scaler=scaler)\n",
+        "test_x_train, test_y_train, _, _ = preprocessing(test_data, scaler=scaler)\n",
         "print(test_x_train.shape)\n",
         "print(test_y_train.shape)\n",
         "results = model.predict(test_x_train)\n",
@@ -678,9 +652,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "MqCeMf3UoxZm",
-        "outputId": "a0591f06-f804-41e9-b973-627a7693ff89"
+        "outputId": "1a448123-6253-4585-c7af-5ff7491c1628"
       },
-      "execution_count": 115,
+      "execution_count": 30,
       "outputs": [
         {
           "output_type": "stream",
@@ -688,9 +662,11 @@
           "text": [
             "max_value:  10.589996337890625\n",
             "max_volume:  1460852400.0\n",
-            "(2082, 500, 6)\n",
-            "(2082, 4)\n",
-            "66/66 [==============================] - 18s 275ms/step\n"
+            "shape norm_data\n",
+            "(1826, 6)\n",
+            "(1326, 500, 6)\n",
+            "(1326, 500, 4)\n",
+            "42/42 [==============================] - 12s 270ms/step\n"
           ]
         }
       ]
@@ -705,7 +681,7 @@
         "for result, expected in zip(results[:], test_y_train[:]):\n",
         "  # print(result)\n",
         "  # print(expected)\n",
-        "  comparer = result[3] * expected[3]\n",
+        "  comparer = result[-1][3] * expected[-1][3]\n",
         "  if comparer > 0:\n",
         "    right_counter += 1\n",
         "  elif comparer == 0:\n",
@@ -726,18 +702,18 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "AVYFQZnqEqhx",
-        "outputId": "7353f76c-f2e6-4a48-ba31-74ab67bb73ea"
+        "outputId": "8110f58a-7344-42b0-ed9a-ba77a4999e30"
       },
-      "execution_count": 120,
+      "execution_count": 31,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "right_counter : 1118\n",
+            "right_counter : 1300\n",
             "no_action_counter : 0\n",
-            "wrong_counter : 959\n",
-            "success rate: 53.6983669548511%\n"
+            "wrong_counter : 22\n",
+            "success rate: 98.03921568627452%\n"
           ]
         }
       ]
@@ -752,25 +728,25 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "gyhzy_l6sAvi",
-        "outputId": "78f5d2cf-cd21-47b1-b58e-6b3321a802bd"
+        "outputId": "6bd3c88a-b61b-44e8-d16d-0880a17ebab2"
       },
-      "execution_count": 123,
+      "execution_count": 32,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "Open         2.802500e+01\n",
-              "High         2.863000e+01\n",
-              "Low          2.800250e+01\n",
-              "Close        2.849750e+01\n",
-              "Adj Close    2.561277e+01\n",
-              "Volume       1.348840e+08\n",
-              "Name: 2014-12-26 00:00:00, dtype: float64"
+              "Open         4.252500e+01\n",
+              "High         4.269500e+01\n",
+              "Low          4.242750e+01\n",
+              "Close        4.265000e+01\n",
+              "Adj Close    4.049405e+01\n",
+              "Volume       8.599280e+07\n",
+              "Name: 2017-12-27 00:00:00, dtype: float64"
             ]
           },
           "metadata": {},
-          "execution_count": 123
+          "execution_count": 32
         }
       ]
     }