{
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 157 images belonging to 3 classes.\n",
      "Found 58 images belonging to 3 classes.\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:43:10.692611: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - ETA: 0s - loss: 1.2917 - accuracy: 0.6306"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:43:14.403495: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 5s 823ms/step - loss: 1.2917 - accuracy: 0.6306 - val_loss: 0.7088 - val_accuracy: 0.7414\n",
      "Epoch 2/10\n",
      "5/5 [==============================] - 3s 625ms/step - loss: 0.6134 - accuracy: 0.8344 - val_loss: 0.0587 - val_accuracy: 0.9828\n",
      "Epoch 3/10\n",
      "5/5 [==============================] - 3s 694ms/step - loss: 0.0851 - accuracy: 0.9682 - val_loss: 0.1311 - val_accuracy: 0.9310\n",
      "Epoch 4/10\n",
      "5/5 [==============================] - 3s 688ms/step - loss: 0.1061 - accuracy: 0.9427 - val_loss: 0.0191 - val_accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "5/5 [==============================] - 3s 680ms/step - loss: 0.0302 - accuracy: 0.9936 - val_loss: 0.0478 - val_accuracy: 0.9828\n",
      "Epoch 6/10\n",
      "5/5 [==============================] - 3s 684ms/step - loss: 0.0363 - accuracy: 0.9936 - val_loss: 0.0378 - val_accuracy: 0.9828\n",
      "Epoch 7/10\n",
      "5/5 [==============================] - 3s 660ms/step - loss: 0.0126 - accuracy: 0.9936 - val_loss: 0.0217 - val_accuracy: 0.9828\n",
      "Epoch 8/10\n",
      "5/5 [==============================] - 3s 611ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.0092 - val_accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "5/5 [==============================] - 3s 683ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "5/5 [==============================] - 3s 631ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:43:45.348055: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 289ms/step - loss: 0.0051 - accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n",
      "WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss after Transfer Learning: 0.005056549794971943\n",
      "Validation Accuracy after Transfer Learning: 1.0\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:43:46.612117: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - ETA: 0s - loss: 0.1052 - accuracy: 0.9554"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:43:53.429648: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 8s 1s/step - loss: 0.1052 - accuracy: 0.9554 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
      "Epoch 2/10\n",
      "5/5 [==============================] - 6s 1s/step - loss: 0.1112 - accuracy: 0.9363 - val_loss: 0.0040 - val_accuracy: 1.0000\n",
      "Epoch 3/10\n",
      "5/5 [==============================] - 6s 1s/step - loss: 0.0515 - accuracy: 0.9745 - val_loss: 0.0038 - val_accuracy: 1.0000\n",
      "Epoch 4/10\n",
      "5/5 [==============================] - 5s 1s/step - loss: 0.0589 - accuracy: 0.9809 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "5/5 [==============================] - 6s 1s/step - loss: 0.0747 - accuracy: 0.9809 - val_loss: 0.0036 - val_accuracy: 1.0000\n",
      "Epoch 6/10\n",
      "5/5 [==============================] - 5s 1s/step - loss: 0.0269 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "5/5 [==============================] - 5s 1s/step - loss: 0.0508 - accuracy: 0.9936 - val_loss: 0.0035 - val_accuracy: 1.0000\n",
      "Epoch 8/10\n",
      "5/5 [==============================] - 5s 1s/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "5/5 [==============================] - 5s 1s/step - loss: 0.0391 - accuracy: 0.9936 - val_loss: 0.0034 - val_accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "5/5 [==============================] - 6s 1s/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.0032 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-20 20:44:43.829725: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 252ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Validation Loss after Fine-Tuning: 0.003204665146768093\n",
      "Validation Accuracy after Fine-Tuning: 1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "import os\n",
    "\n",
    "# Folders\n",
    "train_dir = 'train_dataset'\n",
    "val_dir = 'validation_dataset'\n",
    "\n",
    "# Check if folders exist\n",
    "assert os.path.exists(train_dir), \"Trainingsverzeichnis existiert nicht\"\n",
    "assert os.path.exists(val_dir), \"Validierungsverzeichnis existiert nicht\"\n",
    "\n",
    "# Data augmentation and normalization\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest'\n",
    ")\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Data generators\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(\n",
    "    val_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "# Create model\n",
    "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "predictions = Dense(train_generator.num_classes, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# Freeze base model layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train model with freezed base model layers\n",
    "history_transfer_learning = model.fit(\n",
    "    train_generator,\n",
    "    epochs=10,\n",
    "    validation_data=val_generator\n",
    ")\n",
    "\n",
    "# Evaluation of performance after transfer learning\n",
    "val_loss, val_accuracy = model.evaluate(val_generator)\n",
    "print(f\"Validation Loss after Transfer Learning: {val_loss}\")\n",
    "print(f\"Validation Accuracy after Transfer Learning: {val_accuracy}\")\n",
    "\n",
    "# Fine-tuning\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = True\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train model with fine-tuning\n",
    "history_fine_tuning = model.fit(\n",
    "    train_generator,\n",
    "    epochs=10,\n",
    "    validation_data=val_generator\n",
    ")\n",
    "\n",
    "# Evaluation of performance after fine-tuning\n",
    "val_loss_fine_tuning, val_accuracy_fine_tuning = model.evaluate(val_generator)\n",
    "print(f\"Validation Loss after Fine-Tuning: {val_loss_fine_tuning}\")\n",
    "print(f\"Validation Accuracy after Fine-Tuning: {val_accuracy_fine_tuning}\")\n",
    "\n",
    "# Save model\n",
    "model.save('pokemon_classifier_model.keras')\n"
   ]
  }
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