{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 2199 images belonging to 2 classes.\n",
"Found 549 images belonging to 2 classes.\n",
"Training samples: 2199\n",
"Validation samples: 549\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"dataset_dir = 'C:/Users/vsavelev/GITHUB/DS_projet/jan24_cds_mushrooms/data'\n",
"\n",
"# Create ImageDataGenerator with validation split\n",
"datagen = ImageDataGenerator(rescale=1.0/255, validation_split=0.2)\n",
"\n",
"train_generator = datagen.flow_from_directory(\n",
" dataset_dir,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" subset='training' # Set as training data\n",
")\n",
"\n",
"validation_generator = datagen.flow_from_directory(\n",
" dataset_dir,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" subset='validation' # Set as validation data\n",
")\n",
"\n",
"print(f'Training samples: {train_generator.samples}')\n",
"print(f'Validation samples: {validation_generator.samples}')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"Model: \"sequential\"\n",
"\n"
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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"output_type": "display_data"
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"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ resnet50 (Functional) │ ? │ 23,587,712 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ global_average_pooling2d │ ? │ 0 (unbuilt) │\n",
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" Total params: 23,587,712 (89.98 MB)\n",
"\n"
],
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"output_type": "display_data"
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{
"data": {
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" Trainable params: 0 (0.00 B)\n",
"\n"
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" Non-trainable params: 23,587,712 (89.98 MB)\n",
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],
"source": [
"from tensorflow.keras.applications import ResNet50\n",
"from tensorflow.keras import layers, models\n",
"\n",
"# Load and Configure the Pre-trained ResNet50 Model\n",
"base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
"\"\"\"\n",
"weights='imagenet': Loads the pre-trained weights from the ImageNet dataset.\n",
"include_top=False: Excludes the top fully-connected layers of the ResNet50 model, enabling you to add your own custom layers.\n",
"input_shape=(224, 224, 3): Specifies the input shape of the images (224x224 pixels, with 3 color channels - RGB).\n",
"\"\"\"\n",
"\n",
"# Freeze the base model (to freeze the pre-trained layers)\n",
"base_model.trainable = False\n",
"\n",
"# Add custom layers on top of the base model\n",
"model = models.Sequential([ #allows to stack layers linearly\n",
" base_model,\n",
" layers.GlobalAveragePooling2D(),\n",
" layers.Dense(1024, activation='relu'),\n",
" layers.Dropout(0.5),\n",
" layers.Dense(train_generator.num_classes, activation='softmax')\n",
"])\n",
"\n",
"\"\"\"\n",
"GlobalAveragePooling2D(): Reduces each feature map to a single number by taking the average, \n",
"which helps to reduce the size of the model and prevent overfitting.\n",
"Dense(1024, activation='relu'): Adds a fully connected layer with 1024 units and ReLU activation function.\n",
"Dropout(0.5): Adds a dropout layer with a 50% dropout rate to prevent overfitting by randomly setting half of the input units \n",
"to 0 at each update during training.\n",
"Dense(train_generator.num_classes, activation='softmax'): \n",
"Adds the final output layer with units equal to the number of classes in your dataset, using the softmax activation function for multi-class classification.\n",
"\"\"\"\n",
"model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"\"\"\"\n",
"optimizer=tf.keras.optimizers.Adam(): Uses the Adam optimizer, which is an adaptive learning rate optimization algorithm.\n",
"loss='categorical_crossentropy': Uses categorical cross-entropy as the loss function, suitable for multi-class classification.\n",
"metrics=['accuracy']: Tracks accuracy as the metric to evaluate the model's performance during training and testing.\n",
"\"\"\"\n",
"model.summary()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\vsavelev\\AppData\\Local\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m135s\u001b[0m 2s/step - accuracy: 0.8657 - loss: 0.5402 - val_accuracy: 0.8860 - val_loss: 0.3811\n",
"Epoch 2/10\n",
"\u001b[1m 1/68\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:36\u001b[0m 1s/step - accuracy: 0.8125 - loss: 0.5545"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\vsavelev\\AppData\\Local\\anaconda3\\Lib\\contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
" self.gen.throw(typ, value, traceback)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8125 - loss: 0.5545 - val_accuracy: 1.0000 - val_loss: 0.2601\n",
"Epoch 3/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 2s/step - accuracy: 0.8812 - loss: 0.3830 - val_accuracy: 0.8860 - val_loss: 0.3604\n",
"Epoch 4/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.9375 - loss: 0.2276 - val_accuracy: 1.0000 - val_loss: 0.1036\n",
"Epoch 5/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 3s/step - accuracy: 0.8776 - loss: 0.3826 - val_accuracy: 0.8860 - val_loss: 0.3568\n",
"Epoch 6/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9688 - loss: 0.1941 - val_accuracy: 1.0000 - val_loss: 0.1264\n",
"Epoch 7/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 3s/step - accuracy: 0.8844 - loss: 0.3665 - val_accuracy: 0.8879 - val_loss: 0.3532\n",
"Epoch 8/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.9062 - loss: 0.2978 - val_accuracy: 0.8000 - val_loss: 0.5479\n",
"Epoch 9/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 3s/step - accuracy: 0.8929 - loss: 0.3426 - val_accuracy: 0.8879 - val_loss: 0.3531\n",
"Epoch 10/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - accuracy: 0.8125 - loss: 0.4996 - val_accuracy: 0.8000 - val_loss: 0.5142\n"
]
}
],
"source": [
"history = model.fit(\n",
" train_generator,\n",
" steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
" validation_data=validation_generator,\n",
" validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
" epochs=10\n",
")\n",
"\n",
"#This specifies the number of complete passes through the training dataset. Here, the model will train for 10 epochs."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m330s\u001b[0m 4s/step - accuracy: 0.8855 - loss: 0.4046 - val_accuracy: 0.8879 - val_loss: 0.3791\n",
"Epoch 2/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 10ms/step - accuracy: 0.8750 - loss: 0.3539 - val_accuracy: 0.8000 - val_loss: 0.5808\n",
"Epoch 3/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m289s\u001b[0m 4s/step - accuracy: 0.8960 - loss: 0.2870 - val_accuracy: 0.8860 - val_loss: 0.3903\n",
"Epoch 4/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 7ms/step - accuracy: 0.9688 - loss: 0.1397 - val_accuracy: 1.0000 - val_loss: 0.2601\n",
"Epoch 5/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m283s\u001b[0m 4s/step - accuracy: 0.9162 - loss: 0.2052 - val_accuracy: 0.8787 - val_loss: 0.4098\n",
"Epoch 6/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.8438 - loss: 0.3068 - val_accuracy: 0.6000 - val_loss: 0.7204\n",
"Epoch 7/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m290s\u001b[0m 4s/step - accuracy: 0.9429 - loss: 0.1480 - val_accuracy: 0.8382 - val_loss: 0.4598\n",
"Epoch 8/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - accuracy: 0.9062 - loss: 0.1594 - val_accuracy: 1.0000 - val_loss: 0.2145\n",
"Epoch 9/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m299s\u001b[0m 4s/step - accuracy: 0.9658 - loss: 0.1009 - val_accuracy: 0.8548 - val_loss: 0.4892\n",
"Epoch 10/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.0651 - val_accuracy: 0.8000 - val_loss: 0.9206\n"
]
}
],
"source": [
"# Unfreeze some layers\n",
"base_model.trainable = True\n",
"fine_tune_at = 100 # fine-tune from this layer onwards\n",
"\n",
"for layer in base_model.layers[:fine_tune_at]:\n",
" layer.trainable = False\n",
"\n",
"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),\n",
" loss='categorical_crossentropy', #The categorical cross-entropy loss function is used because this is a multi-class classification problem\n",
" metrics=['accuracy'])\n",
"\n",
"\"\"\"\n",
"The Adam optimizer is used with a very small learning rate (1e-5). Fine-tuning typically \n",
"uses a smaller learning rate to prevent large updates to the weights, which could potentially destroy the learned features in the pre-trained model.\n",
"\"\"\"\n",
"history_fine = model.fit(\n",
" train_generator,\n",
" steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
" validation_data=validation_generator,\n",
" validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
" epochs=10\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 2s/step - accuracy: 0.8559 - loss: 0.4854\n",
"Validation loss: 0.4950728416442871\n",
"Validation accuracy: 0.8561019897460938\n"
]
}
],
"source": [
"loss, accuracy = model.evaluate(validation_generator)\n",
"print(f'Validation loss: {loss}')\n",
"print(f'Validation accuracy: {accuracy}')\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}