{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.applications import ResNet50\n",
"from tensorflow.keras import layers, models\n",
"import keras_tuner as kt\n",
"\n",
"def build_model(hp):\n",
" base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
" base_model.trainable = False\n",
"\n",
" model = models.Sequential()\n",
" model.add(base_model)\n",
" model.add(layers.GlobalAveragePooling2D())\n",
" model.add(layers.Dense(hp.Int('units', min_value=256, max_value=1024, step=256), activation='relu'))\n",
" model.add(layers.Dropout(hp.Float('dropout', min_value=0.2, max_value=0.5, step=0.1)))\n",
" model.add(layers.Dense(train_generator.num_classes, activation='softmax'))\n",
" \n",
" model.compile(optimizer=tf.keras.optimizers.Adam(hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
" \n",
" return model\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Set Up the Tuner\n",
"\n",
"tuner = kt.RandomSearch(\n",
" build_model,\n",
" objective='val_accuracy',\n",
" max_trials=5,\n",
" executions_per_trial=3,\n",
" directory='my_dir',\n",
" project_name='mushroom_classification'\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trial 5 Complete [05h 32m 33s]\n",
"val_accuracy: 0.8870673775672913\n",
"\n",
"Best val_accuracy So Far: 0.8870673775672913\n",
"Total elapsed time: 11h 14m 56s\n"
]
}
],
"source": [
"tuner.search(train_generator, epochs=10, validation_data=validation_generator)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The hyperparameter search is complete. The optimal number of units in the dense layer is 768.\n",
"The optimal learning rate for the optimizer is 0.001.\n",
"\n"
]
},
{
"data": {
"text/html": [
"Model: \"sequential_1\"\n",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ resnet50 (Functional) │ ? │ 23,587,712 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ global_average_pooling2d_1 │ ? │ 0 (unbuilt) │\n",
"│ (GlobalAveragePooling2D) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_2 (Dense) │ ? │ 0 (unbuilt) │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_1 (Dropout) │ ? │ 0 (unbuilt) │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"\n"
],
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ resnet50 (\u001b[38;5;33mFunctional\u001b[0m) │ ? │ \u001b[38;5;34m23,587,712\u001b[0m │\n",
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"│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Total params: 23,587,712 (89.98 MB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,587,712\u001b[0m (89.98 MB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Trainable params: 0 (0.00 B)\n",
"\n"
],
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Non-trainable params: 23,587,712 (89.98 MB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m23,587,712\u001b[0m (89.98 MB)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
"\n",
"print(f\"\"\"\n",
"The hyperparameter search is complete. The optimal number of units in the dense layer is {best_hps.get('units')}.\n",
"The optimal learning rate for the optimizer is {best_hps.get('learning_rate')}.\n",
"\"\"\")\n",
"\n",
"# Build the best model\n",
"model = tuner.hypermodel.build(best_hps)\n",
"model.summary()"
]
},
{
"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[1m164s\u001b[0m 2s/step - accuracy: 0.8856 - loss: 0.4458 - val_accuracy: 0.8860 - val_loss: 0.3645\n",
"Epoch 2/10\n",
"\u001b[1m 1/68\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:57\u001b[0m 2s/step - accuracy: 0.9062 - loss: 0.3521"
]
},
{
"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 11ms/step - accuracy: 0.9062 - loss: 0.3521 - val_accuracy: 1.0000 - val_loss: 0.0784\n",
"Epoch 3/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 2s/step - accuracy: 0.8923 - loss: 0.3465 - val_accuracy: 0.8879 - val_loss: 0.3535\n",
"Epoch 4/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8750 - loss: 0.4432 - val_accuracy: 0.8000 - val_loss: 0.5806\n",
"Epoch 5/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 2s/step - accuracy: 0.8817 - loss: 0.3742 - val_accuracy: 0.8860 - val_loss: 0.3608\n",
"Epoch 6/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.9375 - loss: 0.2319 - val_accuracy: 1.0000 - val_loss: 0.0935\n",
"Epoch 7/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m156s\u001b[0m 2s/step - accuracy: 0.8882 - loss: 0.3576 - val_accuracy: 0.8860 - val_loss: 0.3543\n",
"Epoch 8/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8438 - loss: 0.4161 - val_accuracy: 1.0000 - val_loss: 0.1267\n",
"Epoch 9/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 2s/step - accuracy: 0.8969 - loss: 0.3322 - val_accuracy: 0.8860 - val_loss: 0.3748\n",
"Epoch 10/10\n",
"\u001b[1m68/68\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9375 - loss: 0.2687 - val_accuracy: 1.0000 - val_loss: 0.1681\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": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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[1m30s\u001b[0m 2s/step - accuracy: 0.8850 - loss: 0.3732\n",
"Validation loss: 0.36864525079727173\n",
"Validation accuracy: 0.8870673775672913\n"
]
}
],
"source": [
"loss, accuracy = model.evaluate(validation_generator)\n",
"print(f'Validation loss: {loss}')\n",
"print(f'Validation accuracy: {accuracy}')\n"
]
}
],
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"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
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"name": "python",
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