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import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Flatten, Dense
# Define data paths (modify as needed)
train_data_dir = 'train'
validation_data_dir = 'validation'
test_data_dir = 'test'
# Set image dimensions (adjust if necessary)
img_width, img_height = 224, 224 # VGG16 expects these dimensions
# Data augmentation for improved generalization (optional)
train_datagen = ImageDataGenerator(
rescale=1./255, # Normalize pixel values
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)
validation_datagen = ImageDataGenerator(rescale=1./255) # Only rescale for validation
# Load training and validation data
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32, # Adjust batch size based on GPU memory
class_mode='binary' # Two classes: cat or dog
)
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary'
)
# Load pre-trained VGG16 model (without the top layers)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))
# Freeze the base model layers (optional - experiment with unfreezing for fine-tuning)
base_model.trainable = False
# Add custom layers for classification on top of the pre-trained model
x = base_model.output
x = Flatten()(x)
predictions = Dense(1, activation='sigmoid')(x) # Sigmoid for binary classification
# Create the final model
model = tf.keras.Model(inputs=base_model.input, outputs=predictions)
# Compile the model
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Train the model
history = model.fit(
train_generator,
epochs=10, # Adjust number of epochs based on dataset size and validation performance
validation_data=validation_generator
)
# Evaluate the model on test data (optional)
test_generator = validation_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary'
)
test_loss, test_acc = model.evaluate(test_generator)
print('Test accuracy:', test_acc)
# Save the model for future use (optional)
model.save('cat_dog_classifier.h5')
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