Upload train3.py

train3.py

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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 = 'tt'
+validation_data_dir = 'tt'
+test_data_dir = 'tt'
+
+# 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')