EffiencientNetB0

transferlearning.py

# 1. Create base model with tf.keras.applications
base_model = tf.keras.applications.EfficientNetB0(include_top=False)

# 2. Freeze the base model (so the pre-learned patterns remain)
base_model.trainable = False

# 3. Create inputs into the base model
inputs = tf.keras.layers.Input(shape=(224, 224, 3), name="input_layer")

# 4. If using ResNet50V2, add this to speed up convergence, remove for EfficientNet
# x = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)(inputs)

# 5. Pass the inputs to the base_model (note: using tf.keras.applications, EfficientNet inputs don't have to be normalized)
x = base_model(inputs)
# Check data shape after passing it to base_model
print(f"Shape after base_model: {x.shape}")

# 6. Average pool the outputs of the base model (aggregate all the most important information, reduce number of computations)
x = tf.keras.layers.GlobalAveragePooling2D(name="global_average_pooling_layer")(x)
print(f"After GlobalAveragePooling2D(): {x.shape}")

# 7. Create the output activation layer
outputs = tf.keras.layers.Dense(6, activation="softmax", name="output_layer")(x)

# 8. Combine the inputs with the outputs into a model
model_2 = tf.keras.Model(inputs, outputs)

# 9. Compile the model
model_2.compile(loss='categorical_crossentropy',
              optimizer=tf.keras.optimizers.Adam(),
              metrics=["accuracy"])

# 10. Fit the model (we use less steps for validation so it's faster)
history_2 = model_2.fit(train_data,
                        epochs=10,
                        steps_per_epoch=len(train_data),
                        validation_data=val_data,
                        # Track model training logs
                        callbacks=[create_tensorboard_callback("CNN_learning", "transfer_learning_efficientNet_0")])