To retrain only the top layer of the AlexNet model in Keras, you can follow these steps:

  1. Load the Pretrained AlexNet Model: First, you need to load the pretrained AlexNet model with its top layers included. In Keras, you can use the keras.applications.AlexNet module to load the model.
from keras.applications import AlexNet # Load the pretrained AlexNet model model = AlexNet(weights='imagenet', include_top=True)
  1. Freeze Lower Layers: To ensure that only the top layer gets retrained, you need to freeze the weights of the lower layers so that they are not updated during the training process. This can be achieved by setting the trainable property of the layers you want to freeze to False.
# Freeze all layers except the top layer for layer in model.layers[:-1]: layer.trainable = False
  1. Add Your Top Layer: Replace the original top layer of the model with your own custom top layer. This new top layer should have the same number of output units as the number of classes in your new classification task.
from keras.layers import Dense # Number of classes in your new classification task num_classes = 10 # Add a new Dense (fully connected) layer as the top layer top_layer = Dense(num_classes, activation='softmax')(model.layers[-2].output) # Create a new model with the new top layer new_model = Model(inputs=model.input, outputs=top_layer)
  1. Compile and Train the Model: Compile the new model with an appropriate optimizer and loss function and train it on your new dataset.
# Compile the model new_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Train the model with your new dataset, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))

By following these steps, you retrain only the top layer of the AlexNet model while keeping the lower layers frozen and using the pretrained weights from the original model. This allows you to leverage the learned features from the original model and adapt it to your new classification task.

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