I am trying to produce a CNN using Keras, and wrote the following code:
batch_size = 64
epochs = 20
num_classes = 5
cnn_model = Sequential()
cnn_model.add(Conv2D(32, kernel_size=(3, 3), activation='linear',
input_shape=(380, 380, 1), padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D((2, 2), padding='same'))
cnn_model.add(Conv2D(64, (3, 3), activation='linear', padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
cnn_model.add(Conv2D(128, (3, 3), activation='linear', padding='same'))
cnn_model.add(Activation('relu'))
cnn_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
cnn_model.add(Flatten())
cnn_model.add(Dense(128, activation='linear'))
cnn_model.add(Activation('relu'))
cnn_model.add(Dense(num_classes, activation='softmax'))
cnn_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
I want to use Keras's LeakyReLU activation layer instead of using Activation('relu'). However, I tried using LeakyReLU(alpha=0.1) in place, but this is an activation layer in Keras, and I get an error about using an activation layer and not an activation function.
How can I use LeakyReLU in this example?
Sometimes you just want a drop-in replacement for a built-in activation layer, and not having to add extra activation layers just for this purpose.
For that, you can use the fact that the activation argument can be a callable object.
lrelu = lambda x: tf.keras.activations.relu(x, alpha=0.1)
model.add(Conv2D(..., activation=lrelu, ...)
Since a Layer is also a callable object, you could also simply use
model.add(Conv2D(..., activation=tf.keras.layers.LeakyReLU(alpha=0.1), ...)
which now works in TF2. This is a better solution as this avoids the need to use a custom_object during loading as @ChristophorusReyhan mentionned.