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?
All advanced activations in Keras, including LeakyReLU
, are available as layers, and not as activations; therefore, you should use it as such:
from keras.layers import LeakyReLU
# instead of cnn_model.add(Activation('relu'))
# use
cnn_model.add(LeakyReLU(alpha=0.1))