The keras Conv2D layer does not come with an activation function itself. I am currently rebuilding the YOLOv1 model for practicing. In the YOLOv1 model, there are several Conv2D layers followed by activations using the leaky relu function. Is there a way to combine
from keras.layers import Conv2D, LeakyReLU ... def model(input): ... X = Conv2D(filters, kernel_size)(X) X = LeakyReLU(X) ...
into a single line of code, like
X = conv_with_leaky_relu(X)? I think it should be similar to
def conv_with_leaky_relu(*args, **kwargs): X = Conv2D(*args, **kwargs)(X) X = LeakyReLU(X) return X
but this of course doesn’t work because it is undefined what X ist. Any ideas?
You can just pass it as an activation:
X = Conv2D(filters, kernel_size, activation=LeakyReLU())(X)
Answered By – Yannick Funk