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)
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