I've stumbled across some Python expression from which I don't know whether its tensorflow or Python related. Since Im not very profound in Python, I may ask if someone could explain me, what the concatenation of (layer1)(layer2) actually means:
(layer1)(layer2)
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) d = LeakyReLU(alpha=0.2)(d)
d is later given as an kwargs argument in the Model(*args, **kwargs). Its not a simple multiplication * operation, Ive tried that.
kwargs
Model(*args, **kwargs)
*
Thank you in advance!
This is not a concatenation. Those second brackets are inputs to the layer.
Conv2D(filters, kernel_size=f_size, strides=2, padding='same') returns function for which (layer_input) is input layer.
Conv2D(filters, kernel_size=f_size, strides=2, padding='same')
(layer_input)
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