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I am new to CNNs, and I'm trying to follow along with a Pytorch DCGAN tutorial by reimplementing it in Keras. Clearly there are some differences in the frameworks, but in particular I am struggling to understand how the input noise vector is handled per the following image of the generator architecture:

Generator architecture

The code goes on to define the first layer of the network as:

# input is Z, going into a convolution
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4

Where nz = 100 (size of noise vector) and ngf = 64.

How does that work? Is the noise vector implicitly reshaped by that first convolutional layer to a (?, 4, 4) tensor, as suggested by the diagram? How would that even work? Is there an implicit dense connection between the noise vector and the convolutional layer? How does that first layer result in a tensor of shape (64*8, 4, 4) per the comment?

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Turned out to be a dumb question in the end - the noise vector is simply treated as a 1x1 array with "depth" (i.e. number of channels/features) of 100, which can of course be processed by the ConvTranspose2d layer. This reshaping to 1x1x100 just has to be explicitly specified as the first layer in keras:

model.add(Reshape((1, 1, 100), input_shape=(100,)))
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