January 2019
Intermediate to advanced
316 pages
8h 16m
English
Content preview from Generative Adversarial Networks Projects
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Downsampling blocks
This block takes a low-resolution image of the dimensions 64x64x3 from the generator of Stage-I and downsamples it to generate a tensor with a shape of 16x16x512. The image goes through a series of 2D convolution blocks.
In this section, we will write the implementation for the downsampling blocks.
- Start by creating with the first downsampling block. This block contains a 2D convolution layer with ReLU as the activation function. Before applying the 2D convolution, pad the input with zeros on all sides. The configurations for the different layers in this block are the following:
- Padding size: (1, 1)
- Filters: 128
- Kernel size: (3, 3)
- Strides: 1
- Activation: ReLU
x = ZeroPadding2D(padding=(1, 1))(input_lr_images)x = Conv2D( ...
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