Before moving forward, let's have a look at the architecture of the discriminator network:
The preceding diagram gives a top-level overview of the architecture of the generator network.
As mentioned, the discriminator network is a CNN that contains 10 layers (you can add more layers to the network according to your requirements). Basically, it takes an image of dimensions of 64 x 64 x 3, downsamples it using 2D convolutional layers, and passes it to the fully connected layers for classification. Its output is a prediction as to whether the given image is a fake image or a real image. This can be either 0 or 1; if the output is 1, the image passed to the discriminator is real and if the output is 0, the image passed is a fake image.
Let's have a look at the layers in the discriminator network:
|
Layer # |
Layer name |
Configuration |
|
1. |
Input layer |
input_shape=(batch_size, 64, 64, 3), output_shape=(batch_size, 64, 64, 3) |
|
2. |
2D convolutional Layer |
filters=128, kernel_size=(5, 5), strides=(1, 1), padding='valid', input_shape=(batch_size, 64, 64, 3), output_shape=(batch_size, 64, 64, 128), activation='leakyrelu', leaky_relu_alpha=0.2 |
|
3. |
MaxPooling2D |
pool_size=(2, 2), input_shape=(batch_size, 64, 64, 128), output_shape=(batch_size, 32, 32, 128) |
|
4. |
2D convolutional layer |
filters=256, kernel_size=(3, 3), strides=(1, 1), padding='valid', input_shape=(batch_size, 32, 32, 128), output_shape=(batch_size, 30, 30, 256), activation='leakyrelu', leaky_relu_alpha=0.2 |
|
5. |
MaxPooling2D |
pool_size=(2, 2), input_shape=(batch_size, 30, 30, 256), output_shape=(batch_size, 15, 15, 256) |
|
6. |
2D convolutional layer |
filters=512, kernel_size=(3, 3), strides=(1, 1), padding='valid', input_shape=(batch_size, 15, 15, 256), output_shape=(batch_size, 13, 13, 512), activation='leakyrelu', leaky_relu_alpha=0.2 |
|
7. |
MaxPooling2D |
pool_size=(2, 2), input_shape=(batch_size, 13, 13, 512), output_shape=(batch_size, 6, 6, 512) |
|
8. |
Flatten layer |
input_shape=(batch_size, 6, 6, 512), output_shape=(batch_size, 18432) |
|
9. |
Dense layer |
neurons=1024, input_shape=(batch_size, 18432), output_shape=(batch_size, 1024), activation='leakyrelu', 'leakyrelu_alpha'=0.2 |
|
10. |
Dense layer |
neurons=1, input_shape=(batch_size, 1024), output_shape=(batch_size, 1), activation='sigmoid' |
Let's have a look at how tensors flow from the first layer to the last layer. The following diagram shows input and output shapes for the different layers:
