Generative Adversarial Networks Projects

Generator

As mentioned in the Architecture of DCGAN section, the generator network consists of some 2D convolutional layers, upsampling layers, a reshape layer, and a batch normalization layer. In Keras, every operation can be specified as a layer. Even activation functions are layers in Keras and can be added to a model just like a normal dense layer.

Perform the following steps to create a generator network:

1. Let's start by creating a Sequential Keras model:

gen_model = Sequential()

2. Next, add a dense layer that has 2,048 nodes, followed by an activation layer, tanh:

gen_model.add(Dense(units=2048))gen_model.add(Activation('tanh'))

3. Next, add the second layer, which is also a dense layer that has 16,384 neurons. This is followed by a ...