Designed a generator G that generates data from a desired distribution using multi-layer perceptron (only fully connected layers with non-linearities) which takes a single scalar z as input and outputs a single scalar x. Complemented the generator G with a discriminator D for an adversarial training which also uses multi-layer perceptron (only fully connected layer with non-linearities). D takes a single scalar x as input and outputs a [0,1] value for classification; x is the output from G and xdata is a sample from the desired data distribution.
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Design a generator G that generates data from a desired distribution using multi-layer perceptron (only fully connected layers with non-linearities) which takes a single scalar z as input and outputs a single scalar x. You can use ReLU as the non-linear activation function for the hidden layers, and the output layer does not have any activations…
K1shor3/Generative-Adversarial-Network-1-D
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Design a generator G that generates data from a desired distribution using multi-layer perceptron (only fully connected layers with non-linearities) which takes a single scalar z as input and outputs a single scalar x. You can use ReLU as the non-linear activation function for the hidden layers, and the output layer does not have any activations…
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