Coding a Generative Adversarial Network (GAN) for MNIST [Python with Tensorflow]

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Generative Adversarial Network for mnist
Neural Network properties: [Generator]
Hidden Layer: 1
Hidden Nodes: 256
Learning Rate: 0.001
Training steps: 7000 per label
Activation function: Leaky_relu / Sigmoid
Backprob: Adam Optimizer

Neural Network properties: [Discriminator]
Hidden Layer: 1 Fully Connected layer / 2 Convolutional Layers
Hidden Nodes: 1568, 128
Learning Rate: 0.001
Training steps: 7000 per label
Activation function: Leaky_relu / Sigmoid
Backprob: Adam Optimizer

Simple Deep Learning
deep learning
numpy
matplotlib
python
neural

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An explanation of what each section does, while typing code, would have been better

angelachikaebirim

Could you please upload video on DCGAN implementation using dataset insert from google drive

lalithavanik

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