Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis

 

 

 

Writing Our First Generative Adversarial Network

Let us now move on to actually implementing a GAN in tensorflow. We will study the performance of our GAN on the MNIST dataset. This code is based on and adapted from the google tutorial

First we import our libraries 

import os
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers
from tensorflow.keras.utils import plot_model

Next we define our hyperparameters and import our data the usual way 

BUFFER_SIZE = 60000
BATCH_SIZE = 256
EPOCHS = 30

data = tf.keras.datasets.mnist.load_data()
(train_images, train_labels), (test_images, test_labels) = data
train_images = np.reshape(train_images, (train_images.shape[0],
                                         28,
                                         28,
                                         1)).astype('float32')

# we normalize between -1 and 1
train_images = (train_images - 127.5) / 127.5
training_dataset = tf.data.Dataset.from_tensor_slices(
                      train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)