Deep Learning By Example

Model training

It's time to start training this deeper network, which in turn will take more time to converge by reconstructing noise-free images from the noisy input.

So let's start off by creating the session variable:

sess = tf.Session()

Next up, we will kick off the training process but for more number of epochs:

num_epochs = 100train_batch_size = 200# Defining a noise factor to be added to MNIST datasetmnist_noise_factor = 0.5sess.run(tf.global_variables_initializer())for e in range(num_epochs):    for ii in range(mnist_dataset.train.num_examples//train_batch_size):        input_batch = mnist_dataset.train.next_batch(train_batch_size)                # Getting and reshape the images from the corresponding batch batch_images = input_batch[0].reshape((-1, 28, ...