Let's kick off the training process by creating a session variable that will be responsible for executing the computational graph that we defined earlier:
session = tf.Session()
Also, we need to initialize the variables that we have defined so far:
session.run(tf.global_variables_initializer())
We are going to feed the images in batches to avoid an out-of-memory error:
train_batch_size = 64
Before kicking the training process, we are going to define a helper function that will perform the optimization process by iterating through the training batches:
# number of optimization iterations performed so fartotal_iterations = 0def optimize(num_iterations): # Update globally the total number of iterations performed so far. ...
