Let's go ahead and kick off the training process:

num_epochs = 10train_batch_size = 1000contextual_window_size = 10with train_graph.as_default():    saver = tf.train.Saver()with tf.Session(graph=train_graph) as sess:        iteration_num = 1    average_loss = 0        #Initializing all the vairables    for e in range(1, num_epochs+1):                #Generating random batch for training        batches = generate_random_batches(training_words, train_batch_size, contextual_window_size)               #Iterating through the batch samples        for input_vals, target in batches:                        #Creating the feed dict            feed_dict = {inputs_values: input_vals,                    labels_values: np.array(target)[:, None]}             train_loss, _ =[model_cost, model_optimizer], feed_dict=feed_dict) ...

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