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 sess.run(tf.global_variables_initializer()) 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, _ = sess.run([model_cost, model_optimizer], feed_dict=feed_dict) ...
