neural-net-go.py

#Running our Neural Network!
#First we init the session
iterations = 100000

with tf.Session(graph=graph) as sess:
    #We run the initialize all global variables operation
    sess.run(tf.global_variables_initializer())
    average_loss = 0.0
    #For all of the iterations
    for index in range(iterations):
        #Generate a batch
        ti, tl = gen_batch(genres, scores, batch_size, 8)
        #Put these in our feed dictionary
        feed_dict = {training_inputs: ti, training_labels: tl}
        #Run the NN! Notice the feed dict feeding our placeholders from before
        _, loss_val = sess.run([optimizer, loss], feed_dict=feed_dict)
        #Some metrics so we can see how we're doing
        average_loss += loss_val
        if (index + 1) % 2000 == 0:
            print('Average loss at step', index + 1, average_loss / (index + 1))
    #Returns our Embeddings so we can access them       
    final_embeddings = embeddings.eval()