Tensorflow Supervised Learning Boilerplate

TF_boilerplate(supervised).py

# initialize variables/model parameters

# define the training loop operations
def inference(X):
    # compute inference model over data X nd return the result
    
def loss(X, Y):
    # compute loss over training data X and expected outputs Y
    
def inputs():
    # read/generate input trading data X and expected outputs Y

def train(total_loss):
    # train / adjust model parameters according to computed total loss
    
def evaluate(sess, X, Y):
    # evaluate the resulting trained model
    
# Launch the graph in a session, setup boilerplate
with tf.Session() as sess:
    
    tf.global_variables_initializer().run()
    
    X, Y = inputs()
    
    total_loss = loss(X, Y)
    train_op = train(total_loss)
    
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    
    # actual training loop
    training_steps = 1000
    
    for step in trange(training_steps):
        sess.run([train_op])
        # for debugging and learning purposes, see how the loss gets decremented through training steps
        if step % 10 == 0: # for every 10 epochs
            print "loss: ", sess.run([total_loss])
            
    
    # Evaluate the model
    evaluate(sess, X, Y)
    
    
    # Close training session
    coord.request_stop()
    coord.join(threads)
    sess.close()