<jupyter_start><jupyter_text>Tensorflow Code Scaffolding for building simple models<jupyter_code># Import modules
import tensorflow as tf
# Initialize variable/model parameters
# define the training loop operations
def inference(X):
    """
    Computes inference model over data X and 
    returns the result
    """
    pass
def loss(X,y):
    """
    Computes loss over training data X and
    expected value y
    """
    pass
def inputs():
    """
    Read/generate input training data X and
    expected value y
    """
    pass
def train(total_loss):
    """
    Train/adjust model parameters according to computed
    total loss
    """
    pass
# Create a saver
saver = tf.train.Saver()
# Launch the graph in a session
with tf.Session() as sess:
    
    # model setup
    initial_step=0
    
    # verify if we don't have a checkpoint
    # saved already
    checkpoint = tf.train.get_checkpoint_state(
    os.path.dirname(__file__)
    )
    
    if checkpoint and checkpoint.model_checkpoint_path:
        # Restore variables from checkpoint
        saver.restore(sess,
            checkpoint.model_checkpoint_path
                     )
        initial_step = int(
            checkpoint.model_checkpoint_path.rsplit(
            '-',1
            )[1]
        )
    
    # Do these operations only if no
    # checkpoint was found
    else:
        # Initialize all variables and run
        tf.global_variables_initializer().run()
    
        # Get inputs
        X,y = inputs()
    
    # Get total loss
    total_loss = loss(X,y)
    
    # Get training operation
    train_op = train(total_loss)
    
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(
    sess = sess,
        coord = coord
    )
    
    # Training loop
    traincoord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(
    sess = sess,
        coord = coord
    )
    ing_steps = 1000
    for step in range(initial_step, training_steps):
        sess.run([train_op])
        # for debugging and learning purposes,
        # see how the loss gets decremented 
        # during training steps
        if step % 10 == 0:
            print("loss: {}".format(sess.run(
                [total_loss]
            )))
        # Create checkpoint
        if step % 1000 == 0:
            saver.save(sess,"my-model",
                      global_step=step
                      )
    # Evaluate the resulting model
    evaluate(sess,X,y)
    
    coord.request_stop()
    coord.join(threads)
    saver.save(sess,"my-model",
              global_step=training_steps)
    sess.close()<jupyter_output><empty_output>
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