import tensorflow as tf


    X, Y= create_placeholders(f, l)                 # Create Placeholders

    parameters = initialize_parameters(network, activation, stdbeta)
    betan = tf.identity(parameters['beta'+str(L)], name="betan") #add the output noise to the graph for later retrieval

    an, hn, _ , _ = FW_prop(X, parameters, activation) #post and pre-activation output of the last layer

    an = tf.identity(an, name= "an")               #add the output post-activation value to the graph for later retrieval
    hn = tf.identity(hn, name='hn')                #add the output pre-activation value to the graph for later retrieval

    #Create a saver for the Model
    if save_model == True:
        saver = tf.train.Saver()

    #-----------------Initialize the cost and gradients---------------------------------------------------------
