import tensorflow as tf


    acc_train = [] #store train accuracy for each epoch
    acc_test  = [] #store test accuracy for each epoch

    if actL == 'sigmoid': #accuracy score for binary class classification

        Yp = tf.greater(an , 0.5)
        accuracy = tf.reduce_mean(tf.cast(tf.equal(Yp, tf.equal(Y,1.0)), "float"))

    elif actL == 'esp' or actL == 'relu': #r2 score

         norm= tf.reduce_mean( tf.squared_difference(Y,tf.reduce_mean(Y)) )
         accuracy = 1 - tf.divide( tf.reduce_mean(tf.squared_difference(an, Y)), norm)

    elif actL == 'softmax': #accuracy score for multiclass classification

         Yp = tf.sigmoid(betan*hn)
         correct = tf.equal(tf.argmax(Yp), tf.argmax(Y))
         accuracy= tf.reduce_mean(tf.cast(correct, "float"))

    #-----------------Initialize the graph and start the session-------------------------------------------------

    init = tf.global_variables_initializer()

