#Redefining our data
Y_pred = tf.linspace(-4., 6., 500)
Y_label = tf.constant(1.)
Y_labels = tf.fill([500,], 1.)

#applying sigmoid
x_entropy_vals = - tf.multiply(Y_label, tf.log(Y_pred)) - tf.multiply((1. - Y_label), tf.log(1. - Y_pred))
x_entropy_loss = sess.run(x_entropy_vals)
#ploting the predicted values against the cross entropy loss
Y_array = sess.run(Y_pred)
plt.plot(Y_array, x_entropy_loss, 'r-' )
plt.title('Cross entropy loss')
plt.xlabel('$Y_{pred}$', fontsize=15)
plt.ylabel('$Y_{label}$', fontsize=15)
plt.ylim(-2, 5)
plt.show()