# Train and evaluate CNN with Early Stopping procedure defined at the very top
with tf.Session() as sess:
    init.run()
    for epoch in range(n_epochs):
        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):
            iteration += 1
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch, training: True})
            if iteration % check_interval == 0:
                loss_val = loss.eval(feed_dict={X: X_valid, y: y_valid})
                if loss_val < best_loss_val:
                    best_loss_val = loss_val
                    checks_since_last_progress = 0
                    best_model_params = get_model_params()
                else:
                    checks_since_last_progress += 1
        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
        acc_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})
        print("Epoch {}, last batch accuracy: {:.4f}%, valid. accuracy: {:.4f}%, valid. best loss: {:.6f}".format(
                  epoch, acc_batch * 100, acc_val * 100, best_loss_val))
        if checks_since_last_progress > max_checks_without_progress:
            print("Early stopping!")
            break

    if best_model_params:
        restore_model_params(best_model_params)
    acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
    print("Final accuracy on test set:", acc_test)
    save_path = saver.save(sess, "./my_mnist_model")