import tensorflow as tf

    print("------------------pred_Y-------------------")
    print(pred_Y)

    # Loss,train_step,evaluation
    l2 = config.lambda_loss_amount * \
         sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())
    # Softmax loss and L2
    cost = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(pred_Y, Y)) + l2
    train_step = tf.train.AdamOptimizer(
