import tensorflow as tf


        print("------------------list-------------------")
        print(outputs)
        # Get last time step's output feature for a "many to one" style classifier,
        # as in the image describing RNNs at the top of this page
        lstm_last_output = outputs[-1]  # Get the last element of the array: [?, 32]

        print("------------------last outputs-------------------")
        print (lstm_last_output)

        # Linear activation
        return tf.matmul(lstm_last_output, config.W['output']) + config.biases['output']

    pred_Y = LSTM_Network(X, config)   # shape[?,6]
    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(
