LSTM：About data sequence in 7-RNN_Classifier_example.py 关于过程数据序列化问题 7-RNN_Classifier_example.py？

[rosefun]

Now dataset X={x1,x2,x3...,xn},shape=[n,m], x1,x2,...,xn are samples of X.
And label data y.shape=[n,k]
If I use a time window with length of 2，then after reshape:
X= tf.reshape(X,[int(n/2), 2, m])
X.shape=[n/2,m]
But I have a problem in getting the cost by formula,
cost_rnn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_ , labels=y))
because both X and y have different shape.

Anybody knows how to solve this problem?

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现在，有数据集X={x1,x2,x3...,xn},shape=[n,m]
其中，x1包含多个变量，shape=[m].
比如，X=[[1,10,100],[2,20,200],[3,30,300]]，可以看做X由多个样本x1,x2,...组成的。

标签样本y={y1,y2,...yn},shape=[n,k],
比如，Y=[[1,0,0],[0,1,0],[0,0,1]]。
这个LSTM如果序列化数据的话，比如说，用时间窗time_step=2,
X= tf.reshape(X,[int(n/2), 2, m])

那么，序列化之后的样本，X就只有n-1 个了,shape=[n/2,m]
这样，由于维度不一样，就无法求出cost了
cost_rnn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_rnn, labels=y))

针对这种数据集应该怎样处理？