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.
Now dataset X={x1,x2,x3...,xn},
shape=[n,m], x1,x2,...,xn are samples of X. And label datay.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?
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？