tf.nn.sparse_softmax_cross_entropy_with_logits, but beware that it can't accept the output of
tf.nn.softmax. Instead, calculate the unscaled activations, and then the cost:
logits = tf.matmul(state_below, U) + b cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)
In this case:
U should be 2D matrices,
b should be a vector of a size equal to the number of classes, and
labels should be a 2D matrix of
int64. This function also supports activation tensors with more than two dimensions.