tensorflow Multidimensional softmax Computing Costs on a Softmax Output Layer


Use 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: state_below and 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 int32 or int64. This function also supports activation tensors with more than two dimensions.