e8caea8ea26a18f93c49ceb6e8d9a48403ca9e30,lxmls/deep_learning/numpy_models/rnn.py,NumpyRNN,log_forward,#NumpyRNN#Any#,34

Before Change

        y = h[1:, :].dot(W_y.T)

        // Softmax
        log_p_y = y - logsumexp(y, axis=1)[:, None]

        return log_p_y, y, h, z_e, input

    def backpropagation(self, input, output):

After Change

        y = h[1:, :].dot(W_y.T)

        // Softmax
        log_p_y = y - logsumexp(y, axis=1, keepdims=True)

        return log_p_y, y, h, z_e, input

    def backpropagation(self, input, output):

Instances