e8caea8ea26a18f93c49ceb6e8d9a48403ca9e30,lxmls/deep_learning/numpy_models/log_linear.py,NumpyLogLinear,log_forward,#NumpyLogLinear#,17

Before Change

        z = np.dot(input, self.weight.T) + self.bias

        // Softmax implemented in log domain
        log_tilde_z = z - logsumexp(z, axis=1)[:, None]

        return log_tilde_z

    def predict(self, input=None):

After Change

        z = np.dot(input, self.weight.T) + self.bias

        // Softmax implemented in log domain
        log_tilde_z = z - logsumexp(z, axis=1, keepdims=True)

        return log_tilde_z

    def predict(self, input=None):

Instances