967e77866ca63db25f5b688db4be5d55480a1102,mlxtend/classifier/softmax_regression.py,SoftmaxRegression,_softmax,#SoftmaxRegression#Any#,81

Before Change

        return (X.dot(W) + b)

    def _softmax(self, z):
        return (np.exp(z.T) / np.sum(np.exp(z), axis=1)).T

    def _cross_entropy(self, output, y_target):
        return - np.sum(np.log(output) * (y_target), axis=1)

After Change

        return (X.dot(W) + b)

    def _softmax(self, z):
        e_x = np.exp(z - z.max(axis=1, keepdims=True))
        out = e_x / e_x.sum(axis=1, keepdims=True)
        return out
        //return (np.exp(z.T) / np.sum(np.exp(z), axis=1)).T

    def _cross_entropy(self, output, y_target):

Instances