nn.py 文件源码

def softmax_loss(p_true, output_before_softmax):
    output_before_softmax -= T.max(output_before_softmax, axis=1, keepdims=True)
    if p_true.ndim==2:
        return T.mean(T.log(T.sum(T.exp(output_before_softmax),axis=1)) - T.sum(p_true*output_before_softmax, axis=1))
    else:
        return T.mean(T.log(T.sum(T.exp(output_before_softmax),axis=1)) - output_before_softmax[T.arange(p_true.shape[0]),p_true])