def forward_gpu(self, inputs):
x, t = inputs
self.y, = Softmax(self.use_cudnn).forward_gpu((x, ))
ret = cuda.reduce('int* t, float* y, int n_channel',
'-log(y[i * n_channel + t[i]])', 'a+b', '0',
'crossent_fwd', numpy.float32)(t, self.y,
self.y.shape[1])
ret /= t.size
return ret,
def forward_cpu(self, inputs):
x, t = inputs
self.y, = Softmax().forward_cpu((x, ))
return -numpy.log(self.y[range(len(t)),
t]).sum(keepdims=True) / t.size,
