def __call__(self, x):
h = x
for layer in self.hidden_layers:
h = F.relu(layer(h))
#for
p = F.softmax(self.output_layer(h))
return D.Categorical(p)
def make_categorical_dist(self, is_gpu=False):
p = numpy.random.normal(size=self.shape + (3, )).astype(numpy.float32)
p = numpy.exp(p)
p /= numpy.expand_dims(p.sum(axis=-1), axis=-1)
params = self.encode_params({"p": p}, is_gpu)
return distributions.Categorical(**params)
