我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用chainer.functions.identity()。
def __init__(self, args): super(LSTM, self).__init__( # RNN LSTM=L.LSTM(args.n_in_units, args.n_units), #W_predict=L.Linear(args.n_units, args.n_units), W_candidate=L.Linear(args.n_in_units, args.n_units), ) #self.act1 = F.tanh self.act1 = F.identity self.args = args self.n_in_units = args.n_in_units self.n_units = args.n_units self.dropout_ratio = args.d_ratio self.margin = args.margin self.initialize_parameters()
def __init__(self, in_channels, out_channels, ksize=3, fiber_map='id', stride=1, pad=1, wscale=1, bias=0, nobias=False, use_cudnn=True, initialW=None, initial_bias=None): assert ksize % 2 == 1 assert pad == (ksize - 1) // 2 super(ResBlock, self).__init__( bn1=L.BatchNormalization(in_channels), conv1=L.Convolution2D(in_channels, out_channels, ksize, stride, pad, wscale), bn2=L.BatchNormalization(out_channels), conv2=L.Convolution2D(out_channels, out_channels, ksize, 1, pad, wscale), ) if fiber_map == 'id': assert in_channels == out_channels self.fiber_map = F.identity elif fiber_map == 'linear': self.add_link('fiber_map', L.Convolution2D(in_channels, out_channels, 1, 2, 0, wscale)) else: raise ValueError('Unimplemented fiber map {}'.format(fiber_map))
def __init__(self, in_channels, out_channels, ksize=3, fiber_map='id', conv_link=L.Convolution2D, stride=1, pad=1, wscale=1): assert ksize % 2 == 1 if not pad == (ksize - 1) // 2: raise NotImplementedError() super(ResBlock2D, self).__init__( bn1=L.BatchNormalization(in_channels), conv1=conv_link( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, wscale=wscale), bn2=L.BatchNormalization(out_channels), conv2=conv_link( in_channels=out_channels, out_channels=out_channels, ksize=ksize, stride=1, pad=pad, wscale=wscale) ) if fiber_map == 'id': if not in_channels == out_channels: raise ValueError('fiber_map cannot be identity when channel dimension is changed.') self.fiber_map = F.identity elif fiber_map == 'zero_pad': raise NotImplementedError() elif fiber_map == 'linear': fiber_map = conv_link( in_channels=in_channels, out_channels=out_channels, ksize=1, stride=stride, pad=0, wscale=wscale) self.add_link('fiber_map', fiber_map) else: raise ValueError('Unknown fiber_map: ' + str(type))
def house_transform(self,z): vec_t = self.qh_vec_0 for i in range(self.num_trans): vec_t = F.identity(self.qlin_h_vec_t(vec_t)) vec_t_product = F.matmul(vec_t, vec_t, transb=True) vec_t_norm_sqr = F.tile(F.sum(F.square(vec_t)), (z.shape[0], z.shape[1])) z = z - 2*F.matmul(vec_t_product, z)/vec_t_norm_sqr return z
def test_backward(self): x = chainer.Variable(numpy.array([1]), name='x') y1 = F.identity(x) y1.name = 'y1' y2 = F.identity(x) y2.name = 'y2' z = y1 + y2 z.name = 'z' z.grad = numpy.array([1]) z.backward(retain_grad=True) self.assertEqual(y1.grad[0], 1) self.assertEqual(y2.grad[0], 1) self.assertEqual(x.grad[0], 2)
def __init__(self, predictor, lossfun=identity, accuracyfun=accuracy, **links): super(Model, self).__init__(predictor=predictor, **links) self.lossfun = lossfun self.accuracyfun = accuracyfun self.y = None self.loss = None self.accuracy = None
def crop(inputs, outsize, offset): x = F.identity(inputs) crop_axis = [i!=j for i, j in zip(inputs.data.shape, outsize)] i = 0 for index, tf in enumerate(crop_axis): if tf: _, x, _ = F.split_axis(x, [offset[i], offset[i] + outsize[index]], index) i += 1 return x
