我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用chainer.functions.ReLU()。
def __init__(self, train=False): super(VGG16, self).__init__() self.trunk = [ ('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)), ('_relu1_1', F.ReLU()), ('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)), ('_relu1_2', F.ReLU()), ('_pool1', F.MaxPooling2D(2, 2)), ('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)), ('_relu2_1', F.ReLU()), ('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)), ('_relu2_2', F.ReLU()), ('_pool2', F.MaxPooling2D(2, 2)), ('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)), ('_relu3_1', F.ReLU()), ('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)), ('_relu3_2', F.ReLU()), ('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)), ('_relu3_3', F.ReLU()), ('_pool3', F.MaxPooling2D(2, 2)), ('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)), ('_relu4_1', F.ReLU()), ('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)), ('_relu4_2', F.ReLU()), ('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)), ('_relu4_3', F.ReLU()), ('_pool4', F.MaxPooling2D(2, 2)), ('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)), ('_relu5_1', F.ReLU()), ('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)), ('_relu5_2', F.ReLU()), ('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)), ('_relu5_3', F.ReLU()), ] for name, link in self.trunk: if not name.startswith('_'): self.add_link(name, link)
def check_backward(self, x_data, y_grad, use_cudnn=True): gradient_check.check_backward( functions.ReLU(use_cudnn), x_data, y_grad, **self.check_backward_options)
def __init__(self, train=False): super(VGG16, self).__init__() self.trunk = [ ('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)), ('relu1_1', F.ReLU()), ('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)), ('relu1_2', F.ReLU()), ('pool1', F.MaxPooling2D(2, 2)), ('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)), ('relu2_1', F.ReLU()), ('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)), ('relu2_2', F.ReLU()), ('pool2', F.MaxPooling2D(2, 2)), ('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)), ('relu3_1', F.ReLU()), ('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)), ('relu3_2', F.ReLU()), ('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)), ('relu3_3', F.ReLU()), ('pool3', F.MaxPooling2D(2, 2)), ('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)), ('relu4_1', F.ReLU()), ('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)), ('relu4_2', F.ReLU()), ('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)), ('relu4_3', F.ReLU()), ('pool4', F.MaxPooling2D(2, 2)), ('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)), ('relu5_1', F.ReLU()), ('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)), ('relu5_2', F.ReLU()), ('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)), ('relu5_3', F.ReLU()), ('rpn_conv_3x3', L.Convolution2D(512, 512, 3, 1, 1)), ('rpn_relu_3x3', F.ReLU()), ] for name, link in self.trunk: if 'conv' in name: self.add_link(name, link)
def __call__(self, x, train): param_num = 0 for name, f in self.forward: if 'conv1' in name: x = getattr(self, name)(x) param_num += (f.W.shape[0]*f.W.shape[2]*f.W.shape[3]*f.W.shape[1]+f.W.shape[0]) elif 'bn1' in name: x = getattr(self, name)(x, not train) param_num += x.data.shape[1]*2 return (F.relu(x), param_num) # [(CONV -> Batch -> ReLU -> CONV -> Batch) + (x)]
def __init__(self, ksize, n_out, initializer): super(ResBlock, self).__init__() pad_size = ksize // 2 links = [('conv1', L.Convolution2D(None, n_out, ksize, pad=pad_size, initialW=initializer))] links += [('bn1', L.BatchNormalization(n_out))] links += [('_act1', F.ReLU())] links += [('conv2', L.Convolution2D(n_out, n_out, ksize, pad=pad_size, initialW=initializer))] links += [('bn2', L.BatchNormalization(n_out))] for link in links: if not link[0].startswith('_'): self.add_link(*link) self.forward = links
def __init__(self, depth=18, alpha=16, start_channel=16, skip=False): super(PyramidNet, self).__init__() channel_diff = float(alpha) / depth channel = start_channel links = [('bconv1', BatchConv2D(3, channel, 3, 1, 1))] skip_size = depth * 3 - 3 for i in six.moves.range(depth): if skip: skip_ratio = float(i) / skip_size * 0.5 else: skip_ratio = 0 in_channel = channel channel += channel_diff links.append(('py{}'.format(len(links)), PyramidBlock(int(round(in_channel)), int(round(channel)), skip_ratio=skip_ratio))) in_channel = channel channel += channel_diff links.append(('py{}'.format(len(links)), PyramidBlock(int(round(in_channel)), int(round(channel)), stride=2))) for i in six.moves.range(depth - 1): if skip: skip_ratio = float(i + depth) / skip_size * 0.5 else: skip_ratio = 0 in_channel = channel channel += channel_diff links.append(('py{}'.format(len(links)), PyramidBlock(int(round(in_channel)), int(round(channel)), skip_ratio=skip_ratio))) in_channel = channel channel += channel_diff links.append(('py{}'.format(len(links)), PyramidBlock(int(round(in_channel)), int(round(channel)), stride=2))) for i in six.moves.range(depth - 1): if skip: skip_ratio = float(i + depth * 2 - 1) / skip_size * 0.5 else: skip_ratio = 0 in_channel = channel channel += channel_diff links.append(('py{}'.format(len(links)), PyramidBlock(int(round(in_channel)), int(round(channel)), skip_ratio=skip_ratio))) links.append(('bn{}'.format(len(links)), L.BatchNormalization(int(round(channel))))) links.append(('_relu{}'.format(len(links)), F.ReLU())) links.append(('_apool{}'.format(len(links)), F.AveragePooling2D(8, 1, 0, False))) links.append(('fc{}'.format(len(links)), L.Linear(int(round(channel)), 10))) for name, f in links: if not name.startswith('_'): self.add_link(*(name, f)) self.layers = links
