Python chainer.functions 模块，leaky_relu() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用chainer.functions.leaky_relu()。

项目：YOLO_chainer 作者：ashitani | 项目源码 | 文件源码

def  predict(self,x):
        h = F.leaky_relu(self.c1(x),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c3(h),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c5(h),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c7(h),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c9(h),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c11(h),slope=0.1)
        h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
        h = F.leaky_relu(self.c13(h),slope=0.1)
        h = F.leaky_relu(self.c14(h),slope=0.1)
        h = F.leaky_relu(self.c15(h),slope=0.1)
        h = F.leaky_relu(self.l16(h),slope=0.1)
        h = F.leaky_relu(self.l17(h),slope=0.1)
        # skip dropout
        h = self.l19(h)

        return h

项目：ddnn 作者：kunglab | 项目源码 | 文件源码

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        if self.nonlinearity.lower() == "bst":
            return bst()
        raise NotImplementedError()

项目：chainer-pix2pix 作者：wuhuikai | 项目源码 | 文件源码

def __call__(self, x, test=False, dropout=True):
        e1 = self.c1(x)
        e2 = self.b2(self.c2(F.leaky_relu(e1)), test=test)
        e3 = self.b3(self.c3(F.leaky_relu(e2)), test=test)
        e4 = self.b4(self.c4(F.leaky_relu(e3)), test=test)
        e5 = self.b5(self.c5(F.leaky_relu(e4)), test=test)
        e6 = self.b6(self.c6(F.leaky_relu(e5)), test=test)
        e7 = self.b7(self.c7(F.leaky_relu(e6)), test=test)
        e8 = self.b8(self.c8(F.leaky_relu(e7)), test=test)
        d1 = F.concat((F.dropout(self.b1_d(self.dc1(F.relu(e8)), test=test), train=dropout), e7))
        d2 = F.concat((F.dropout(self.b2_d(self.dc2(F.relu(d1)), test=test), train=dropout), e6))
        d3 = F.concat((F.dropout(self.b3_d(self.dc3(F.relu(d2)), test=test), train=dropout), e5))
        d4 = F.concat((self.b4_d(self.dc4(F.relu(d3)), test=test), e4))
        d5 = F.concat((self.b5_d(self.dc5(F.relu(d4)), test=test), e3))
        d6 = F.concat((self.b6_d(self.dc6(F.relu(d5)), test=test), e2))
        d7 = F.concat((self.b7_d(self.dc7(F.relu(d6)), test=test), e1))
        y = F.tanh(self.dc8(F.relu(d7)))

        return y

项目：chainer_frmqn 作者：okdshin | 项目源码 | 文件源码

def main():
    class PoleModel(Chain):
        def __init__(self, input_num, action_num):
            print(input_num, action_num)
            super(PoleModel, self).__init__(
                l1=L.Linear(input_num, 32),
                l2=L.Linear(32, 32),
                l3=L.Linear(32, action_num)
            )

        def q_function(self, state):
            h1 = F.leaky_relu(self.l1(state))
            h2 = F.leaky_relu(self.l2(h1))
            return self.l3(h2)

    dqn = DeepQNet(state_shape=(3, 32, 32), action_num=2, image_num_per_state=12,
            model=PoleModel(3*12*32*32, action_num=2))

项目：unrolled-gan 作者：musyoku | 项目源码 | 文件源码

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

项目：SeRanet 作者：corochann | 项目源码 | 文件源码

def __call__(self, x, t=None):
        self.clear()
        #x = Variable(x_data)  # x_data.astype(np.float32)

        h = F.leaky_relu(self.conv1(x), slope=0.1)
        h = F.leaky_relu(self.conv2(h), slope=0.1)
        h = F.leaky_relu(self.conv3(h), slope=0.1)
        h = F.leaky_relu(self.conv4(h), slope=0.1)
        h = F.leaky_relu(self.conv5(h), slope=0.1)
        h = F.leaky_relu(self.conv6(h), slope=0.1)
        h = F.clipped_relu(self.conv7(h), z=1.0)
        if self.train:
            self.loss = F.mean_squared_error(h, t)
            return self.loss
        else:
            return h

项目：LSGAN 作者：musyoku | 项目源码 | 文件源码

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

项目：adgm 作者：musyoku | 项目源码 | 文件源码

def to_function(self):
        if self.nonlinearity.lower() == "clipped_relu":
            return clipped_relu()
        if self.nonlinearity.lower() == "crelu":
            return crelu()
        if self.nonlinearity.lower() == "elu":
            return elu()
        if self.nonlinearity.lower() == "hard_sigmoid":
            return hard_sigmoid()
        if self.nonlinearity.lower() == "leaky_relu":
            return leaky_relu()
        if self.nonlinearity.lower() == "relu":
            return relu()
        if self.nonlinearity.lower() == "sigmoid":
            return sigmoid()
        if self.nonlinearity.lower() == "softmax":
            return softmax()
        if self.nonlinearity.lower() == "softplus":
            return softplus()
        if self.nonlinearity.lower() == "tanh":
            return tanh()
        raise NotImplementedError()

项目：chainer-gan-experiments 作者：Aixile | 项目源码 | 文件源码

def differentiable_backward(self, g):
        if self.normalize_input:
            raise NotImplementedError

        if self.activation is F.leaky_relu:
            g = backward_leaky_relu(self.x, g)
        elif self.activation is F.relu:
            g = backward_relu(self.x, g)
        elif self.activation is F.tanh:
            g = backward_tanh(self.x, g)
        elif self.activation is F.sigmoid:
            g = backward_sigmoid(self.x, g)
        elif not self.activation is None:
            raise NotImplementedError

        if self.norm == 'ln':
            g = backward_layernormalization(self.nx, g, self.n)
        elif not self.norm is None:
            raise NotImplementedError

        if self.nn == 'down_conv' or self.nn == 'conv':
            g = backward_convolution(None, g, self.c)
        elif self.nn == 'linear':
            g = backward_linear(None, g, self.c)
        elif self.nn == 'up_deconv':
            g = backward_deconvolution(None, g, self.c)
        else:
            raise NotImplementedError

        return g

项目：chainer-gan-experiments 作者：Aixile | 项目源码 | 文件源码

def __init__(self, in_ch=3, base_size=128, down_layers=4, use_bn=True, noise_all_layers=False, conv_as_last=False, w_init=None):
        layers = {}

        self.down_layers = down_layers
        self.conv_as_last = conv_as_last

        if use_bn:
            norm = 'bn'
        else:
            norm = None

        act = F.leaky_relu
        if w_init is None:
            w_init = chainer.initializers.Normal(0.02)

        layers['c_first'] = NNBlock(in_ch, base_size, nn='down_conv', norm=None, activation=act, noise=noise_all_layers, w_init=w_init)
        base = base_size

        for i in range(down_layers-1):
            layers['c'+str(i)] = NNBlock(base, base*2, nn='down_conv', norm=norm, activation=act, noise=noise_all_layers, w_init=w_init)
            base*=2

        if conv_as_last:
            layers['c_last'] = NNBlock(base, 1, nn='conv', norm=None, activation=None, w_init=w_init)
        else:
            layers['c_last'] = NNBlock(None, 1, nn='linear', norm=None, activation=None, w_init=w_init)

        super(DCGANDiscriminator, self).__init__(**layers)

项目：chainer-gan-experiments 作者：Aixile | 项目源码 | 文件源码

def __init__(self, in_ch=3, base_size=128, down_layers=4, use_bn=True, w_init=None, output_len=38):
        layers = {}

        self.down_layers = down_layers

        if use_bn:
            norm = 'bn'
        else:
            norm = None

        act = F.leaky_relu
        if w_init is None:
            w_init = chainer.initializers.Normal(0.02)

        layers['c_first'] = NNBlock(in_ch, base_size, nn='down_conv', norm=None, activation=act, w_init=w_init)
        base = base_size

        for i in range(down_layers-1):
            layers['c'+str(i)] = NNBlock(base, base*2, nn='down_conv', norm=norm, activation=act, w_init=w_init)
            base*=2

        layers['c_last_0'] = NNBlock(None, 1, nn='linear', norm=None, activation=None, w_init=w_init)
        layers['c_last_1_0'] = NNBlock(None, output_len, nn='linear', norm=None, activation=None, w_init=None)
        #layers['c_last_1_1'] = NNBlock(1024, 1024, nn='linear', norm=None, activation=F.leaky_relu, w_init=None)
        #layers['c_last_1_2'] = NNBlock(1024, output_len, nn='linear', norm=None, activation=None, w_init=None)

        super(ACGANDiscriminator, self).__init__(**layers)

项目：chainer-gan-experiments 作者：Aixile | 项目源码 | 文件源码

def __init__(self, hidden_size=768, output_size=1, use_bn=True):
        if use_bn:
            norm = 'bn'
            w_init=None
        else:
            norm = None
            w_init=None
            #w_init=Chainer.initializers.HeNormal()

        super(ThreeLayersMLP, self).__init__(
            l0 = NNBlock(None, hidden_size, norm=norm, nn='linear', w_init=w_init, activation=F.leaky_relu),
            l1 = NNBlock(hidden_size, hidden_size, norm=norm, nn='linear', w_init=w_init, activation=F.leaky_relu),
            l2 = NNBlock(hidden_size, output_size, norm=None, activation=None, nn='linear', w_init=w_init),
        )

项目：chainer-object-detection 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.bias1(self.bn1(self.conv1(x), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias2(self.bn2(self.conv2(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias3(self.bn3(self.conv3(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias4(self.bn4(self.conv4(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias5(self.bn5(self.conv5(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias6(self.bn6(self.conv6(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias7(self.bn7(self.conv7(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias8(self.bn8(self.conv8(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias9(self.bn9(self.conv9(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias10(self.bn10(self.conv10(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias11(self.bn11(self.conv11(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias12(self.bn12(self.conv12(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias13(self.bn13(self.conv13(h), finetune=self.finetune)), slope=0.1)
        high_resolution_feature = h
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias14(self.bn14(self.conv14(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias15(self.bn15(self.conv15(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias16(self.bn16(self.conv16(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias17(self.bn17(self.conv17(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias18(self.bn18(self.conv18(h), finetune=self.finetune)), slope=0.1)

        h = F.leaky_relu(self.bias19(self.bn19(self.conv19(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias20(self.bn20(self.conv20(h), finetune=self.finetune)), slope=0.1)

        h2 = high_resolution_feature
        h2 = F.leaky_relu(self.bias21(self.bn21(self.conv21(h2), finetune=self.finetune)), slope=0.1)
        h2 = reorg(h2)

        h = F.concat((h2, h), axis=1)
        h = F.leaky_relu(self.bias22(self.bn22(self.conv22(h), finetune=self.finetune)), slope=0.1)

        h = self.bias23(self.conv23(h))

        return h

项目：chainer-object-detection 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.bias1(self.bn1(self.conv1(x), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.dropout(h, 0.25)
        h = F.leaky_relu(self.bias2(self.bn2(self.conv2(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.dropout(h, 0.25)
        h = F.leaky_relu(self.bias3(self.bn3(self.conv3(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias4(self.bn4(self.conv4(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias5(self.bn5(self.conv5(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.dropout(h, 0.25)
        h = F.leaky_relu(self.bias6(self.bn6(self.conv6(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias7(self.bn7(self.conv7(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias8(self.bn8(self.conv8(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.dropout(h, 0.25)
        h = F.leaky_relu(self.bias9(self.bn9(self.conv9(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias10(self.bn10(self.conv10(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias11(self.bn11(self.conv11(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias12(self.bn12(self.conv12(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias13(self.bn13(self.conv13(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.dropout(h, 0.25)
        h = F.leaky_relu(self.bias14(self.bn14(self.conv14(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias15(self.bn15(self.conv15(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias16(self.bn16(self.conv16(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias17(self.bn17(self.conv17(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias18(self.bn18(self.conv18(h), finetune=self.finetune)), slope=0.1)
        h = F.average_pooling_2d(h, h.shape[-2:])
        h = self.fc19(h)
        return h

项目：chainer-object-detection 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.bias1(self.bn1(self.conv1(x), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias2(self.bn2(self.conv2(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias3(self.bn3(self.conv3(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias4(self.bn4(self.conv4(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias5(self.bn5(self.conv5(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias6(self.bn6(self.conv6(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias7(self.bn7(self.conv7(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias8(self.bn8(self.conv8(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias9(self.bn9(self.conv9(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias10(self.bn10(self.conv10(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias11(self.bn11(self.conv11(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias12(self.bn12(self.conv12(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias13(self.bn13(self.conv13(h), finetune=self.finetune)), slope=0.1)
        high_resolution_feature = h
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias14(self.bn14(self.conv14(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias15(self.bn15(self.conv15(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias16(self.bn16(self.conv16(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias17(self.bn17(self.conv17(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias18(self.bn18(self.conv18(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias19(self.bn19(self.conv19(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias20(self.bn20(self.conv20(h), finetune=self.finetune)), slope=0.1)

        h2 = high_resolution_feature
        h2 = F.leaky_relu(self.bias21(self.bn21(self.conv21(h2), finetune=self.finetune)), slope=0.1)
        h2 = reorg(h2)

        h = F.concat((h2, h), axis=1)
        h = F.leaky_relu(self.bias22(self.bn22(self.conv22(h), finetune=self.finetune)), slope=0.1)

        h = self.bias23(self.conv23(h))

        return h

项目：chainer-object-detection 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.bias1(self.bn1(self.conv1(x), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias2(self.bn2(self.conv2(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias3(self.bn3(self.conv3(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias4(self.bn4(self.conv4(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias5(self.bn5(self.conv5(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias6(self.bn6(self.conv6(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias7(self.bn7(self.conv7(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias8(self.bn8(self.conv8(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias9(self.bn9(self.conv9(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias10(self.bn10(self.conv10(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias11(self.bn11(self.conv11(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias12(self.bn12(self.conv12(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias13(self.bn13(self.conv13(h), finetune=self.finetune)), slope=0.1)
        h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
        h = F.leaky_relu(self.bias14(self.bn14(self.conv14(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias15(self.bn15(self.conv15(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias16(self.bn16(self.conv16(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias17(self.bn17(self.conv17(h), finetune=self.finetune)), slope=0.1)
        h = F.leaky_relu(self.bias18(self.bn18(self.conv18(h), finetune=self.finetune)), slope=0.1)
        h = F.average_pooling_2d(h, h.shape[-2:])
        h = self.fc19(h)
        return h

项目：chainerrl 作者：chainer | 项目源码 | 文件源码

def parse_activation(activation_str):
    if activation_str == 'relu':
        return F.relu
    elif activation_str == 'elu':
        return F.elu
    elif activation_str == 'lrelu':
        return F.leaky_relu
    else:
        raise RuntimeError(
            'Not supported activation: {}'.format(activation_str))

项目：chainer-cyclegan 作者：Aixile | 项目源码 | 文件源码

def __init__(self, in_ch=3, n_down_layers=4):
        layers = {}
        w = chainer.initializers.Normal(0.02)
        self.n_down_layers = n_down_layers

        layers['c0'] = CBR(in_ch, 64, bn=False, sample='down', activation=F.leaky_relu, dropout=False, noise=True)
        base = 64

        for i in range(1, n_down_layers):
            layers['c'+str(i)] = CBR(base, base*2, bn=True, sample='down', activation=F.leaky_relu, dropout=False, noise=True)
            base*=2

        layers['c'+str(n_down_layers)] = CBR(base, 1, bn=False, sample='none', activation=None, dropout=False, noise=True)

        super(Discriminator, self).__init__(**layers)

项目：tensorboard-pytorch 作者：lanpa | 项目源码 | 文件源码

def __call__(self, x):
        h = add_noise(x)
        h = F.leaky_relu(add_noise(self.c0_0(h)))
        h = F.leaky_relu(add_noise(self.bn0_1(self.c0_1(h))))
        h = F.leaky_relu(add_noise(self.bn1_0(self.c1_0(h))))
        h = F.leaky_relu(add_noise(self.bn1_1(self.c1_1(h))))
        h = F.leaky_relu(add_noise(self.bn2_0(self.c2_0(h))))
        h = F.leaky_relu(add_noise(self.bn2_1(self.c2_1(h))))
        h = F.leaky_relu(add_noise(self.bn3_0(self.c3_0(h))))
        return self.l4(h)

项目：chainer-stack-gan 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x, train=True):
        return F.leaky_relu(self.bn(self.conv(x), test=not train))

项目：chainer-stack-gan 作者：dsanno | 项目源码 | 文件源码

def __call__(self, x, train=True):
        links = self.children()
        h = F.leaky_relu(next(links)(x))
        for link in links:
            h = link(h, train)
        return h

项目：chainer-speech-recognition 作者：musyoku | 项目源码 | 文件源码

def __call__(self, x):
        return functions.leaky_relu(x, self.slope)

项目：chainer-deconv 作者：germanRos | 项目源码 | 文件源码

def _setup_relu(self, layer):
        slope = layer.relu_param.negative_slope

        if slope != 0:
            fw = _SingleArgumentFunction(functions.leaky_relu, slope=slope)
        else:
            fw = functions.relu

        self.forwards[layer.name] = fw
        self._add_layer(layer)

项目：chainer-deconv 作者：germanRos | 项目源码 | 文件源码

def check_forward(self, x_data):
        x = chainer.Variable(x_data)
        y = functions.leaky_relu(x, slope=self.slope)
        self.assertEqual(y.data.dtype, self.dtype)

        expected = self.x.copy()
        for i in numpy.ndindex(self.x.shape):
            if self.x[i] < 0:
                expected[i] *= self.slope

        gradient_check.assert_allclose(
            expected, y.data, **self.check_forward_options)

项目：chainer-wasserstein-gan 作者：hvy | 项目源码 | 文件源码

def __call__(self, x, test=False):
        h = F.leaky_relu(self.c0(x))
        h = F.leaky_relu(self.bn_c1(self.c1(h), test=test))
        h = F.leaky_relu(self.bn_c2(self.c2(h), test=test))
        h = self.c3(h)
        h = F.sum(h) / h.size  # Mean
        return h

项目：ddnn 作者：kunglab | 项目源码 | 文件源码

def __init__(self, slope=0.2):
        self._function = "leaky_relu"
        self.slope = slope

项目：ddnn 作者：kunglab | 项目源码 | 文件源码

def __call__(self, x):
        return F.leaky_relu(x, self.slope)

项目：adversarial-autoencoder 作者：musyoku | 项目源码 | 文件源码

def __call__(self, x):
        return functions.leaky_relu(x, self.slope)

项目：chainer-LSGAN 作者：pfnet-research | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.c0(x))   
        h = F.leaky_relu(self.bn1(self.c1(h)))
        h = F.leaky_relu(self.bn2(self.c2(h)))
        h = F.leaky_relu(self.bn3(self.c3(h)))
        l = self.l4l(h)
        return l

项目：deel 作者：uei | 项目源码 | 文件源码

def _setup_relu(self, layer):
        slope = layer.relu_param.negative_slope

        if slope != 0:
            fw = _SingleArgumentFunction(functions.leaky_relu, slope=slope)
        else:
            fw = functions.relu

        self.forwards[layer.name] = fw
        self._add_layer(layer)

项目：chainermn 作者：chainer | 项目源码 | 文件源码

def __call__(self, x):
        h = add_noise(x)
        h = F.leaky_relu(add_noise(self.c0_0(h)))
        h = F.leaky_relu(add_noise(self.bn0_1(self.c0_1(h))))
        h = F.leaky_relu(add_noise(self.bn1_0(self.c1_0(h))))
        h = F.leaky_relu(add_noise(self.bn1_1(self.c1_1(h))))
        h = F.leaky_relu(add_noise(self.bn2_0(self.c2_0(h))))
        h = F.leaky_relu(add_noise(self.bn2_1(self.c2_1(h))))
        h = F.leaky_relu(add_noise(self.bn3_0(self.c3_0(h))))
        return self.l4(h)

项目：chainer-pix2pix 作者：wuhuikai | 项目源码 | 文件源码

def __call__(self, x, test=False):
        h = F.leaky_relu(self.c0(x))

        for idx in range(1, self.n_layers):
            h = F.leaky_relu(self['b{}'.format(idx)](self['c{}'.format(idx)](h), test=test))

        h = F.leaky_relu(self['b{}'.format(self.n_layers)](self['c{}'.format(self.n_layers)](h), test=test))
        h = F.sigmoid(self.c(h))

        return h

项目：chainer-pix2pix 作者：pfnet-research | 项目源码 | 文件源码

def __init__(self, in_ch):
        layers = {}
        w = chainer.initializers.Normal(0.02)
        layers['c0'] = L.Convolution2D(in_ch, 64, 3, 1, 1, initialW=w)
        layers['c1'] = CBR(64, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c2'] = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c3'] = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c4'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c5'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c6'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c7'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        super(Encoder, self).__init__(**layers)

项目：chainer-pix2pix 作者：pfnet-research | 项目源码 | 文件源码

def __call__(self, x):
        hs = [F.leaky_relu(self.c0(x))]
        for i in range(1,8):
            hs.append(self['c%d'%i](hs[i-1]))
        return hs

项目：chainer-pix2pix 作者：pfnet-research | 项目源码 | 文件源码

def __init__(self, in_ch, out_ch):
        layers = {}
        w = chainer.initializers.Normal(0.02)
        layers['c0_0'] = CBR(in_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c0_1'] = CBR(out_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c1'] = CBR(64, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c2'] = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c3'] = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c4'] = L.Convolution2D(512, 1, 3, 1, 1, initialW=w)
        super(Discriminator, self).__init__(**layers)

项目：chainer-examples 作者：nocotan | 项目源码 | 文件源码

def __call__(self, x):
        h = F.leaky_relu(self.c0_0(x))
        h = F.leaky_relu(self.bn0_1(self.c0_1(h)))
        h = F.leaky_relu(self.bn1_0(self.c1_0(h)))
        h = F.leaky_relu(self.bn1_1(self.c1_1(h)))
        h = F.leaky_relu(self.bn2_0(self.c2_0(h)))
        h = F.leaky_relu(self.bn2_1(self.c2_1(h)))
        h = F.leaky_relu(self.bn3_0(self.c3_0(h)))
        return self.l4(h)

项目：chainer-examples 作者：nocotan | 项目源码 | 文件源码

def __call__(self, x):
        h = x
        h = F.leaky_relu(self.c0(h))
        h = F.leaky_relu(self.c1(h))
        h = F.leaky_relu(self.c2(h))
        h = F.leaky_relu(self.c3(h))
        h = F.leaky_relu(self.l4(h))
        h = F.reshape(F.leaky_relu(self.l5(h)),
                      (x.data.shape[0], self.ch, 4, 4))
        h = F.leaky_relu(self.dc3(h))
        h = F.leaky_relu(self.dc2(h))
        h = F.leaky_relu(self.dc1(h))
        h = F.tanh(self.dc0(h))
        return F.mean_absolute_error(h, x)

项目：chainer-examples 作者：nocotan | 项目源码 | 文件源码

def __call__(self, x):
        h = self.bn1(F.leaky_relu(self.l1(x)), test=not self.train)
        h = self.bn2(F.leaky_relu(self.l2(h)), test=not self.train)
        h = self.bn3(F.leaky_relu(self.l3(h)), test=not self.train)
        h = self.bn4(F.leaky_relu(self.l4(h)), test=not self.train)
        y = self.l5(h)
        return y

项目：pose2img 作者：Hi-king | 项目源码 | 文件源码

def __init__(self, in_ch):
        layers = {}
        w = chainer.initializers.Normal(0.02)
        layers['c0'] = L.Convolution2D(in_ch, 64, 3, 1, 1, initialW=w)
        layers['c1'] = CBR(64, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c2'] = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c3'] = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c4'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c5'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c6'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c7'] = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        super(Encoder, self).__init__(**layers)

项目：pose2img 作者：Hi-king | 项目源码 | 文件源码

def __call__(self, x):
        hs = [F.leaky_relu(self.c0(x))]
        for i in range(1, 8):
            hs.append(self['c%d' % i](hs[i - 1]))
        return hs

项目：pose2img 作者：Hi-king | 项目源码 | 文件源码

def __init__(self, in_ch, out_ch, will_concat=True, layers={}):
        self.will_concat = will_concat
        channel_expansion = 2 if will_concat else 1
        w = chainer.initializers.Normal(0.02)
        layers['c0_0'] = CBR(in_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c0_1'] = CBR(out_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c1'] = CBR(32 * channel_expansion, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c2'] = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c3'] = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False)
        layers['c4'] = L.Convolution2D(512, 1, 3, 1, 1, initialW=w)
        super(Discriminator, self).__init__(**layers)

项目：chainer-ADDA 作者：pfnet-research | 项目源码 | 文件源码

def __call__(self, x):
        l1 = F.leaky_relu(self.l1(x))
        l2 = F.leaky_relu(self.l2(l1))
        out = self.l3(l2)
        return out