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Python layers.ReLU方法代码示例

本文整理汇总了Python中keras.layers.ReLU方法的典型用法代码示例。如果您正苦于以下问题:Python layers.ReLU方法的具体用法?Python layers.ReLU怎么用?Python layers.ReLU使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在keras.layers的用法示例。


在下文中一共展示了layers.ReLU方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: emit_Relu6

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def emit_Relu6(self, IR_node, in_scope=False):
        try:
            # Keras == 2.1.6
            from keras.applications.mobilenet import relu6
            str_relu6 = 'keras.applications.mobilenet.relu6'
            code = "{:<15} = layers.Activation({}, name = '{}')({})".format(
                IR_node.variable_name,
                str_relu6,
                IR_node.name,
                self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name)
            return code

        except:
            # Keras == 2.2.2
            from keras.layers import ReLU
            code = "{:<15} = layers.ReLU(6, name = '{}')({})".format(
                IR_node.variable_name,
                IR_node.name,
                self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name)
            return code 
开发者ID:microsoft,项目名称:MMdnn,代码行数:22,代码来源:keras2_emitter.py

示例2: shortcut_pool

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def shortcut_pool(inputs, output, filters=256, pool_type='max', shortcut=True):
    """
        ResNet(shortcut连接|skip连接|residual连接), 
        这里是用shortcut连接. 恒等映射, block+f(block)
        再加上 downsampling实现
        参考: https://github.com/zonetrooper32/VDCNN/blob/keras_version/vdcnn.py
    :param inputs: tensor
    :param output: tensor
    :param filters: int
    :param pool_type: str, 'max'、'k-max' or 'conv' or other
    :param shortcut: boolean
    :return: tensor
    """
    if shortcut:
        conv_2 = Conv1D(filters=filters, kernel_size=1, strides=2, padding='SAME')(inputs)
        conv_2 = BatchNormalization()(conv_2)
        output = downsampling(output, pool_type=pool_type)
        out = Add()([output, conv_2])
    else:
        out = ReLU(inputs)
        out = downsampling(out, pool_type=pool_type)
    if pool_type is not None: # filters翻倍
        out = Conv1D(filters=filters*2, kernel_size=1, strides=1, padding='SAME')(out)
        out = BatchNormalization()(out)
    return out 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:27,代码来源:graph.py

示例3: initial_oct_conv_bn_relu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def initial_oct_conv_bn_relu(ip, filters, kernel_size=(3, 3), strides=(1, 1),
                             alpha=0.5, padding='same', dilation=None, bias=False,
                             activation=True):

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x_high, x_low = initial_octconv(ip, filters, kernel_size, strides, alpha,
                                    padding, dilation, bias)

    relu = ReLU()
    x_high = BatchNormalization(axis=channel_axis)(x_high)
    if activation:
        x_high = relu(x_high)

    x_low = BatchNormalization(axis=channel_axis)(x_low)
    if activation:
        x_low = relu(x_low)

    return x_high, x_low 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:21,代码来源:octave_conv_block.py

示例4: oct_conv_bn_relu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def oct_conv_bn_relu(ip_high, ip_low, filters, kernel_size=(3, 3), strides=(1, 1),
                     alpha=0.5, padding='same', dilation=None, bias=False, activation=True):

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x_high, x_low = octconv_block(ip_high, ip_low, filters, kernel_size, strides, alpha,
                                  padding, dilation, bias)

    relu = ReLU()
    x_high = BatchNormalization(axis=channel_axis)(x_high)
    if activation:
        x_high = relu(x_high)

    x_low = BatchNormalization(axis=channel_axis)(x_low)
    if activation:
        x_low = relu(x_low)

    return x_high, x_low 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:20,代码来源:octave_conv_block.py

示例5: _bottleneck_original

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def _bottleneck_original(ip, filters, strides=(1, 1), downsample_shortcut=False,
                         expansion=4):

    final_filters = int(filters * expansion)

    shortcut = ip

    x = _conv_bn_relu(ip, filters, kernel_size=(1, 1))
    x = _conv_bn_relu(x, filters, kernel_size=(3, 3), strides=strides)
    x = _conv_bn_relu(x, final_filters, kernel_size=(1, 1), activation=False)

    if downsample_shortcut:
        shortcut = _conv_block(shortcut, final_filters, kernel_size=(1, 1),
                               strides=strides)

    x = add([x, shortcut])
    x = ReLU()(x)

    return x 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:21,代码来源:octave_resnet.py

示例6: build_generator

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def build_generator():
    gen_model = Sequential()

    gen_model.add(Dense(input_dim=100, output_dim=2048))
    gen_model.add(ReLU())

    gen_model.add(Dense(256 * 8 * 8))
    gen_model.add(BatchNormalization())
    gen_model.add(ReLU())
    gen_model.add(Reshape((8, 8, 256), input_shape=(256 * 8 * 8,)))
    gen_model.add(UpSampling2D(size=(2, 2)))

    gen_model.add(Conv2D(128, (5, 5), padding='same'))
    gen_model.add(ReLU())

    gen_model.add(UpSampling2D(size=(2, 2)))

    gen_model.add(Conv2D(64, (5, 5), padding='same'))
    gen_model.add(ReLU())

    gen_model.add(UpSampling2D(size=(2, 2)))

    gen_model.add(Conv2D(3, (5, 5), padding='same'))
    gen_model.add(Activation('tanh'))
    return gen_model 
开发者ID:PacktPublishing,项目名称:Generative-Adversarial-Networks-Projects,代码行数:27,代码来源:run.py

示例7: call

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def call(self, x):
        return nn.ReLU(max_value=6.0)(x) 
开发者ID:osmr,项目名称:imgclsmob,代码行数:4,代码来源:common.py

示例8: get_activation_layer

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def get_activation_layer(x,
                         activation,
                         name="activ"):
    """
    Create activation layer from string/function.

    Parameters:
    ----------
    x : keras.backend tensor/variable/symbol
        Input tensor/variable/symbol.
    activation : function or str
        Activation function or name of activation function.
    name : str, default 'activ'
        Block name.

    Returns
    -------
    keras.backend tensor/variable/symbol
        Resulted tensor/variable/symbol.
    """
    assert (activation is not None)
    if isfunction(activation):
        x = activation()(x)
    elif isinstance(activation, str):
        if activation == "relu":
            x = nn.Activation("relu", name=name)(x)
        elif activation == "relu6":
            x = nn.ReLU(max_value=6.0, name=name)(x)
        elif activation == "swish":
            x = swish(x=x, name=name)
        elif activation == "hswish":
            x = HSwish(name=name)(x)
        else:
            raise NotImplementedError()
    else:
        x = activation(x)
    return x 
开发者ID:osmr,项目名称:imgclsmob,代码行数:39,代码来源:common.py

示例9: ResidualBlock

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def ResidualBlock(self, inp, dim_out):
        """Residual Block with instance normalization."""
        x = ZeroPadding2D(padding = 1)(inp)
        x = Conv2D(filters = dim_out, kernel_size=3, strides=1, padding='valid', use_bias = False)(x)
        x = InstanceNormalization(axis = -1)(x)
        x = ReLU()(x)
        x = ZeroPadding2D(padding = 1)(x)
        x = Conv2D(filters = dim_out, kernel_size=3, strides=1, padding='valid', use_bias = False)(x)
        x = InstanceNormalization(axis = -1)(x)
        return Add()([inp, x]) 
开发者ID:hoangthang1607,项目名称:StarGAN-Keras,代码行数:12,代码来源:StarGAN.py

示例10: __init__

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def __init__(self, model):
        super(Keras2Parser, self).__init__()

        # load model files into Keras graph
        if isinstance(model, _string_types):
            try:
                # Keras 2.1.6
                from keras.applications.mobilenet import relu6
                from keras.applications.mobilenet import DepthwiseConv2D
                model = _keras.models.load_model(
                    model,
                    custom_objects={
                        'relu6': _keras.applications.mobilenet.relu6,
                        'DepthwiseConv2D': _keras.applications.mobilenet.DepthwiseConv2D
                    }
                )
            except:
                # Keras. 2.2.2
                import keras.layers as layers
                model = _keras.models.load_model(
                    model,
                    custom_objects={
                        'relu6': layers.ReLU(6, name='relu6'),
                        'DepthwiseConv2D': layers.DepthwiseConv2D
                    }
                )
            self.weight_loaded = True

        elif isinstance(model, tuple):
            model = self._load_model(model[0], model[1])

        else:
            assert False

        # _keras.utils.plot_model(model, "model.png", show_shapes = True)

        # Build network graph
        self.data_format = _keras.backend.image_data_format()
        self.keras_graph = Keras2Graph(model)
        self.keras_graph.build()
        self.lambda_layer_count = 0 
开发者ID:microsoft,项目名称:MMdnn,代码行数:43,代码来源:keras2_parser.py

示例11: create_model

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def create_model(self, hyper_parameters):
        """
            构建神经网络
        :param hyper_parameters:json,  hyper parameters of network
        :return: tensor, moedl
        """
        super().create_model(hyper_parameters)
        embedding_output = self.word_embedding.output
        embedding_output_spatial = SpatialDropout1D(self.dropout_spatial)(embedding_output)

        # 首先是 region embedding 层
        conv_1 = Conv1D(self.filters[0][0],
                        kernel_size=1,
                        strides=1,
                        padding='SAME',
                        kernel_regularizer=l2(self.l2),
                        bias_regularizer=l2(self.l2),
                        activation=self.activation_conv,
                        )(embedding_output_spatial)
        block = ReLU()(conv_1)

        for filters_block in self.filters:
            for j in range(filters_block[1]-1):
                # conv + short-cut
                block_mid = self.convolutional_block(block, units=filters_block[0])
                block = shortcut_conv(block, block_mid, shortcut=True)
            # 这里是conv + max-pooling
            block_mid = self.convolutional_block(block, units=filters_block[0])
            block = shortcut_pool(block, block_mid, filters=filters_block[0], pool_type=self.pool_type, shortcut=True)

        block = k_max_pooling(top_k=self.top_k)(block)
        block = Flatten()(block)
        block = Dropout(self.dropout)(block)
        # 全连接层
        # block_fully = Dense(2048, activation='tanh')(block)
        # output = Dense(2048, activation='tanh')(block_fully)
        output = Dense(self.label, activation=self.activate_classify)(block)
        self.model = Model(inputs=self.word_embedding.input, outputs=output)
        self.model.summary(120) 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:41,代码来源:graph.py

示例12: convolutional_block

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def convolutional_block(self, inputs, units=256):
        """
            Each convolutional block (see Figure 2) is a sequence of two convolutional layers, 
            each one followed by a temporal BatchNorm (Ioffe and Szegedy, 2015) layer and an ReLU activation. 
            The kernel size of all the temporal convolutions is 3, 
            with padding such that the temporal resolution is preserved 
            (or halved in the case of the convolutional pooling with stride 2, see below). 
        :param inputs: tensor, input
        :param units: int, units
        :return: tensor, result of convolutional block
        """
        x = Conv1D(units,
                    kernel_size=3,
                    padding='SAME',
                    strides=1,
                    kernel_regularizer=l2(self.l2),
                    bias_regularizer=l2(self.l2),
                    activation=self.activation_conv,
                    )(inputs)
        x = BatchNormalization()(x)
        x = ReLU()(x)
        x = Conv1D(units,
                    kernel_size=3,
                    strides=1,
                    padding='SAME',
                    kernel_regularizer=l2(self.l2),
                    bias_regularizer=l2(self.l2),
                    activation=self.activation_conv,
                    )(x)
        x = BatchNormalization()(x)
        x = ReLU()(x)
        return x 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:34,代码来源:graph.py

示例13: final_oct_conv_bn_relu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def final_oct_conv_bn_relu(ip_high, ip_low, filters, kernel_size=(3, 3), strides=(1, 1),
                           padding='same', dilation=None, bias=False, activation=True):

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = final_octconv(ip_high, ip_low, filters, kernel_size, strides,
                      padding, dilation, bias)

    x = BatchNormalization(axis=channel_axis)(x)
    if activation:
        x = ReLU()(x)

    return x 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:15,代码来源:octave_conv_block.py

示例14: _conv_bn_relu

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def _conv_bn_relu(ip, filters, kernel_size=(3, 3), strides=(1, 1),
                  padding='same', bias=False, activation=True):

    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = _conv_block(ip, filters, kernel_size, strides, padding, bias)
    x = BatchNormalization(axis=channel_axis)(x)
    if activation:
        x = ReLU()(x)

    return x 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:13,代码来源:octave_resnet.py

示例15: _octresnet_bottleneck_block

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ReLU [as 别名]
def _octresnet_bottleneck_block(ip, filters, alpha=0.5, strides=(1, 1),
                                downsample_shortcut=False, first_block=False,
                                expansion=4):

    if first_block:
        x_high_res, x_low_res = initial_oct_conv_bn_relu(ip, filters, kernel_size=(1, 1),
                                                         alpha=alpha)

        x_high, x_low = oct_conv_bn_relu(x_high_res, x_low_res, filters, kernel_size=(3, 3),
                                         strides=strides, alpha=alpha)

    else:
        x_high_res, x_low_res = ip
        x_high, x_low = oct_conv_bn_relu(x_high_res, x_low_res, filters, kernel_size=(1, 1),
                                         alpha=alpha)

        x_high, x_low = oct_conv_bn_relu(x_high, x_low, filters, kernel_size=(3, 3),
                                         strides=strides, alpha=alpha)

    final_out_filters = int(filters * expansion)
    x_high, x_low = oct_conv_bn_relu(x_high, x_low, filters=final_out_filters,
                                     kernel_size=(1, 1), alpha=alpha, activation=False)

    if downsample_shortcut:
        x_high_res, x_low_res = oct_conv_bn_relu(x_high_res, x_low_res,
                                                 final_out_filters, kernel_size=(1, 1),
                                                 strides=strides, alpha=alpha,
                                                 activation=False)

    x_high = add([x_high, x_high_res])
    x_low = add([x_low, x_low_res])

    x_high = ReLU()(x_high)
    x_low = ReLU()(x_low)

    return x_high, x_low 
开发者ID:titu1994,项目名称:keras-octconv,代码行数:38,代码来源:octave_resnet.py


注:本文中的keras.layers.ReLU方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。