Python layers.ReLU方法代码示例

# 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 

# 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 

# 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 

# 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 

# 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 

# 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 

# 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) 

# 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 

# 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]) 

# 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 

# 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) 

# 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 

# 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 

# 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 

# 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