本文整理匯總了Python中keras.layers.ReLU方法的典型用法代碼示例。如果您正苦於以下問題:Python layers.ReLU方法的具體用法?Python layers.ReLU怎麽用?Python layers.ReLU使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.layers
的用法示例。
在下文中一共展示了layers.ReLU方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: emit_Relu6
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例2: shortcut_pool
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
def shortcut_pool(inputs, output, filters=256, pool_type='max', shortcut=True):
"""
ResNet(shortcut連接|skip連接|residual連接),
這裏是用shortcut連接. 恒等映射, block+f(block)
再加上 downsampling實現
參考: 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
示例3: initial_oct_conv_bn_relu
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例4: oct_conv_bn_relu
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例5: _bottleneck_original
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例6: build_generator
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例7: call
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
def call(self, x):
return nn.ReLU(max_value=6.0)(x)
示例8: get_activation_layer
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例9: ResidualBlock
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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])
示例10: __init__
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例11: create_model
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
def create_model(self, hyper_parameters):
"""
構建神經網絡
: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 層
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)
# 這裏是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)
# 全連接層
# 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)
示例12: convolutional_block
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例13: final_oct_conv_bn_relu
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例14: _conv_bn_relu
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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
示例15: _octresnet_bottleneck_block
# 需要導入模塊: from keras import layers [as 別名]
# 或者: from keras.layers import ReLU [as 別名]
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