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keras 一维残差神经网络（1D
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1.介绍
本文整合了部分深度残差收缩网络以及残差神经网络现有的2D及1D版本资源，并给出TensorFlow&Keras环境下的1D ResNet和DRSN程序和使用示例。 2.资源整合深度残差收缩网络： -介绍：http://t.csdn.cn/DvnBL -（pytorch，2D）https://cloud.tencent.com/developer/article/1813376 -（pytorch，2D）https://zhuanlan.zhihu.com/p/337346575 -（pytorch，2D）https://blog.csdn.net/weixin_47174159/article/details/115409058 -（Tensorflow2.0&Keras，2D）https://blog.csdn.net/qq_36758914/article/details/109452735 -（pytorch，1D）https://github.com/liguge/Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN- -（官方，TFLearn-1D、TensorFlow&Keras-2D、TFLearn-2D）https://github.com/zhao62/Deep-Residual-Shrinkage-Networks 残差神经网络 -（pytorch，1D）https://github.com/StChenHaoGitHub/1D-deeplearning-model-LeNet-AlexNet-ZFNet-VGG-GoogLeNet-ResNet-DenseNet -（TensorFlow，1D）https://github.com/ralasun/tensorflow-resnet-1d/blob/master/Resnet.ipynb -（Keras，2D）https://keras.io/api/applications/resnet/#resnet50v2-function 3.ResNet-1D修改内容：参考DRSN风格，以keras.applications.ResNet50V21为基础，将2D操作更改为1D操作。 代码片段：复制以下内容或前往github下载https://github.com/M73ACat/ResNet-1D-DRSN-1D 示例结构图，参考2 
图 文献2中的模型结构图
from keras.layers import (Activation, Add, BatchNormalization, Conv1D, Dense,
GlobalAveragePooling1D, Input)
from keras.models import Model
from keras.optimizers import Nadam
def res_block(x, filters, block_nums, kernel_size=3, stride=1):
"""A residual block.
Arguments:
x: input tensor.
filters: integer, filters of the bottleneck layer.
block_nums: integer, numbers of block.
kernel_size: default 3, kernel size of the bottleneck layer.
stride: default 1, stride of the first layer.
Returns:
Output tensor for the residual block.
"""
for _ in range(block_nums):
preact = BatchNormalization(
epsilon=1.001e-5)(x)
preact = Activation('relu')(preact)
shortcut = Conv1D(
4 * filters, 1, strides=stride,padding='same')(preact)
x = Conv1D(
filters, 1, strides=1, use_bias=False)(preact)
x = BatchNormalization(
epsilon=1.001e-5)(x)
x = Activation('relu')(x)
x = Conv1D(
filters,
kernel_size,
strides=stride,
use_bias=False,
padding='same')(x)
x = BatchNormalization(
epsilon=1.001e-5)(x)
x = Activation('relu')(x)
x = Conv1D(4 * filters, 1)(x)
x = Add()([shortcut, x])
return x
使用示例（建立图1所示ResNet模型）： inputs = 2048 outputs = 8 x_input = Input(shape=(inputs,1)) x = Conv1D(4,3,2,padding='same')(x_input) x = res_block(x,filters=4,block_nums=1,stride=2) x = res_block(x,filters=4,block_nums=3,stride=1) x = res_block(x,filters=8,block_nums=1,stride=2) x = res_block(x,filters=8,block_nums=3,stride=1) x = res_block(x,filters=16,block_nums=1,stride=2) x = res_block(x,filters=16,block_nums=3,stride=1) x = BatchNormalization()(x) x = Activation('relu')(x) x = GlobalAveragePooling1D()(x) x = Dense(outputs,activation='softmax')(x) model = Model(inputs=x_input,outputs=x) optimizers = Nadam(lr=1e-5) model.compile(optimizer = optimizers, loss= 'categorical_crossentropy',metrics=['accuracy']) model.summary() 4.DRSN-1D修改内容：以DRSN Keras2D3为基础，将2D操作更改为1D操作。 代码片段：复制以下内容或前往github下载https://github.com/M73ACat/ResNet-1D-DRSN-1D import keras from keras import backend as K from keras.layers import (Activation, AveragePooling1D, BatchNormalization, Conv1D, Dense, GlobalAveragePooling1D, Input) from keras.layers.core import Lambda from keras.models import Model from keras.optimizers import Nadam from keras.regularizers import l2 def abs_backend(inputs): return K.abs(inputs) def expand_dim_backend(inputs): return K.expand_dims(inputs,1) def sign_backend(inputs): return K.sign(inputs) def pad_backend(inputs, in_channels, out_channels): pad_dim = (out_channels - in_channels)//2 inputs = K.expand_dims(inputs) inputs = K.spatial_2d_padding(inputs,padding=((0,0),(pad_dim,pad_dim))) return K.squeeze(inputs,-1) def residual_shrinkage_block(incoming, nb_blocks, out_channels, downsample=False, downsample_strides=2): """A residual_shrinkage_block. Arguments: incoming: input tensor. nb_blocks: integer, numbers of block. out_channels: integer, filters of the conv1d layer. downsample: default False, downsample or not. downsample_strides: default 2, stride of the first layer. Returns: Output tensor for the residual block. """ residual = incoming in_channels = incoming.get_shape().as_list()[-1] for _ in range(nb_blocks): identity = residual if not downsample: downsample_strides = 1 residual = BatchNormalization()(residual) residual = Activation('relu')(residual) residual = Conv1D(out_channels, 3, strides=downsample_strides, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(residual) residual = BatchNormalization()(residual) residual = Activation('relu')(residual) residual = Conv1D(out_channels, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(residual) # Calculate global means residual_abs = Lambda(abs_backend)(residual) abs_mean = GlobalAveragePooling1D()(residual_abs) # Calculate scaling coefficients scales = Dense(out_channels, activation=None, kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(abs_mean) scales = BatchNormalization()(scales) scales = Activation('relu')(scales) scales = Dense(out_channels, activation='sigmoid', kernel_regularizer=l2(1e-4))(scales) scales = Lambda(expand_dim_backend)(scales) # Calculate thresholds thres = keras.layers.multiply([abs_mean, scales]) # Soft thresholding sub = keras.layers.subtract([residual_abs, thres]) zeros = keras.layers.subtract([sub, sub]) n_sub = keras.layers.maximum([sub, zeros]) residual = keras.layers.multiply([Lambda(sign_backend)(residual), n_sub]) # Downsampling using the pooL-size of (1, 1) if downsample_strides > 1: identity = AveragePooling1D(pool_size=1, strides=2)(identity) # Zero_padding or Conv1D to match channels if in_channels != out_channels: """ padding """ identity = Lambda(pad_backend, arguments={'in_channels':in_channels,'out_channels':out_channels})(identity) """ Conv1D """ # identity = Conv1D(out_channels,1,strides=1,padding='same')(identity) residual = keras.layers.add([residual, identity]) return residual使用示例（建立图1所示DRSN模型）： inputs = 2048 outputs = 8 x_input = Input(shape=(inputs,1)) x = Conv1D(4,3,2,padding='same')(x_input) x = residual_shrinkage_block(x, 1, 4, downsample=True) x = residual_shrinkage_block(x, 3, 4, downsample=False) x = residual_shrinkage_block(x, 1, 8, downsample=True) x = residual_shrinkage_block(x, 3, 8, downsample=False) x = residual_shrinkage_block(x, 1, 16, downsample=True) x = residual_shrinkage_block(x, 3, 16, downsample=False) x = BatchNormalization()(x) x = Activation('relu')(x) x = GlobalAveragePooling1D()(x) x = Dense(outputs,activation='softmax')(x) model = Model(inputs=x_input,outputs=x) optimizers = Nadam(lr=1e-5) model.compile(optimizer = optimizers, loss= 'categorical_crossentropy',metrics=['accuracy']) model.summary() 参考文献https://keras.io/api/applications/resnet/#resnet50v2-function ↩︎ ZHAO M., ZHONG S., FU X., et al. Deep Residual Shrinkage Networks for Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7):4681-4690. ↩︎ https://github.com/zhao62/Deep-Residual-Shrinkage-Networks/blob/master/DRSN_Keras.py ↩︎ |
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