残差收缩网络：一种深度学习故障诊断算法

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 28 23:24:05 2019
Implemented using TensorFlow 1.0.1 and Keras 2.2.1
 
M. Zhao, S. Zhong, X. Fu, et al., Deep Residual Shrinkage Networks for Fault Diagnosis, 
IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2943898
@author: super_9527
"""

from __future__ import print_function
import keras
import numpy as np
from keras.datasets import mnist
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import AveragePooling2D, Input, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras.layers.core import Lambda
K.set_learning_phase(1)

# Input image dimensions
img_rows, img_cols = 28, 28

# The data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

# Noised data
x_train = x_train.astype('float32') / 255. + 0.5*np.random.random([x_train.shape[0], img_rows, img_cols, 1])
x_test = x_test.astype('float32') / 255. + 0.5*np.random.random([x_test.shape[0], img_rows, img_cols, 1])
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)


def abs_backend(inputs):
    return K.abs(inputs)

def expand_dim_backend(inputs):
    return K.expand_dims(K.expand_dims(inputs,1),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,-1)
    inputs = K.spatial_3d_padding(inputs, ((0,0),(0,0),(pad_dim,pad_dim)), 'channels_last')
    return K.squeeze(inputs, -1)

# Residual Shrinakge Block
def residual_shrinkage_block(incoming, nb_blocks, out_channels, downsample=False,
                             downsample_strides=2):
    
    residual = incoming
    in_channels = incoming.get_shape().as_list()[-1]
    
    for i in range(nb_blocks):
        
        identity = residual
        
        if not downsample:
            downsample_strides = 1
        
        residual = BatchNormalization()(residual)
        residual = Activation('relu')(residual)
        residual = Conv2D(out_channels, 3, strides=(downsample_strides, downsample_strides), 
                          padding='same', kernel_initializer='he_normal', 
                          kernel_regularizer=l2(1e-4))(residual)
        
        residual = BatchNormalization()(residual)
        residual = Activation('relu')(residual)
        residual = Conv2D(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 = GlobalAveragePooling2D()(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 = AveragePooling2D(pool_size=(1,1), strides=(2,2))(identity)
            
        # Zero_padding to match channels
        if in_channels != out_channels:
            identity = Lambda(pad_backend, arguments={'in_channels':in_channels,'out_channels':out_channels})(identity)
        
        residual = keras.layers.add([residual, identity])
    
    return residual


# define and train a model
inputs = Input(shape=input_shape)
net = Conv2D(8, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(inputs)
net = residual_shrinkage_block(net, 1, 8, downsample=True)
net = BatchNormalization()(net)
net = Activation('relu')(net)
net = GlobalAveragePooling2D()(net)
outputs = Dense(10, activation='softmax', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(net)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=100, epochs=5, verbose=1, validation_data=(x_test, y_test))

# get results
K.set_learning_phase(0)
DRSN_train_score = model.evaluate(x_train, y_train, batch_size=100, verbose=0)
print('Train loss:', DRSN_train_score[0])
print('Train accuracy:', DRSN_train_score[1])
DRSN_test_score = model.evaluate(x_test, y_test, batch_size=100, verbose=0)
print('Test loss:', DRSN_test_score[0])
print('Test accuracy:', DRSN_test_score[1])

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 23 21:23:09 2019
Implemented using TensorFlow 1.0 and TFLearn 0.3.2
 
M. Zhao, S. Zhong, X. Fu, B. Tang, M. Pecht, Deep Residual Shrinkage Networks for Fault Diagnosis, 
IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2943898
 
@author: super_9527
"""
  
from __future__ import division, print_function, absolute_import
  
import tflearn
import numpy as np
import tensorflow as tf
from tflearn.layers.conv import conv_2d
  
# Data loading
from tflearn.datasets import cifar10
(X, Y), (testX, testY) = cifar10.load_data()
  
# Add noise
X = X + np.random.random((50000, 32, 32, 3))*0.1
testX = testX + np.random.random((10000, 32, 32, 3))*0.1
  
# Transform labels to one-hot format
Y = tflearn.data_utils.to_categorical(Y,10)
testY = tflearn.data_utils.to_categorical(testY,10)
  
def residual_shrinkage_block(incoming, nb_blocks, out_channels, downsample=False,
                   downsample_strides=2, activation='relu', batch_norm=True,
                   bias=True, weights_init='variance_scaling',
                   bias_init='zeros', regularizer='L2', weight_decay=0.0001,
                   trainable=True, restore=True, reuse=False, scope=None,
                   name="ResidualBlock"):
      
    # residual shrinkage blocks with channel-wise thresholds
  
    residual = incoming
    in_channels = incoming.get_shape().as_list()[-1]
  
    # Variable Scope fix for older TF
    try:
        vscope = tf.variable_scope(scope, default_name=name, values=[incoming],
                                   reuse=reuse)
    except Exception:
        vscope = tf.variable_op_scope([incoming], scope, name, reuse=reuse)
  
    with vscope as scope:
        name = scope.name #TODO
  
        for i in range(nb_blocks):
  
            identity = residual
  
            if not downsample:
                downsample_strides = 1
  
            if batch_norm:
                residual = tflearn.batch_normalization(residual)
            residual = tflearn.activation(residual, activation)
            residual = conv_2d(residual, out_channels, 3,
                             downsample_strides, 'same', 'linear',
                             bias, weights_init, bias_init,
                             regularizer, weight_decay, trainable,
                             restore)
  
            if batch_norm:
                residual = tflearn.batch_normalization(residual)
            residual = tflearn.activation(residual, activation)
            residual = conv_2d(residual, out_channels, 3, 1, 'same',
                             'linear', bias, weights_init,
                             bias_init, regularizer, weight_decay,
                             trainable, restore)
              
            # get thresholds and apply thresholding
            abs_mean = tf.reduce_mean(tf.reduce_mean(tf.abs(residual),axis=2,keep_dims=True),axis=1,keep_dims=True)
            scales = tflearn.fully_connected(abs_mean, out_channels//4, activation='linear',regularizer='L2',weight_decay=0.0001,weights_init='variance_scaling')
            scales = tflearn.batch_normalization(scales)
            scales = tflearn.activation(scales, 'relu')
            scales = tflearn.fully_connected(scales, out_channels, activation='linear',regularizer='L2',weight_decay=0.0001,weights_init='variance_scaling')
            scales = tf.expand_dims(tf.expand_dims(scales,axis=1),axis=1)
            thres = tf.multiply(abs_mean,tflearn.activations.sigmoid(scales))
            # soft thresholding
            residual = tf.multiply(tf.sign(residual), tf.maximum(tf.abs(residual)-thres,0))
              
  
            # Downsampling
            if downsample_strides > 1:
                identity = tflearn.avg_pool_2d(identity, 1,
                                               downsample_strides)
  
            # Projection to new dimension
            if in_channels != out_channels:
                if (out_channels - in_channels) % 2 == 0:
                    ch = (out_channels - in_channels)//2
                    identity = tf.pad(identity,
                                      [[0, 0], [0, 0], [0, 0], [ch, ch]])
                else:
                    ch = (out_channels - in_channels)//2
                    identity = tf.pad(identity,
                                      [[0, 0], [0, 0], [0, 0], [ch, ch+1]])
                in_channels = out_channels
  
            residual = residual + identity
  
    return residual
  
  
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
  
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
  
# Build a Deep Residual Shrinkage Network with 3 blocks
net = tflearn.input_data(shape=[None, 32, 32, 3],
                         data_preprocessing=img_prep,
                         data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = residual_shrinkage_block(net, 1, 16)
net = residual_shrinkage_block(net, 1, 32, downsample=True)
net = residual_shrinkage_block(net, 1, 32, downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=20000, staircase=True)
net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, checkpoint_path='model_cifar10',
                    max_checkpoints=10, tensorboard_verbose=0,
                    clip_gradients=0.)
  
model.fit(X, Y, n_epoch=100, snapshot_epoch=False, snapshot_step=500,
          show_metric=True, batch_size=100, shuffle=True, run_id='model_cifar10')
  
training_acc = model.evaluate(X, Y)[0]
validation_acc = model.evaluate(testX, testY)[0]