卷积神经网络迁移学习代码.py

【核心代码】

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└── 好例子网_kerastrans1.py

0 directories, 1 file

# -*- coding: utf-8 -*- 

import os from keras.utils 

import plot_model from keras.applications.resnet50 

import ResNet50 from keras.applications.vgg19 

import VGG19 from keras.applications.inception_v3 

import InceptionV3 from efficientnet.keras 

import EfficientNetB0 # from keras.applications.nasnetmobile 

import NASNetMobile   # #每个网络要求的输入形状大小： #     EfficientNetB0 - (224, 224, 3) #     EfficientNetB1 - (240, 240, 3) #     EfficientNetB2 - (260, 260, 3 #     EfficientNetB3 - (300, 300, 3) #     EfficientNetB4 - (380, 380, 3) #     EfficientNetB5 - (456, 456, 3) #     EfficientNetB6 - (528, 528, 3) #   EfficientNetB7 - (600, 600, 3) from keras.layers import Dense, Flatten, GlobalAveragePooling2D,BatchNormalization,regularizers,Dropout,Input,GlobalAveragePooling1D,Reshape,Activation,multiply from keras.models import Model, load_model from keras.optimizers import SGD from keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt import tensorflow as tf from keras import backend as K class PowerTransferMode: # 数据准备  def DataGen(self, dir_path, img_row, img_col, batch_size, is_train): #image_size, image_size, batch_size, True)  if is_train:
            datagen = ImageDataGenerator(rescale=1. / 255, #随机偏移、转动等变换图像处理  zoom_range=0.25, rotation_range=15.,  channel_shift_range=25., width_shift_range=0.02, height_shift_range=0.02,  horizontal_flip=True, fill_mode='constant') else:
            datagen = ImageDataGenerator(rescale=1. / 255,rotation_range=15.)
        generator = datagen.flow_from_directory( #返回一个生成 (x, y) 元组的 DirectoryIterator，其中 x 是一个包含一批尺寸为 (batch_size, *target_size,  # channels)的图像的 Numpy 数组，y 是对应标签的 Numpy 数组  dir_path, target_size=(img_row, img_col),  batch_size=batch_size, #  batch_size = 32  # class_mode='binary',  shuffle=True) return generator # ResNet模型  def ResNet50_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=3, img_rows=224, img_cols=224, RGB=True,  is_plot_model=False):
        color = 3 if RGB else 1  base_model = ResNet50(weights='imagenet', include_top=False,layers=tf.keras.layers, pooling=None,  input_shape=(img_rows, img_cols, color),  classes=nb_classes) # 冻结base_model所有层，这样就可以正确获得bottleneck特征  # for layer in base_model.layers:  #     layer.trainable = False   # for i, layer in enumerate(base_model.layers):  #      print(i, layer.name)   for layer in base_model.layers[:121]: #解冻第N层  layer.trainable = False  for layer in base_model.layers[121:]:
            layer.trainable = True  K.set_learning_phase(0)
        Inp = Input((224, 224, 3))
        K.set_learning_phase(1)
        x = base_model(Inp) # 添加自己的全链接分类层  #x = Flatten()(x)  #只用这个 loss 8.6 acc 0.5 改后无改善  x = GlobalAveragePooling2D()(x) # x = BatchNormalization()(x)  x = Dropout(0.02)(x)
        x = Dense(1024,kernel_regularizer=regularizers.l2(0.01), activation='relu')(x) # x = BatchNormalization()(x)  predictions = Dense(nb_classes, activation='softmax')(x) # 训练模型  model = Model(inputs=Inp, outputs=predictions)
        sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
        model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # 绘制模型  if is_plot_model:
            plot_model(model, to_file='resnet50_model.png', show_shapes=True) return model