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基于keras与tensorflow手工实现ResNet50网络_keras resnet50
作者：菜鸟追梦旅行 | 2024-02-16 11:38:44
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keras resnet50
前言
在文章 基于tensorflow的ResNet50V2网络识别动物，我们使用了keras已经提供的神经网络，完成了图像分类的。这个时候，小明同学就问了，那么我怎么自己去写一个神经网络来进行训练呢？
本文就基于tensorflow，自己定一个神经网络。
ResNet50网络
从结构上看，与我们之前的的区别在于，输入的格式变成(3,224,224)
ResNet50有两个基本的块，分别名为Conv Block和Identity Block
整体架构
Conv Block架构
Identity Block架构
模型训练
手工实现模型
模型代码(resnet50.py)
# 根据模型进行引入 from keras import layers from keras.layers import Input,Activation,BatchNormalization,Flatten from keras.layers import Dense,Conv2D,MaxPooling2D,ZeroPadding2D,AveragePooling2D from keras.models import Model def identity_block(input_tensor, kernel_size, filters, stage, block): filters1, filters2, filters3 = filters name_base = str(stage) + block + '_identity_block_' x = Conv2D(filters1, (1, 1), name=name_base + 'conv1')(input_tensor) x = BatchNormalization(name=name_base + 'bn1')(x) x = Activation('relu', name=name_base + 'relu1')(x) x = Conv2D(filters2, kernel_size, padding='same', name=name_base + 'conv2')(x) x = BatchNormalization(name=name_base + 'bn2')(x) x = Activation('relu', name=name_base + 'relu2')(x) x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x) x = BatchNormalization(name=name_base + 'bn3')(x) x = layers.add([x, input_tensor], name=name_base + 'add') x = Activation('relu', name=name_base + 'relu4')(x) return x def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): filters1, filters2, filters3 = filters res_name_base = str(stage) + block + '_conv_block_res_' name_base = str(stage) + block + '_conv_block_' x = Conv2D(filters1, (1, 1), strides=strides, name=name_base + 'conv1')(input_tensor) x = BatchNormalization(name=name_base + 'bn1')(x) x = Activation('relu', name=name_base + 'relu1')(x) x = Conv2D(filters2, kernel_size, padding='same', name=name_base + 'conv2')(x) x = BatchNormalization(name=name_base + 'bn2')(x) x = Activation('relu', name=name_base + 'relu2')(x) x = Conv2D(filters3, (1, 1), name=name_base + 'conv3')(x) x = BatchNormalization(name=name_base + 'bn3')(x) shortcut = Conv2D(filters3, (1, 1), strides=strides, name=res_name_base + 'conv')(input_tensor) shortcut = BatchNormalization(name=res_name_base + 'bn')(shortcut) x = layers.add([x, shortcut], name=name_base+'add') x = Activation('relu', name=name_base+'relu4')(x) return x def ResNet50(input_shape=[224,224,3],classes=1000): img_input = Input(shape=input_shape) x = ZeroPadding2D((3, 3))(img_input) x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x) x = BatchNormalization(name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') x = AveragePooling2D((7, 7), name='avg_pool')(x) x = Flatten()(x) x = Dense(classes, activation='softmax', name='fc1000')(x) model = Model(img_input, x, name='resnet50') return model
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模型训练(resnet50_model_train.py)
将 基于tensorflow的ResNet50V2网络识别动物的模型训练代码进行少量改造
import os import pandas as pd # Model import keras from keras.preprocessing.image import ImageDataGenerator # Callbacks from keras.callbacks import EarlyStopping, ModelCheckpoint # Pre-Trained Model import tensorflow as tf import resnet50 root_path = './animal/Animals_Classification/Animal-Data-V2/Data-V2/Training Data/' valid_path = './animal/Animals_Classification/Animal-Data-V2/Data-V2/Validation Data/' test_path = './animal/Animals_Classification/Animal-Data-V2/Data-V2/Testing Data/' class_names = sorted(os.listdir(root_path)) n_classes = len(class_names) print(f"Total Number of Classes : {n_classes} \nClass Names : {class_names}") class_dis = [len(os.listdir(root_path+name)) for name in class_names] train_gen = ImageDataGenerator(rescale=1/255., rotation_range=10, horizontal_flip=True) valid_gen = ImageDataGenerator(rescale=1/255.) test_gen = ImageDataGenerator(rescale=1/255) # Load Data train_ds = train_gen.flow_from_directory(root_path, class_mode='binary', target_size=(224,224), shuffle=True, batch_size=32) valid_ds = valid_gen.flow_from_directory(valid_path, class_mode='binary', target_size=(224,224), shuffle=True, batch_size=32) test_ds = test_gen.flow_from_directory(test_path, class_mode='binary', target_size=(224,224), shuffle=True, batch_size=32) with tf.device("/GPU:0"): ## Pre-Trained Model model = resnet50.ResNet50() model.summary() model_file = "ResNet50_V1.h5" # 加载预训练模型 if os.path.exists(model_file): model.load_weights(model_file) # Callbacks cbs = [EarlyStopping(patience=5, restore_best_weights=True), ModelCheckpoint(model_file, save_best_only=True)] # Model opt = tf.keras.optimizers.Adam(learning_rate=2e-3) model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy']) # Model Training history = model.fit(train_ds, validation_data=valid_ds, callbacks=cbs, epochs=200) data = pd.DataFrame(history.history) print(data)
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模型执行
GPU基本跑满
Model: "resnet50" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3 0 [] )] zero_padding2d (ZeroPadding2D) (None, 230, 230, 3) 0 ['input_1[0][0]'] conv1 (Conv2D) (None, 112, 112, 64 9472 ['zero_padding2d[0][0]'] ) bn_conv1 (BatchNormalization) (None, 112, 112, 64 256 ['conv1[0][0]'] ) activation (Activation) (None, 112, 112, 64 0 ['bn_conv1[0][0]'] ) max_pooling2d (MaxPooling2D) (None, 55, 55, 64) 0 ['activation[0][0]'] 2a_conv_block_conv1 (Conv2D) (None, 55, 55, 64) 4160 ['max_pooling2d[0][0]'] 2a_conv_block_bn1 (BatchNormal (None, 55, 55, 64) 256 ['2a_conv_block_conv1[0][0]'] ization) 2a_conv_block_relu1 (Activatio (None, 55, 55, 64) 0 ['2a_conv_block_bn1[0][0]'] n) 2a_conv_block_conv2 (Conv2D) (None, 55, 55, 64) 36928 ['2a_conv_block_relu1[0][0]'] 2a_conv_block_bn2 (BatchNormal (None, 55, 55, 64) 256 ['2a_conv_block_conv2[0][0]'] ization) 2a_conv_block_relu2 (Activatio (None, 55, 55, 64) 0 ['2a_conv_block_bn2[0][0]'] n) 2a_conv_block_conv3 (Conv2D) (None, 55, 55, 256) 16640 ['2a_conv_block_relu2[0][0]'] 2a_conv_block_res_conv (Conv2D (None, 55, 55, 256) 16640 ['max_pooling2d[0][0]'] ) 2a_conv_block_bn3 (BatchNormal (None, 55, 55, 256) 1024 ['2a_conv_block_conv3[0][0]'] ization) 2a_conv_block_res_bn (BatchNor (None, 55, 55, 256) 1024 ['2a_conv_block_res_conv[0][0]'] malization) 2a_conv_block_add (Add) (None, 55, 55, 256) 0 ['2a_conv_block_bn3[0][0]', '2a_conv_block_res_bn[0][0]'] 2a_conv_block_relu4 (Activatio (None, 55, 55, 256) 0 ['2a_conv_block_add[0][0]'] n) 2b_identity_block_conv1 (Conv2 (None, 55, 55, 64) 16448 ['2a_conv_block_relu4[0][0]'] D) 2b_identity_block_bn1 (BatchNo (None, 55, 55, 64) 256 ['2b_identity_block_conv1[0][0]'] rmalization) 2b_identity_block_relu1 (Activ (None, 55, 55, 64) 0 ['2b_identity_block_bn1[0][0]'] ation) 2b_identity_block_conv2 (Conv2 (None, 55, 55, 64) 36928 ['2b_identity_block_relu1[0][0]'] D) *******略，大致意思是有50层，因为是ResNet50********* 5c_identity_block_bn3 (BatchNo (None, 7, 7, 2048) 8192 ['5c_identity_block_conv3[0][0]'] rmalization) 5c_identity_block_add (Add) (None, 7, 7, 2048) 0 ['5c_identity_block_bn3[0][0]', '5b_identity_block_relu4[0][0]'] 5c_identity_block_relu4 (Activ (None, 7, 7, 2048) 0 ['5c_identity_block_add[0][0]'] ation) avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 ['5c_identity_block_relu4[0][0]'] flatten (Flatten) (None, 2048) 0 ['avg_pool[0][0]'] fc1000 (Dense) (None, 1000) 2049000 ['flatten[0][0]'] ================================================================================================== Total params: 25,636,712 Trainable params: 25,583,592 Non-trainable params: 53,120 __________________________________________________________________________________________________
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训练结果
Epoch 1/200 625/625 [==============================] - 245s 376ms/step - loss: 0.3225 - accuracy: 0.8965 - val_loss: 0.7825 - val_accuracy: 0.7670 Epoch 2/200 625/625 [==============================] - 226s 361ms/step - loss: 0.2998 - accuracy: 0.9018 - val_loss: 0.9110 - val_accuracy: 0.7060 Epoch 3/200 625/625 [==============================] - 223s 357ms/step - loss: 0.2644 - accuracy: 0.9113 - val_loss: 1.2283 - val_accuracy: 0.6760 Epoch 4/200 625/625 [==============================] - 223s 357ms/step - loss: 0.2465 - accuracy: 0.9188 - val_loss: 0.9871 - val_accuracy: 0.7500 Epoch 5/200 625/625 [==============================] - 225s 360ms/step - loss: 0.2307 - accuracy: 0.9234 - val_loss: 1.1059 - val_accuracy: 0.6720 Epoch 6/200 625/625 [==============================] - 228s 365ms/step - loss: 0.2016 - accuracy: 0.9341 - val_loss: 0.5819 - val_accuracy: 0.8370 Epoch 7/200 625/625 [==============================] - 227s 363ms/step - loss: 0.1859 - accuracy: 0.9380 - val_loss: 0.8662 - val_accuracy: 0.7740 Epoch 8/200 625/625 [==============================] - 223s 356ms/step - loss: 0.1732 - accuracy: 0.9419 - val_loss: 0.6927 - val_accuracy: 0.8130 Epoch 9/200 625/625 [==============================] - 221s 353ms/step - loss: 0.1631 - accuracy: 0.9446 - val_loss: 0.7033 - val_accuracy: 0.8090 Epoch 10/200 625/625 [==============================] - 220s 352ms/step - loss: 0.1416 - accuracy: 0.9528 - val_loss: 0.8072 - val_accuracy: 0.8130 Epoch 11/200 625/625 [==============================] - 221s 352ms/step - loss: 0.1403 - accuracy: 0.9524 - val_loss: 0.9740 - val_accuracy: 0.7570 loss accuracy val_loss val_accuracy 0 0.322520 0.89655 0.782460 0.767 1 0.299801 0.90180 0.910993 0.706 2 0.264406 0.91130 1.228291 0.676 3 0.246451 0.91880 0.987062 0.750 4 0.230691 0.92340 1.105917 0.672 5 0.201550 0.93405 0.581927 0.837 6 0.185873 0.93800 0.866195 0.774 7 0.173195 0.94185 0.692714 0.813 8 0.163083 0.94460 0.703307 0.809 9 0.141572 0.95280 0.807219 0.813 10 0.140329 0.95240 0.974021 0.757
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训练结果
从输出的训练结果来看，效果没有ResNet50V2的，对参数进行了一些调整，没有太多的效果。各位如果有兴趣可以对这样的网络进行修改，从而提升验证的正确性。
模型验证
模型验证代码
from keras.models import load_model import tensorflow as tf from tensorflow.keras.utils import load_img, img_to_array import numpy as np import os import matplotlib.pyplot as plt root_path = './animal/Animals_Classification/Animal-Data-V2/Data-V2/Training Data/' class_names = sorted(os.listdir(root_path)) model = load_model('./ResNet50_V1.h5') model.summary() def load_image(path): '''This function will load the image present at the given location''' image = tf.cast(tf.image.resize(img_to_array(load_img(path))/255., (224,224)), tf.float32) #image = tf.cast(tf.image.resize(img_to_array(load_img(path)) / 255., (224, 224)), tf.float32) return image i_path = './animal/Animals_Classification/Animal-Data-V2/Data-V2/Validation Data/Gorilla/Gorilla (3).jpeg' image = load_image(i_path) preds = model.predict(image[np.newaxis, ...])[0] print(preds) pred_class = class_names[np.argmax(preds)] confidence_score = np.round(preds[np.argmax(preds)], 2) # Configure Title title = f"Pred : {pred_class}\nConfidence : {confidence_score:.2}" print(title) plt.figure(figsize=(25, 8)) plt.title(title) plt.imshow(image) plt.show() while True: path = input("input:") if (path == "q!"): exit() image = load_image(path) preds = model.predict(image[np.newaxis, ...])[0] print(preds) pred_class = class_names[np.argmax(preds)] confidence_score = np.round(preds[np.argmax(preds)], 2) # Configure Title title = f"Pred : {pred_class}\nConfidence : {confidence_score:.2}" print(title) plt.figure(figsize=(25, 8)) plt.title(title) plt.imshow(image) plt.show()
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