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python神經網絡ResNet50模型的復現詳解_python

作者：Bubbliiiing ? 更新時間： 2022-06-30 編程語言

什么是殘差網絡

最近看yolo3里面講到了殘差網絡，對這個網絡結構很感興趣，于是了解到這個網絡結構最初的使用是在ResNet網絡里。

Residual net(殘差網絡)：

將靠前若干層的某一層數據輸出直接跳過多層引入到后面數據層的輸入部分。

意味著后面的特征層的內容會有一部分由其前面的某一層線性貢獻。

其結構如下：

深度殘差網絡的設計是為了克服由于網絡深度加深而產生的學習效率變低與準確率無法有效提升的問題。

什么是ResNet50模型

ResNet50有兩個基本的塊，分別名為Conv Block和Identity Block，其中Conv Block輸入和輸出的維度是不一樣的，所以不能連續串聯，它的作用是改變網絡的維度；

Identity Block輸入維度和輸出維度相同，可以串聯，用于加深網絡的。

Conv Block的結構如下：

Identity Block的結構如下：

這兩個都是殘差網絡結構。

總的網絡結構如下：

這樣看起來可能比較抽象，還有一副很好的我從網上找的圖，可以拉到最后面去看哈，放前面太占位置了。

ResNet50網絡部分實現代碼

#-------------------------------------------------------------#
#   ResNet50的網絡部分
#-------------------------------------------------------------#
from __future__ import print_function
import numpy as np
from keras import layers
from keras.layers import Input
from keras.layers import Dense,Conv2D,MaxPooling2D,ZeroPadding2D,AveragePooling2D
from keras.layers import Activation,BatchNormalization,Flatten
from keras.models import Model
from keras.preprocessing import image
import keras.backend as K
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
def identity_block(input_tensor, kernel_size, filters, stage, block):
    filters1, filters2, filters3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters2, kernel_size,padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(name=bn_name_base + '2c')(x)
    x = layers.add([x, input_tensor])
    x = Activation('relu')(x)
    return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    filters1, filters2, filters3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    x = Conv2D(filters1, (1, 1), strides=strides,
               name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters2, kernel_size, padding='same',
               name=conv_name_base + '2b')(x)
    x = BatchNormalization(name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(name=bn_name_base + '2c')(x)
    shortcut = Conv2D(filters3, (1, 1), strides=strides,
                      name=conv_name_base + '1')(input_tensor)
    shortcut = BatchNormalization(name=bn_name_base + '1')(shortcut)
    x = layers.add([x, shortcut])
    x = Activation('relu')(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')
    model.load_weights("resnet50_weights_tf_dim_ordering_tf_kernels.h5")
    return model

圖片預測

建立網絡后，可以用以下的代碼進行預測。

if __name__ == '__main__':
    model = ResNet50()
    model.summary()
    img_path = 'elephant.jpg'
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    print('Input image shape:', x.shape)
    preds = model.predict(x)
    print('Predicted:', decode_predictions(preds))

預測所需的已經訓練好的ResNet50模型可以在https://github.com/fchollet/deep-learning-models/releases下載。非常方便。

預測結果為：

Predicted: [[('n01871265', 'tusker', 0.41107917), ('n02504458', 'African_elephant', 0.39015812), ('n02504013', 'Indian_elephant', 0.12260196), ('n03000247', 'chain_mail', 0.023176488), ('n02437312', 'Arabian_camel', 0.020982226)]]

ResNet50模型的完整的結構圖

原文鏈接：https://blog.csdn.net/weixin_44791964/article/details/102790260

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