基于keras与tensorflow手工实现ResNet50网络
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
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)
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
__________________________________________________________________________________________________
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
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()