ResNet（Residual Network）是由何凯明（Kaiming He）等人提出的深度学习网络架构，是ImageNet竞赛中历史最好的结果之一。ResNet的主要特点是在深度较深的网络中，通过特殊的残差块（residual block）和跨层连接（skip connection）的方式，使得网络训练更加容易，使得网络深度可以进一步增加，从而获得更好的性能表现。
在ResNet中，每个残差块包含两个子层，每个子层都以一个卷积层和一个批量归一化层为主，其中第二个子层还包括了一个激活函数。在残差块中，通过跨层连接将输入数据直接传递到输出数据的过程中，网络直接“学习”残差的方式，从而避免了由于深度增加而产生的梯度消失或梯度爆炸等问题，使得网络的收敛速度更快、训练效果更好。
ResNet还引入了一种称为“bottleneck”的结构，通过在残差块中增加一个额外的瓶颈层，使得网络在保持较少的计算复杂度的情况下，能够更好地适应更深的网络结构。此外，ResNet还采用了全局平均池化（Global Average Pooling）的方式，减少了全连接层的数量，使得模型更加轻量化。
总之，ResNet是一种引入了残差结构的深度学习网络，通过跨层连接和特殊的残差块，使得网络更加易于训练，进而使得网络深度可以进一步增加，提升了网络的性能表现。
需要安装必要的库：tensorflow、matplotlib。
“`python
!pip install tensorflow
!pip install matplotlib
“`
导入必要的库：
“`python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
“`
接下来，加载数据集，将训练集、验证集和测试集分别存放到不同的文件夹中，并使用ImageDataGenerator对图像进行数据增强。
“`python
# 加载数据集
train_data_dir = “path/to/train/folder”
valid_data_dir = “path/to/valid/folder”
test_data_dir = “path/to/test/folder”
# 数据增强
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode=’nearest’)
valid_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(100, 100),
batch_size=32,
class_mode=’categorical’)
valid_generator = valid_datagen.flow_from_directory(
valid_data_dir,
target_size=(100, 100),
batch_size=32,
class_mode=’categorical’)
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(100, 100),
batch_size=32,
class_mode=’categorical’)
“`
接下来，定义ResNet模型。这里使用的是ResNet50，可以通过调整`depth`参数来改变网络深度。
“`python
# 定义ResNet
def identity_block(inputs, filters):
filters1, filters2, filters3 = filters
x = layers.Conv2D(filters1, (1, 1), padding=’valid’)(inputs)
x = layers.BatchNormalization()(x)
x = layers.Activation(‘relu’)(x)
x = layers.Conv2D(filters2, (3, 3), padding=’same’)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation(‘relu’)(x)
x = layers.Conv2D(filters3, (1, 1), padding=’valid’)(x)
x = layers.BatchNormalization()(x)
x = layers.Add()([x, inputs])
x = layers.Activation(‘relu’)(x)
return x
def conv_block(inputs, filters, strides):
filters1, filters2, filters3 = filters
x = layers.Conv2D(filters1, (1, 1), strides=strides, padding=’valid’)(inputs)
x = layers.BatchNormalization()(x)
x = layers.Activation(‘relu’)(x)
x = layers.Conv2D(filters2, (3, 3), padding=’same’)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation(‘relu’)(x)
x = layers.Conv2D(filters3, (1, 1), padding=’valid’)(x)
x = layers.BatchNormalization()(x)
shortcut = layers.Conv2D(filters3, (1, 1), strides=strides, padding=’valid’)(inputs)
shortcut = layers.BatchNormalization()(shortcut)
x = layers.Add()([x, shortcut])
x = layers.Activation(‘relu’)(x)
return x
def ResNet50(input_shape, num_classes):
inputs = layers.Input(shape=input_shape)
x = layers.ZeroPadding2D((3, 3))(inputs)
x = layers.Conv2D(64, (7, 7), strides=(2, 2))(x)
x = layers.BatchNormalization()(x)
x = layers.Activation(‘relu’)(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
# stage 2
x = conv_block(x, filters=[64, 64, 256], strides=(1, 1))
x = identity_block(x, filters=[64, 64, 256])
x = identity_block(x, filters=[64, 64, 256])
# stage 3
x = conv_block(x, filters=[128, 128, 512], strides=(2, 2))
x = identity_block(x, filters=[128, 128, 512])
x = identity_block(x, filters=[128, 128, 512])
x = identity_block(x, filters=[128, 128, 512])
# stage 4
x = conv_block(x, filters=[256, 256, 1024], strides=(2, 2))
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
x = identity_block(x, filters=[256, 256, 1024])
# stage 5
x = conv_block(x, filters=[512, 512, 2048], strides=(2, 2))
x = identity_block(x, filters=[512, 512, 2048])
x = identity_block(x, filters=[512, 512, 2048])
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(num_classes, activation=’softmax’)(x)
model = keras.Model(inputs, outputs)
return model
# 实例化模型
model = ResNet50(input_shape=(100, 100, 3), num_classes=60)
“`
接下来，编译模型并训练。
“`python
# 编译模型
model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-4),
loss=keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=[‘accuracy’])
# 训练模型
history = model.fit(train_generator,
epochs=50,
validation_data=valid_generator)
“`
最后，评估模型并绘制准确率和损失曲线。
“`python
# 评估模型
test_loss, test_acc = model.evaluate(test_generator)
print(‘Test accuracy:’, test_acc)
# 绘制准确率和损失曲线
acc = history.history[‘accuracy’]
val_acc = history.history[‘val_accuracy’]
loss = history.history[‘loss’]
val_loss = history.history[‘val_loss’]
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(acc, label=’Training Accuracy’)
plt.plot(val_acc, label=’Validation Accuracy’)
plt.legend(loc=’lower right’)
plt.title(‘Training and Validation Accuracy’)
plt.subplot(1, 2, 2)
plt.plot(loss, label=’Training Loss’)
plt.plot(val_loss, label=’Validation Loss’)
plt.legend(loc=’upper right’)
plt.title(‘Training and Validation Loss’)
plt.show()
“`
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