如何实现2015 ResNet
简介
ResNet是一种深度神经网络架构，由微软研究院提出，广泛应用于图像分类、目标检测等计算机视觉任务中。2015 ResNet是ResNet的第一个版本，通过引入残差连接（residual connection）解决了深度神经网络中的梯度消失和梯度爆炸问题，使得网络可以更深更容易训练。
实现步骤
以下是实现2015 ResNet的步骤概览：
| 步骤 | 描述 |
|---|---|
| 1 | 导入必要的库和模块 |
| 2 | 定义ResNet的基本组件 |
| 3 | 构建网络结构 |
| 4 | 编译和训练模型 |
| 5 | 评估模型性能 |
| 6 | 进行预测 |
现在让我们逐步进行实现。
1. 导入必要的库和模块
首先，我们需要导入以下库和模块来实现2015 ResNet：
import tensorflow as tf
from tensorflow.keras import layers
2. 定义ResNet的基本组件
在实现2015 ResNet之前，我们需要定义一些基本的组件，包括卷积块（Convolution Block）、恒等块（Identity Block）和ResNet模块（ResNet Block）。
卷积块
class ConvBlock(tf.keras.Model):
def __init__(self, filters, strides):
super(ConvBlock, self).__init__()
self.conv1 = layers.Conv2D(filters, 3, strides=strides, padding='same')
self.bn1 = layers.BatchNormalization()
self.relu = layers.Activation('relu')
self.conv2 = layers.Conv2D(filters, 3, strides=1, padding='same')
self.bn2 = layers.BatchNormalization()
self.shortcut = tf.keras.Sequential()
if strides > 1:
self.shortcut.add(layers.Conv2D(filters, 1, strides=strides))
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
shortcut = self.shortcut(inputs)
x = layers.add([x, shortcut])
x = self.relu(x)
return x
恒等块
class IdentityBlock(tf.keras.Model):
def __init__(self, filters):
super(IdentityBlock, self).__init__()
self.conv1 = layers.Conv2D(filters, 3, strides=1, padding='same')
self.bn1 = layers.BatchNormalization()
self.relu = layers.Activation('relu')
self.conv2 = layers.Conv2D(filters, 3, strides=1, padding='same')
self.bn2 = layers.BatchNormalization()
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = layers.add([x, inputs])
x = self.relu(x)
return x
ResNet模块
class ResNetBlock(tf.keras.Model):
def __init__(self, filters, strides, block_fn):
super(ResNetBlock, self).__init__()
self.conv_block1 = block_fn(filters, strides)
self.conv_block2 = block_fn(filters, strides=1)
self.conv_block3 = block_fn(filters, strides=1)
def call(self, inputs):
x = self.conv_block1(inputs)
x = self.conv_block2(x)
x = self.conv_block3(x)
return x
3. 构建网络结构
在这一步中，我们将使用上述定义的组件构建ResNet的网络结构。
class ResNet(tf.keras.Model):
def __init__(self, block_fn, repetitions):
super(ResNet, self).__init__()
self.conv = layers.Conv2D(64, 7, strides=2, padding='same')
self.bn = layers.BatchNormalization()
self.relu = layers.Activation('relu')
self.max_pool = layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')
self.res_blocks = tf.keras.Sequential()
filters = 64
for i, r in enumerate(repetitions):
block = ResNetBlock(filters=filters, strides=1, block
