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LeNet卷积神经网络-笔记

手写分析LeNet网三卷积运算和两池化加两全连接层计算分析

修正上图中H,W的计算公式为下面格式

基于paddle飞桨框架构建测试代码

#输出结果为：
#[validation] accuracy/loss: 0.9530/0.1516
#这里准确率为95.3%
#通过运行结果可以看出，LeNet在手写数字识别MNIST验证数据集上的准确率高达92%以上。

详细源代码如下所示：

# 导入需要的包
import paddle
import numpy as np
from paddle.nn import Conv2D, MaxPool2D, Linear## 组网
import paddle.nn.functional as F# 定义 LeNet 网络结构
#==============================================================================
class LeNet(paddle.nn.Layer):def __init__(self, num_classes=1):super(LeNet, self).__init__()# 创建卷积和池化层# 创建第1个卷积层self.conv1 = Conv2D(in_channels=1, out_channels=6, kernel_size=5)self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)# 尺寸的逻辑：池化层未改变通道数；当前通道数为6# 创建第2个卷积层self.conv2 = Conv2D(in_channels=6, out_channels=16, kernel_size=5)self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)# 创建第3个卷积层self.conv3 = Conv2D(in_channels=16, out_channels=120, kernel_size=4)# 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -> [B,C*H*W]# 输入size是[28,28]，经过三次卷积和两次池化之后，C*H*W等于120self.fc1 = Linear(in_features=120, out_features=64)# 创建全连接层，第一个全连接层的输出神经元个数为64， 第二个全连接层输出神经元个数为分类标签的类别数self.fc2 = Linear(in_features=64, out_features=num_classes)# 网络的前向计算过程def forward(self, x):x = self.conv1(x)# 每个卷积层使用Sigmoid激活函数，后面跟着一个2x2的池化x = F.sigmoid(x)x = self.max_pool1(x)x = F.sigmoid(x)x = self.conv2(x)x = self.max_pool2(x)x = self.conv3(x)# 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -> [B,C*H*W]x = paddle.reshape(x, [x.shape[0], -1])x = self.fc1(x)x = F.sigmoid(x)x = self.fc2(x)return x
#==========================================================================================
# 输入数据形状是 [N, 1, H, W]
# 这里用np.random创建一个随机数组作为输入数据
x = np.random.randn(*[3,1,28,28])
x = x.astype('float32')# 创建LeNet类的实例，指定模型名称和分类的类别数目
model = LeNet(num_classes=10)
# 通过调用LeNet从基类继承的sublayers()函数，
# 查看LeNet中所包含的子层
print(model.sublayers())
print(x.shape)
x = paddle.to_tensor(x)
print(x.shape)
for item in model.sublayers():# item是LeNet类中的一个子层# 查看经过子层之后的输出数据形状try:x = item(x)except:x = paddle.reshape(x, [x.shape[0], -1])x = item(x)if len(item.parameters())==2:# 查看卷积和全连接层的数据和参数的形状，# 其中item.parameters()[0]是权重参数w，item.parameters()[1]是偏置参数bprint(item.full_name(), x.shape, item.parameters()[0].shape, item.parameters()[1].shape)else:# 池化层没有参数print(item.full_name(), x.shape)  
#
'''
#显示子图层列表model.sublayers()
[Conv2D(1, 6, kernel_size=[5, 5], data_format=NCHW), MaxPool2D(kernel_size=2, stride=2, padding=0), Conv2D(6, 16, kernel_size=[5, 5], data_format=NCHW), MaxPool2D(kernel_size=2, stride=2, padding=0), Conv2D(16, 120, kernel_size=[4, 4], data_format=NCHW), Linear(in_features=120, out_features=64, dtype=float32), Linear(in_features=64, out_features=10, dtype=float32)
]
'''    # -*- coding: utf-8 -*-
# LeNet 识别手写数字
import os
import random
import paddle
import numpy as np
import paddle
from paddle.vision.transforms import ToTensor
from paddle.vision.datasets import MNIST# 定义训练过程
def train(model, opt, train_loader, valid_loader):# 开启0号GPU训练use_gpu = Truepaddle.device.set_device('gpu:0') if use_gpu else paddle.device.set_device('cpu')print('start training ... ')model.train()for epoch in range(EPOCH_NUM):for batch_id, data in enumerate(train_loader()):img = data[0]label = data[1] # 计算模型输出logits = model(img)# 计算损失函数loss_func = paddle.nn.CrossEntropyLoss(reduction='none')loss = loss_func(logits, label)avg_loss = paddle.mean(loss)if batch_id % 2000 == 0:print("epoch: {}, batch_id: {}, loss is: {:.4f}".format(epoch, batch_id, float(avg_loss.numpy())))avg_loss.backward()opt.step()opt.clear_grad()model.eval()accuracies = []losses = []for batch_id, data in enumerate(valid_loader()):img = data[0]label = data[1] # 计算模型输出logits = model(img)pred = F.softmax(logits)# 计算损失函数loss_func = paddle.nn.CrossEntropyLoss(reduction='none')loss = loss_func(logits, label)acc = paddle.metric.accuracy(pred, label)accuracies.append(acc.numpy())losses.append(loss.numpy())print("[validation] accuracy/loss: {:.4f}/{:.4f}".format(np.mean(accuracies), np.mean(losses)))model.train()# 保存模型参数paddle.save(model.state_dict(), 'mnist_LeNet.pdparams')# 创建模型
model = LeNet(num_classes=10)
# 设置迭代轮数
EPOCH_NUM = 5
# 设置优化器为Momentum，学习率为0.001
opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9, parameters=model.parameters())
# 定义数据读取器
train_loader = paddle.io.DataLoader(MNIST(mode='train', transform=ToTensor()), batch_size=10, shuffle=True)
valid_loader = paddle.io.DataLoader(MNIST(mode='test', transform=ToTensor()), batch_size=10)
# 启动训练过程
train(model, opt, train_loader, valid_loader)#输出结果为：
#[validation] accuracy/loss: 0.9530/0.1516
#这里准确率为95.3%
#通过运行结果可以看出，LeNet在手写数字识别MNIST验证数据集上的准确率高达92%以上。