NNDL 实验六 卷积神经网络(3)LeNet实现MNIST

文章目录

  • 5.3 基于LeNet实现手写体数字识别实验
    • 5.3.1 数据
      • 5.3.1.1 数据预处理
    • 5.3.2 模型构建
    • 5.3.3 模型训练
    • 5.3.4 模型评价
    • 5.3.5 模型预测
    • 选做题
      • 使用前馈神经网络实现MNIST识别,与LeNet效果对比。(选做)
      • 可视化LeNet中的部分特征图和卷积核,谈谈自己的看法。(选做)
  • 总结
    • 心得体会
    • 参考链接

5.3 基于LeNet实现手写体数字识别实验

在本节中,我们实现经典卷积网络LeNet-5,并进行手写体数字识别任务。

5.3.1 数据

手写体数字识别是计算机视觉中最常用的图像分类任务,让计算机识别出给定图片中的手写体数字(0-9共10个数字)。由于手写体风格差异很大,因此手写体数字识别是具有一定难度的任务。

我们采用常用的手写数字识别数据集:MNIST数据集。MNIST数据集是计算机视觉领域的经典入门数据集,包含了60,000个训练样本和10,000个测试样本。这些数字已经过尺寸标准化并位于图像中心,图像是固定大小(28×28像素)。下图给出了部分样本的示例。
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第1张图片
为了节省训练时间,本节选取MNIST数据集的一个子集进行后续实验,数据集的划分为:

  • 训练集:1,000条样本
  • 验证集:200条样本
  • 测试集:200条样本

MNIST数据集分为train_set、dev_set和test_set三个数据集,每个数据集含两个列表分别存放了图片数据以及标签数据。比如train_set包含:

图片数据:[1 000, 784]的二维列表,包含1 000张图片。每张图片用一个长度为784的向量表示,内容是 28×28 尺寸的像素灰度值(黑白图片)。
标签数据:[1 000, 1]的列表,表示这些图片对应的分类标签,即0~9之间的数字。
观察数据集分布情况,代码实现如下:

import json
import gzip

# 打印并观察数据集分布情况
train_set, dev_set, test_set = json.load(gzip.open('./mnist.json.gz'))
train_images, train_labels = train_set[0][:1000], train_set[1][:1000]
dev_images, dev_labels = dev_set[0][:200], dev_set[1][:200]
test_images, test_labels = test_set[0][:200], test_set[1][:200]
train_set, dev_set, test_set = [train_images, train_labels], [dev_images, dev_labels], [test_images, test_labels]
print('Length of train/dev/test set:{}/{}/{}'.format(len(train_set[0]), len(dev_set[0]), len(test_set[0])))

运行结果:

Length of train/dev/test set:1000/200/200

可视化观察其中的一张样本以及对应的标签,代码如下所示:

import matplotlib.pyplot as plt
import numpy as np
import PIL
from PIL import Image
image, label = train_set[0][0], train_set[1][0]
image, label = np.array(image).astype('float32'), int(label)
# 原始图像数据为长度784的行向量,需要调整为[28,28]大小的图像
image = np.reshape(image, [28,28])
image = Image.fromarray(image.astype('uint8'), mode='L')
print("The number in the picture is {}".format(label))
plt.figure(figsize=(5, 5))
plt.imshow(image)
plt.savefig('conv-number5.pdf')

运行结果:

The number in the picture is 5

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第2张图片

5.3.1.1 数据预处理

图像分类网络对输入图片的格式、大小有一定的要求,数据输入模型前,需要对数据进行预处理操作,使图片满足网络训练以及预测的需要。本实验主要应用了如下方法:

  • 调整图片大小:LeNet网络对输入图片大小的要求为 32×32 ,而MNIST数据集中的原始图片大小却是 28×28 ,这里为了符合网络的结构设计,将其调整为32×32;
  • 规范化: 通过规范化手段,把输入图像的分布改变成均值为0,标准差为1的标准正态分布,使得最优解的寻优过程明显会变得平缓,训练过程更容易收敛。

代码实现如下:

# 5.3.1.1
from torchvision.transforms import Compose, Resize, Normalize

# 数据预处理
transforms = Compose([Resize(32), Normalize(mean=[127.5], std=[127.5])])

将原始的数据集封装为Dataset类,以便DataLoader调用。

import random
from torch.utils.data import Dataset,DataLoader
import torchvision.transforms as transforms
 
class MNIST_dataset(Dataset):
    def __init__(self, dataset, transforms, mode='train'):
        self.mode = mode
        self.transforms =transforms
        self.dataset = dataset
 
    def __getitem__(self, idx):
        # 获取图像和标签
        image, label = self.dataset[0][idx], self.dataset[1][idx]
        image, label = np.array(image).astype('float32'), int(label)
        image = np.reshape(image, [28,28])
        image = Image.fromarray(image.astype('uint8'), mode='L')
        image = self.transforms(image)
 
        return image, label
 
    def __len__(self):
        return len(self.dataset[0])
 
random.seed(0)
# 加载 mnist 数据集
train_dataset = MNIST_dataset(dataset=train_set, transforms=transforms, mode='train')
test_dataset = MNIST_dataset(dataset=test_set, transforms=transforms, mode='test')
dev_dataset = MNIST_dataset(dataset=dev_set, transforms=transforms, mode='dev')

5.3.2 模型构建

LeNet-5虽然提出的时间比较早,但它是一个非常成功的神经网络模型。

基于LeNet-5的手写数字识别系统在20世纪90年代被美国很多银行使用,用来识别支票上面的手写数字。
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第3张图片
我们使用上面定义的卷积层算子和汇聚层算子构建一个LeNet-5模型。

这里的LeNet-5和原始版本有4点不同:

1.C3层没有使用连接表来减少卷积数量。
2.汇聚层使用了简单的平均汇聚,没有引入权重和偏置参数以及非线性激活函数。
3.卷积层的激活函数使用ReLU函数。
4.最后的输出层为一个全连接线性层。

网络共有7层,包含3个卷积层、2个汇聚层以及2个全连接层的简单卷积神经网络接,受输入图像大小为32×32=1024,输出对应10个类别的得分。
具体实现如下:

import torch.nn.functional as F
import torch.nn as nn
 
 
class Model_LeNet(nn.Module):
    def __init__(self, in_channels, num_classes=10):
        super(Model_LeNet, self).__init__()
        # 卷积层:输出通道数为6,卷积核大小为5×5
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5)
        # 汇聚层:汇聚窗口为2×2,步长为2
        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2)
        # 卷积层:输入通道数为6,输出通道数为16,卷积核大小为5×5,步长为1
        self.conv3 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        # 汇聚层:汇聚窗口为2×2,步长为2
        self.pool4 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        # 卷积层:输入通道数为16,输出通道数为120,卷积核大小为5×5
        self.conv5 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5, stride=1)
        # 全连接层:输入神经元为120,输出神经元为84
        self.linear6 = nn.Linear(120, 84)
        # 全连接层:输入神经元为84,输出神经元为类别数
        self.linear7 = nn.Linear(84, num_classes)
 
    def forward(self, x):
        # C1:卷积层+激活函数
 
        output = F.relu(self.conv1(x))
        # S2:汇聚层
        output = self.pool2(output)
        # C3:卷积层+激活函数
        output = F.relu(self.conv3(output))
        # S4:汇聚层
        output = self.pool4(output)
        # C5:卷积层+激活函数
        output = F.relu(self.conv5(output))
        # 输入层将数据拉平[B,C,H,W] -> [B,CxHxW]
        output = torch.squeeze(output, dim=3)
        output = torch.squeeze(output, dim=2)
        # F6:全连接层
        output = F.relu(self.linear6(output))
        # F7:全连接层
        output = self.linear7(output)
        return output

下面测试一下上面的LeNet-5模型,构造一个形状为 [1,1,32,32]的输入数据送入网络,观察每一层特征图的形状变化。代码实现如下:

# 这里用np.random创建一个随机数组作为输入数据
inputs = np.random.randn(*[1, 1, 32, 32])
inputs = inputs.astype('float32')
# 创建Model_LeNet类的实例,指定模型名称和分类的类别数目
model = Model_LeNet(in_channels=1, num_classes=10)
print(model)
# 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.named_parameters())
x = torch.tensor(inputs)
print(x)
for item in model.children():
    # 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
    item_shapex = 0
    names = []
    parameter = []
    for name in item.named_parameters():
        names.append(name[0])
        parameter.append(name[1])
        item_shapex += 1
    try:
        x = item(x)
    except:
        # 如果是最后一个卷积层输出,需要展平后才可以送入全连接层
        x = x.reshape([x.shape[0], -1])
        x = item(x)
 
    if item_shapex == 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b
        print(item, x.shape, parameter[0].shape, parameter[1].shape)
    else:
        # 汇聚层没有参数
        print(item, x.shape)

运行结果:

Model_LeNet(
  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (pool4): AvgPool2d(kernel_size=(2, 2), stride=2, padding=0)
  (conv5): Conv2d(16, 120, kernel_size=(5, 5), stride=(1, 1))
  (linear6): Linear(in_features=120, out_features=84, bias=True)
  (linear7): Linear(in_features=84, out_features=10, bias=True)
)
<generator object Module.named_parameters at 0x000001FB08489F10>
tensor([[[[-0.3585, -0.5452,  1.3631,  ...,  1.2136, -0.1091,  2.4632],
          [-0.1857,  0.4949, -0.8363,  ..., -0.8915, -0.6937,  1.2373],
          [ 0.0445,  1.9412,  2.5286,  ..., -1.4750, -0.1582, -0.2900],
          ...,
          [-0.8129,  0.9677, -0.7584,  ..., -0.9465, -0.5650,  0.9721],
          [-1.0167, -1.6895,  0.6300,  ...,  2.8304, -0.5249,  0.1658],
          [ 0.1880, -1.6031, -1.4927,  ...,  1.9868,  0.4754,  0.1171]]]])
Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) torch.Size([1, 6, 28, 28]) torch.Size([6, 1, 5, 5]) torch.Size([6])
MaxPool2d(kernel_size=(2, 2), stride=2, padding=0, dilation=1, ceil_mode=False) torch.Size([1, 6, 14, 14])
Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) torch.Size([1, 16, 10, 10]) torch.Size([16, 6, 5, 5]) torch.Size([16])
AvgPool2d(kernel_size=(2, 2), stride=2, padding=0) torch.Size([1, 16, 5, 5])
Conv2d(16, 120, kernel_size=(5, 5), stride=(1, 1)) torch.Size([1, 120, 1, 1]) torch.Size([120, 16, 5, 5]) torch.Size([120])
Linear(in_features=120, out_features=84, bias=True) torch.Size([1, 84]) torch.Size([84, 120]) torch.Size([84])
Linear(in_features=84, out_features=10, bias=True) torch.Size([1, 10]) torch.Size([10, 84]) torch.Size([10])

从输出结果看,

  • 对于大小为32×32的单通道图像,先用6个大小为5×5的卷积核对其进行卷积运算,输出为6个28×28大小的特征图;
  • 6个28×28大小的特征图经过大小为2×2,步长为2的汇聚层后,输出特征图的大小变为14×14;
  • 6个14×14大小的特征图再经过16个大小为5×5的卷积核对其进行卷积运算,得到16个10×10大小的输出特征图;
  • 16个10×10大小的特征图经过大小为2×2,步长为2的汇聚层后,输出特征图的大小变为5×5;
  • 16个5×5大小的特征图再经过120个大小为5×5的卷积核对其进行卷积运算,得到120个1×1大小的输出特征图;
  • 此时,将特征图展平成1维,则有120个像素点,经过输入神经元个数为120,输出神经元个数为84的全连接层后,输出的长度变为84。
  • 再经过一个全连接层的计算,最终得到了长度为类别数的输出结果。

考虑到自定义的Conv2D和Pool2D算子中包含多个for循环,所以运算速度比较慢。飞桨框架中,针对卷积层算子和汇聚层算子进行了速度上的优化,这里基于paddle.nn.Conv2D、paddle.nn.MaxPool2D和paddle.nn.AvgPool2D构建LeNet-5模型,对比与上边实现的模型的运算速度。代码实现如下:

class Torch_LeNet(nn.Module):
    def __init__(self, in_channels, num_classes=10):
        super(Torch_LeNet, self).__init__()
        # 卷积层:输出通道数为6,卷积核大小为5*5
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为6,输出通道数为16,卷积核大小为5*5
        self.conv3 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为16,输出通道数为120,卷积核大小为5*5
        self.conv5 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)
        # 全连接层:输入神经元为120,输出神经元为84
        self.linear6 = nn.Linear(in_features=120, out_features=84)
        # 全连接层:输入神经元为84,输出神经元为类别数
        self.linear7 = nn.Linear(in_features=84, out_features=num_classes)
 
    def forward(self, x):
        # C1:卷积层+激活函数
        output = F.relu(self.conv1(x))
        # S2:汇聚层
        output = self.pool2(output)
        # C3:卷积层+激活函数
        output = F.relu(self.conv3(output))
        # S4:汇聚层
        output = self.pool4(output)
        # C5:卷积层+激活函数
        output = F.relu(self.conv5(output))
        # 输入层将数据拉平[B,C,H,W] -> [B,CxHxW]
        output = torch.squeeze(output, dim=3)
        output = torch.squeeze(output, dim=2)
        # F6:全连接层
        output = F.relu(self.linear6(output))
        # F7:全连接层
        output = self.linear7(output)
        return output

测试两个网络的运算速度。

import time
 
# 这里用np.random创建一个随机数组作为测试数据
inputs = np.random.randn(*[1,1,32,32])
inputs = inputs.astype('float32')
x = torch.tensor(inputs)
 
# 创建Model_LeNet类的实例,指定模型名称和分类的类别数目
model = Model_LeNet(in_channels=1, num_classes=10)
# 创建Torch_LeNet类的实例,指定模型名称和分类的类别数目
torch_model = Torch_LeNet(in_channels=1, num_classes=10)
 
# 计算Model_LeNet类的运算速度
model_time = 0
for i in range(60):
    strat_time = time.time()
    out = model(x)
    end_time = time.time()
    # 预热10次运算,不计入最终速度统计
    if i < 10:
        continue
    model_time += (end_time - strat_time)
avg_model_time = model_time / 50
print('Model_LeNet speed:', avg_model_time, 's')
# 计算Torch_LeNet类的运算速度
torch_model_time = 0
for i in range(60):
    strat_time = time.time()
    torch_out = torch_model(x)
    end_time = time.time()
    # 预热10次运算,不计入最终速度统计
    if i < 10:
        continue
    torch_model_time += (end_time - strat_time)
avg_torch_model_time = torch_model_time / 50
 
print('Torch_LeNet speed:', avg_torch_model_time, 's')

运行结果:

Model_LeNet speed: 0.0004978704452514648 s
Torch_LeNet speed: 0.000518350601196289 s

这里还可以令两个网络加载同样的权重,测试一下两个网络的输出结果是否一致。

# 这里用np.random创建一个随机数组作为测试数据
inputs = np.random.randn(*[1,1,32,32])
inputs = inputs.astype('float32')
x = torch.tensor(inputs)
 
# 创建Model_LeNet类的实例,指定模型名称和分类的类别数目
model = Model_LeNet(in_channels=1, num_classes=10)
# 获取网络的权重
params = model.state_dict()
# 自定义Conv2D算子的bias参数形状为[out_channels, 1]
# torch API中Conv2D算子的bias参数形状为[out_channels]
# 需要进行调整后才可以赋值
for key in params:
    if 'bias' in key:
        params[key] = params[key].squeeze()
# 创建Torch_LeNet类的实例,指定模型名称和分类的类别数目
torch_model = Torch_LeNet(in_channels=1, num_classes=10)
# 将Model_LeNet的权重参数赋予给Torch_LeNet模型,保持两者一致
torch_model.load_state_dict(params)
 
# 打印结果保留小数点后6位
torch.set_printoptions(6)
# 计算Model_LeNet的结果
output = model(x)
print('Model_LeNet output: ', output)
# 计算Torch_LeNet的结果
torch_output = torch_model(x)
print('Torch_LeNet output: ', torch_output)

运行结果:

Model_LeNet output:  tensor([[ 0.026521,  0.122189, -0.049708, -0.073667, -0.013976, -0.019401,
          0.041322, -0.131140, -0.007805, -0.044831]],
       grad_fn=<AddmmBackward0>)
Torch_LeNet output:  tensor([[ 0.026521,  0.122189, -0.049708, -0.073667, -0.013976, -0.019401,
          0.041322, -0.131140, -0.007805, -0.044831]],
       grad_fn=<AddmmBackward0>)

可以看到,输出结果是一致的。

这里还可以统计一下LeNet-5模型的参数量和计算量。
参数量

按照公式(5.18)进行计算,可以得到:

  • 第一个卷积层的参数量为:6×1×5×5+6=156;
  • 第二个卷积层的参数量为:16×6×5×5+16=2416;
  • 第三个卷积层的参数量为:120×16×5×5+120=48120;
  • 第一个全连接层的参数量为:120×84+84=10164;
  • 第二个全连接层的参数量为:84×10+10=850;

所以,LeNet-5总的参数量为61706。

在飞桨中,还可以使用paddle.summaryAPI自动计算参数量。

from torchsummary import summary
model = Torch_LeNet(in_channels=1, num_classes=10)
params_info = summary(model, (1, 32, 32))
print(params_info)

运行结果:

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1            [-1, 6, 28, 28]             156
         MaxPool2d-2            [-1, 6, 14, 14]               0
            Conv2d-3           [-1, 16, 10, 10]           2,416
         AvgPool2d-4             [-1, 16, 5, 5]               0
            Conv2d-5            [-1, 120, 1, 1]          48,120
            Linear-6                   [-1, 84]          10,164
            Linear-7                   [-1, 10]             850
================================================================
Total params: 61,706
Trainable params: 61,706
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.06
Params size (MB): 0.24
Estimated Total Size (MB): 0.30
----------------------------------------------------------------
None

可以看到,结果与公式推导一致。

计算量

按照公式(5.19)进行计算,可以得到:

  • 第一个卷积层的计算量为: 28 × 28 × 5 × 5 × 6 × 1 + 28 × 28 × 6 = 122304 28\times 28\times 5\times 5\times 6\times 1 + 28\times 28\times 6=122304 28×28×5×5×6×1+28×28×6=122304;
  • 第二个卷积层的计算量为: 10 × 10 × 5 × 5 × 16 × 6 + 10 × 10 × 16 = 241600 10\times 10\times 5\times 5\times 16\times 6 + 10\times 10\times 16=241600 10×10×5×5×16×6+10×10×16=241600;
  • 第三个卷积层的计算量为: 1 × 1 × 5 × 5 × 120 × 16 + 1 × 1 × 120 = 48120 1\times 1\times 5\times 5\times 120\times 16 + 1\times 1\times 120=48120 1×1×5×5×120×16+1×1×120=48120;
  • 平均汇聚层的计算量为: 16 × 5 × 5 = 400 16\times 5\times 5=400 16×5×5=400
  • 第一个全连接层的计算量为: 120 × 84 = 10080 120 \times 84 = 10080 120×84=10080;
  • 第二个全连接层的计算量为: 84 × 10 = 840 84 \times 10 = 840 84×10=840;

所以,LeNet-5总的计算量为 423344 423344 423344。

在飞桨中,还可以使用paddle.flopsAPI自动统计计算量。

可以看到,结果与公式推导一致。

5.3.3 模型训练

使用交叉熵损失函数,并用随机梯度下降法作为优化器来训练LeNet-5网络。
用RunnerV3在训练集上训练5个epoch,并保存准确率最高的模型作为最佳模型。

import  torch
from torch import nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
import warnings
warnings.filterwarnings("ignore", category=UserWarning)

class RunnerV3(object):
    def __init__(self, model, optimizer, loss_fn, metric, **kwargs):
        self.model = model
        self.optimizer = optimizer
        self.loss_fn = loss_fn
        self.metric = metric  # 只用于计算评价指标

        # 记录训练过程中的评价指标变化情况
        self.dev_scores = []

        # 记录训练过程中的损失函数变化情况
        self.train_epoch_losses = []  # 一个epoch记录一次loss
        self.train_step_losses = []  # 一个step记录一次loss
        self.dev_losses = []

        # 记录全局最优指标
        self.best_score = 0

    def train(self, train_loader, dev_loader=None, **kwargs):
        # 将模型切换为训练模式
        self.model.train()

        # 传入训练轮数,如果没有传入值则默认为0
        num_epochs = kwargs.get("num_epochs", 0)
        # 传入log打印频率,如果没有传入值则默认为100
        log_steps = kwargs.get("log_steps", 100)
        # 评价频率
        eval_steps = kwargs.get("eval_steps", 0)

        # 传入模型保存路径,如果没有传入值则默认为"best_model.pdparams"
        save_path = kwargs.get("save_path", "best_model.pdparams")

        custom_print_log = kwargs.get("custom_print_log", None)

        # 训练总的步数
        num_training_steps = num_epochs * len(train_loader)

        if eval_steps:
            if self.metric is None:
                raise RuntimeError('Error: Metric can not be None!')
            if dev_loader is None:
                raise RuntimeError('Error: dev_loader can not be None!')

        # 运行的step数目
        global_step = 0

        # 进行num_epochs轮训练
        for epoch in range(num_epochs):
            # 用于统计训练集的损失
            total_loss = 0
            for step, data in enumerate(train_loader):
                X, y = data
                # 获取模型预测
                logits = self.model(X)
                y = torch.tensor(y, dtype=torch.int64)
                loss = self.loss_fn(logits, y)  # 默认求mean
                total_loss += loss

                # 训练过程中,每个step的loss进行保存
                self.train_step_losses.append((global_step, loss.item()))

                if log_steps and global_step % log_steps == 0:
                    print(
                        f"[Train] epoch: {epoch}/{num_epochs}, step: {global_step}/{num_training_steps}, loss: {loss.item():.5f}")

                # 梯度反向传播,计算每个参数的梯度值
                loss.backward()

                if custom_print_log:
                    custom_print_log(self)

                # 小批量梯度下降进行参数更新
                self.optimizer.step()
                # 梯度归零
                self.optimizer.zero_grad()

                # 判断是否需要评价
                if eval_steps > 0 and global_step > 0 and \
                        (global_step % eval_steps == 0 or global_step == (num_training_steps - 1)):

                    dev_score, dev_loss = self.evaluate(dev_loader, global_step=global_step)
                    print(f"[Evaluate]  dev score: {dev_score:.5f}, dev loss: {dev_loss:.5f}")

                    # 将模型切换为训练模式
                    self.model.train()

                    # 如果当前指标为最优指标,保存该模型
                    if dev_score > self.best_score:
                        self.save_model(save_path)
                        print(
                            f"[Evaluate] best accuracy performence has been updated: {self.best_score:.5f} --> {dev_score:.5f}")
                        self.best_score = dev_score

                global_step += 1

            # 当前epoch 训练loss累计值
            trn_loss = (total_loss / len(train_loader)).item()
            # epoch粒度的训练loss保存
            self.train_epoch_losses.append(trn_loss)

        print("[Train] Training done!")

    # 模型评估阶段,使用'paddle.no_grad()'控制不计算和存储梯度
    @torch.no_grad()
    def evaluate(self, dev_loader, **kwargs):
        assert self.metric is not None

        # 将模型设置为评估模式
        self.model.eval()

        global_step = kwargs.get("global_step", -1)

        # 用于统计训练集的损失
        total_loss = 0

        # 重置评价
        self.metric.reset()

        # 遍历验证集每个批次
        for batch_id, data in enumerate(dev_loader):
            X, y = data

            # 计算模型输出
            logits = self.model(X)
            y = torch.tensor(y, dtype=torch.int64)

            # 计算损失函数
            loss = self.loss_fn(logits, y).item()
            # 累积损失
            total_loss += loss

            # 累积评价
            self.metric.update(logits, y)

        dev_loss = (total_loss / len(dev_loader))
        dev_score = self.metric.accumulate()

        # 记录验证集loss
        if global_step != -1:
            self.dev_losses.append((global_step, dev_loss))
            self.dev_scores.append(dev_score)

        return dev_score, dev_loss

    # 模型评估阶段,使用'paddle.no_grad()'控制不计算和存储梯度
    @torch.no_grad()
    def predict(self, x, **kwargs):
        # 将模型设置为评估模式
        self.model.eval()
        # 运行模型前向计算,得到预测值
        logits = self.model(x)
        return logits

    def save_model(self, save_path):
        torch.save(self.model.state_dict(), save_path)

    def load_model(self, model_path):
        model_state_dict = torch.load(model_path)
        self.model.set_state_dict(model_state_dict)

class torch_LeNet(nn.Module):
    def __init__(self, in_channels, num_classes=10):
        super(torch_LeNet, self).__init__()
        # 卷积层:输出通道数为6,卷积核大小为5*5
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为6,输出通道数为16,卷积核大小为5*5
        self.conv3 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为16,输出通道数为120,卷积核大小为5*5
        self.conv5 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)
        # 全连接层:输入神经元为120,输出神经元为84
        self.linear6 = nn.Linear(in_features=120, out_features=84)
        # 全连接层:输入神经元为84,输出神经元为类别数
        self.linear7 = nn.Linear(in_features=84, out_features=num_classes)

    def forward(self, x):
        # C1:卷积层+激活函数
        output = F.relu(self.conv1(x))
        # S2:汇聚层
        output = self.pool2(output)
        # C3:卷积层+激活函数
        output = F.relu(self.conv3(output))
        # S4:汇聚层
        output = self.pool4(output)
        # C5:卷积层+激活函数
        output = F.relu(self.conv5(output))
        # 输入层将数据拉平[B,C,H,W] -> [B,CxHxW]
        output = torch.squeeze(output)
        # F6:全连接层
        output = F.relu(self.linear6(output))
        # F7:全连接层
        output = self.linear7(output)
        return output
lr = 0.1
# 批次大小
batch_size = 64


class MNIST_dataset(torch.utils.data.Dataset):
    def __init__(self, dataset, transforms, mode='train'):
        self.mode = mode
        self.transforms =transforms
        self.dataset = dataset

    def __getitem__(self, idx):
        # 获取图像和标签
        image, label = self.dataset[0][idx], self.dataset[1][idx]
        image, label = np.array(image).astype('float32'), int(label)
        image = np.reshape(image, [28,28])
        image = Image.fromarray(image.astype('uint8'), mode='L')
        image = self.transforms(image)

        return image, label

    def __len__(self):
        return len(self.dataset[0])

from torchvision.transforms import Compose, Resize, Normalize,ToTensor
def accuracy(preds, labels):
    # 判断是二分类任务还是多分类任务,preds.shape[1]=1时为二分类任务,preds.shape[1]>1时为多分类任务
    if preds.shape[1] == 1:
        # 二分类时,判断每个概率值是否大于0.5,当大于0.5时,类别为1,否则类别为0
        # 使用'torch.tensor()'将preds的数据类型转换为float32类型
        preds = torch.as_tensor((preds >= 0.5),dtype=torch.float32)
    else:
        # 多分类时,使用'torch.argmax'计算最大元素索引作为类别
        preds = torch.argmax(preds, dim=1).int()
    return torch.mean(torch.as_tensor((preds == labels),dtype=torch.float32))


import torch


class Accuracy():
    def __init__(self, is_logist=True):
        """
        输入:
           - is_logist: outputs是logist还是激活后的值
        """

        # 用于统计正确的样本个数
        self.num_correct = 0
        # 用于统计样本的总数
        self.num_count = 0

        self.is_logist = is_logist

    def update(self, outputs, labels):
        """
        输入:
           - outputs: 预测值, shape=[N,class_num]
           - labels: 标签值, shape=[N,1]
        """

        # 判断是二分类任务还是多分类任务,shape[1]=1时为二分类任务,shape[1]>1时为多分类任务
        if outputs.shape[1] == 1:  # 二分类
            outputs = torch.squeeze(outputs, dim=-1)
            if self.is_logist:
                # logist判断是否大于0
                preds = torch.tensor((outputs >= 0), dtype=torch.float32)
            else:
                # 如果不是logist,判断每个概率值是否大于0.5,当大于0.5时,类别为1,否则类别为0
                preds = torch.tensor((outputs >= 0.5), dtype=torch.float32)
        else:
            # 多分类时,使用'paddle.argmax'计算最大元素索引作为类别
            preds = torch.argmax(outputs, dim=1)
            preds = torch.tensor(preds, dtype=torch.int64)

        # 获取本批数据中预测正确的样本个数
        labels = torch.squeeze(labels, dim=-1)
        batch_correct = torch.sum(torch.tensor(preds == labels, dtype=torch.float32)).numpy()
        batch_count = len(labels)

        # 更新num_correct 和 num_count
        self.num_correct += batch_correct
        self.num_count += batch_count

    def accumulate(self):
        # 使用累计的数据,计算总的指标
        if self.num_count == 0:
            return 0
        return self.num_correct / self.num_count

    def reset(self):
        # 重置正确的数目和总数
        self.num_correct = 0
        self.num_count = 0

    def name(self):
        return "Accuracy"
# 数据预处理

transforms = Compose([Resize(32),ToTensor(),  Normalize(mean=[127.5], std=[127.5])])
# 加载 mnist 数据集
train_dataset = MNIST_dataset(dataset=train_set, transforms=transforms, mode='train')
test_dataset = MNIST_dataset(dataset=test_set, transforms=transforms, mode='test')
dev_dataset = MNIST_dataset(dataset=dev_set, transforms=transforms, mode='dev')
# 加载数据
train_loader =	torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
dev_loader = 	torch.utils.data.DataLoader(dev_dataset, batch_size=batch_size)
test_loader = 	torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)
# 定义LeNet网络
# 自定义算子实现的LeNet-5
# model = Model_LeNet(in_channels=1, num_classes=10)
# 飞桨API实现的LeNet-5
model = torch_LeNet(in_channels=1, num_classes=10)
# 定义优化器
optimizer = torch.optim.SGD(lr=lr, params=model.parameters())
# 定义损失函数
loss_fn = F.cross_entropy
# 定义评价指标
metric = Accuracy(is_logist=True)
# 实例化 RunnerV3 类,并传入训练配置。
runner = RunnerV3(model=model, optimizer=optimizer,loss_fn = loss_fn, metric=metric)
# 启动训练
log_steps = 15
eval_steps = 15
runner.train(train_loader, dev_loader, num_epochs=5, log_steps=log_steps,
                eval_steps=eval_steps, save_path="best_model.pdparams")

运行结果:

[Train] epoch: 0/5, step: 0/80, loss: 2.31964
[Train] epoch: 0/5, step: 15/80, loss: 1.96424
[Evaluate]  dev score: 0.31000, dev loss: 1.94272
[Evaluate] best accuracy performence has been updated: 0.00000 --> 0.31000
[Train] epoch: 1/5, step: 30/80, loss: 1.90126
[Evaluate]  dev score: 0.58500, dev loss: 1.58386
[Evaluate] best accuracy performence has been updated: 0.31000 --> 0.58500
[Train] epoch: 2/5, step: 45/80, loss: 0.92311
[Evaluate]  dev score: 0.63000, dev loss: 1.17126
[Evaluate] best accuracy performence has been updated: 0.58500 --> 0.63000
[Train] epoch: 3/5, step: 60/80, loss: 0.31175
[Evaluate]  dev score: 0.80000, dev loss: 0.40704
[Evaluate] best accuracy performence has been updated: 0.63000 --> 0.80000
[Train] epoch: 4/5, step: 75/80, loss: 0.36434
[Evaluate]  dev score: 0.83000, dev loss: 0.44397
[Evaluate] best accuracy performence has been updated: 0.80000 --> 0.83000
[Evaluate]  dev score: 0.85000, dev loss: 0.50347
[Evaluate] best accuracy performence has been updated: 0.83000 --> 0.85000
[Train] Training done!

可视化观察训练集与验证集的损失变化情况。

# 可视化误差
def plot(runner, fig_name):
    plt.figure(figsize=(10, 5))
 
    plt.subplot(1, 2, 1)
    train_items = runner.train_step_losses[::30]
    train_steps = [x[0] for x in train_items]
    train_losses = [x[1] for x in train_items]
 
    plt.plot(train_steps, train_losses, color='#8E004D', label="Train loss")
    if runner.dev_losses[0][0] != -1:
        dev_steps = [x[0] for x in runner.dev_losses]
        dev_losses = [x[1] for x in runner.dev_losses]
        plt.plot(dev_steps, dev_losses, color='#E20079', linestyle='--', label="Dev loss")
    # 绘制坐标轴和图例
    plt.ylabel("loss", fontsize='x-large')
    plt.xlabel("step", fontsize='x-large')
    plt.legend(loc='upper right', fontsize='x-large')
 
    plt.subplot(1, 2, 2)
    # 绘制评价准确率变化曲线
    if runner.dev_losses[0][0] != -1:
        plt.plot(dev_steps, runner.dev_scores,
                 color='#E20079', linestyle="--", label="Dev accuracy")
    else:
        plt.plot(list(range(len(runner.dev_scores))), runner.dev_scores,
                 color='#E20079', linestyle="--", label="Dev accuracy")
    # 绘制坐标轴和图例
    plt.ylabel("score", fontsize='x-large')
    plt.xlabel("step", fontsize='x-large')
    plt.legend(loc='lower right', fontsize='x-large')
 
    plt.savefig(fig_name)
    plt.show()
 
 
runner.load_model('best_model.pdparams')
plot(runner, 'cnn-loss1.pdf')

运行结果:

[Evaluate]  dev score: 0.50000, dev loss: 1.37084
[Evaluate] best accuracy performence has been updated: 0.30000 --> 0.50000
[Train] epoch: 4/10, step: 75/160, loss: 1.79037
[Evaluate]  dev score: 0.39500, dev loss: 1.68836
[Train] epoch: 5/10, step: 90/160, loss: 0.97746
[Evaluate]  dev score: 0.72000, dev loss: 0.99998
[Evaluate] best accuracy performence has been updated: 0.50000 --> 0.72000
[Train] epoch: 6/10, step: 105/160, loss: 0.65764
[Evaluate]  dev score: 0.76000, dev loss: 0.60664
[Evaluate] best accuracy performence has been updated: 0.72000 --> 0.76000
[Train] epoch: 7/10, step: 120/160, loss: 0.36129
[Evaluate]  dev score: 0.77000, dev loss: 0.60093
[Evaluate] best accuracy performence has been updated: 0.76000 --> 0.77000
[Train] epoch: 8/10, step: 135/160, loss: 0.28965
[Evaluate]  dev score: 0.84000, dev loss: 0.39456
[Evaluate] best accuracy performence has been updated: 0.77000 --> 0.84000
[Train] epoch: 9/10, step: 150/160, loss: 0.15746
[Evaluate]  dev score: 0.87500, dev loss: 0.31205
[Evaluate] best accuracy performence has been updated: 0.84000 --> 0.87500
[Evaluate]  dev score: 0.89500, dev loss: 0.28777
[Evaluate] best accuracy performence has been updated: 0.87500 --> 0.89500
[Train] Training done!

5.3.4 模型评价

使用测试数据对在训练过程中保存的最佳模型进行评价,观察模型在测试集上的准确率以及损失变化情况。

# 加载最优模型
runner.load_model('best_model.pdparams')
# 模型评价
score, loss = runner.evaluate(test_loader)
print("[Test] accuracy/loss: {:.4f}/{:.4f}".format(score, loss))

运行结果:

[Test] accuracy/loss: 0.9050/0.2857

5.3.5 模型预测

同样地,我们也可以使用保存好的模型,对测试集中的某一个数据进行模型预测,观察模型效果。

# 获取测试集中第一条数
X, label = next(iter(test_loader))
logits = runner.predict(X)
# 多分类,使用softmax计算预测概率
pred = F.softmax(logits, dim=1)
print(pred.shape)
# 获取概率最大的类别
pred_class = torch.argmax(pred[1]).numpy()
print(pred_class)
label = label[1].numpy()
# 输出真实类别与预测类别
print("The true category is {} and the predicted category is {}".format(label, pred_class))
# 可视化图片
plt.figure(figsize=(2, 2))
image, label = test_set[0][1], test_set[1][1]
image= np.array(image).astype('float32')
image = np.reshape(image, [28,28])
image = Image.fromarray(image.astype('uint8'), mode='L')
plt.imshow(image)
plt.savefig('cnn-number2.pdf')

运行结果:

The true category is 1 and the predicted category is 1

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第4张图片
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第5张图片

选做题

使用前馈神经网络实现MNIST识别,与LeNet效果对比。(选做)

# coding=gbk
import numpy as np
import torch
import matplotlib.pyplot as plt
from torchvision.datasets import mnist
from torchvision import transforms
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
 
train_batch_size = 64#超参数
test_batch_size = 128#超参数
learning_rate = 0.01#学习率
nums_epoches = 20#训练次数
lr = 0.1#优化器参数
momentum = 0.5#优化器参数
train_dataset = mnist.MNIST('./data', train=True, transform=transforms.ToTensor(), target_transform=None, download=True)
test_dataset = mnist.MNIST('./data', train=False, transform=transforms.ToTensor(), target_transform=None, download=False)
train_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)
 
class model(nn.Module):
    def __init__(self, in_dim, hidden_1, hidden_2, out_dim):
        super(model, self).__init__()
        self.layer1 = nn.Sequential(nn.Linear(in_dim, hidden_1, bias=True), nn.BatchNorm1d(hidden_1))
        self.layer2 = nn.Sequential(nn.Linear(hidden_1, hidden_2, bias=True), nn.BatchNorm1d(hidden_2))
        self.layer3 = nn.Sequential(nn.Linear(hidden_2, out_dim))
 
    def forward(self, x):
        # 注意 F 与 nn 下的激活函数使用起来不一样的
        x = F.relu(self.layer1(x))
        x = F.relu(self.layer2(x))
        x = self.layer3(x)
        return x
 
#实例化网络
model = model(28*28,300,100,10)
#定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
#momentum:动量因子
optimizer = optim.SGD(model.parameters(),lr=lr,momentum=momentum)
def train():
    # 开始训练 先定义存储损失函数和准确率的数组
    losses = []
    acces = []
    # 测试用
    eval_losses = []
    eval_acces = []
 
    for epoch in range(nums_epoches):
        # 每次训练先清零
        train_loss = 0
        train_acc = 0
        # 将模型设置为训练模式
        model.train()
        # 动态学习率
        if epoch % 5 == 0:
            optimizer.param_groups[0]['lr'] *= 0.1
        for img, label in train_loader:
            # 例如 img=[64,1,28,28] 做完view()后变为[64,1*28*28]=[64,784]
            # 把图片数据格式转换成与网络匹配的格式
            img = img.view(img.size(0), -1)
            # 前向传播,将图片数据传入模型中
            # out输出10维,分别是各数字的概率,即每个类别的得分
            out = model(img)
            # 这里注意参数out是64*10,label是一维的64
            loss = criterion(out, label)
            # 反向传播
            # optimizer.zero_grad()意思是把梯度置零,也就是把loss关于weight的导数变成0
            optimizer.zero_grad()
            loss.backward()
            # 这个方法会更新所有的参数,一旦梯度被如backward()之类的函数计算好后,我们就可以调用这个函数
            optimizer.step()
            # 记录误差
            train_loss += loss.item()
            # 计算分类的准确率,找到概率最大的下标
            _, pred = out.max(1)
            num_correct = (pred == label).sum().item()  # 记录标签正确的个数
            acc = num_correct / img.shape[0]
            train_acc += acc
        losses.append(train_loss / len(train_loader))
        acces.append(train_acc / len(train_loader))
 
        eval_loss = 0
        eval_acc = 0
        model.eval()
        for img, label in test_loader:
            img = img.view(img.size(0), -1)
 
            out = model(img)
            loss = criterion(out, label)
 
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
 
            eval_loss += loss.item()
 
            _, pred = out.max(1)
            num_correct = (pred == label).sum().item()
            acc = num_correct / img.shape[0]
            eval_acc += acc
        eval_losses.append(eval_loss / len(test_loader))
        eval_acces.append(eval_acc / len(test_loader))
 
        print('epoch:{},Train Loss:{:.4f},Train Acc:{:.4f},Test Loss:{:.4f},Test Acc:{:.4f}'
              .format(epoch, train_loss / len(train_loader), train_acc / len(train_loader),
                      eval_loss / len(test_loader), eval_acc / len(test_loader)))
    plt.title('trainloss')
    plt.plot(np.arange(len(losses)), losses)
    plt.legend(['Train Loss'], loc='upper right')
#测试
from sklearn.metrics import confusion_matrix
import seaborn as sns
def test():
    correct = 0
    total = 0
    y_predict=[]
    y_true=[]
    with torch.no_grad():
        for data in test_loader:
            input, target = data
            input = input.view(input.size(0), -1)
            output = model(input)#输出十个最大值
            _, predict = torch.max(output.data, dim=1)#元组取最大值的下表
            #
            #print('predict:',predict)
            total += target.size(0)
            correct += (predict == target).sum().item()
            y_predict.extend(predict.tolist())
            y_true.extend(target.tolist())
    print('正确率:', correct / total)
    print('correct=', correct)
    sns.set()
    f, ax = plt.subplots()
    C2 = confusion_matrix(y_true, y_predict, labels=[0, 1, 2,3,4,5,6,7,8,9])
    print(C2)
    plt.imshow(C2, cmap=plt.cm.Blues)
    plt.xticks(range(10),labels=[0, 1, 2,3,4,5,6,7,8,9] , rotation=45)
    plt.yticks(range(10),labels=[0, 1, 2,3,4,5,6,7,8,9])
    plt.colorbar()
    plt.xlabel('True Labels')
    plt.ylabel('Predicted Labels')
    plt.title('Confusion matrix (acc=' + str(correct / total)+ ')')
 
    plt.show()
 
train()
test()

运行结果:

epoch:0,Train Loss:0.3542,Train Acc:0.9152,Test Loss:0.1281,Test Acc:0.9617
epoch:1,Train Loss:0.1271,Train Acc:0.9665,Test Loss:0.0822,Test Acc:0.9767
epoch:2,Train Loss:0.0865,Train Acc:0.9769,Test Loss:0.0619,Test Acc:0.9825
epoch:3,Train Loss:0.0646,Train Acc:0.9825,Test Loss:0.0504,Test Acc:0.9857
epoch:4,Train Loss:0.0536,Train Acc:0.9853,Test Loss:0.0423,Test Acc:0.9891
epoch:5,Train Loss:0.0362,Train Acc:0.9912,Test Loss:0.0265,Test Acc:0.9940
epoch:6,Train Loss:0.0328,Train Acc:0.9924,Test Loss:0.0269,Test Acc:0.9940
epoch:7,Train Loss:0.0312,Train Acc:0.9935,Test Loss:0.0258,Test Acc:0.9941
epoch:8,Train Loss:0.0306,Train Acc:0.9930,Test Loss:0.0258,Test Acc:0.9937
epoch:9,Train Loss:0.0293,Train Acc:0.9938,Test Loss:0.0251,Test Acc:0.9941
epoch:10,Train Loss:0.0276,Train Acc:0.9943,Test Loss:0.0249,Test Acc:0.9944
epoch:11,Train Loss:0.0277,Train Acc:0.9944,Test Loss:0.0242,Test Acc:0.9941
epoch:12,Train Loss:0.0277,Train Acc:0.9945,Test Loss:0.0243,Test Acc:0.9948
epoch:13,Train Loss:0.0277,Train Acc:0.9943,Test Loss:0.0241,Test Acc:0.9947
epoch:14,Train Loss:0.0280,Train Acc:0.9941,Test Loss:0.0237,Test Acc:0.9946
epoch:15,Train Loss:0.0275,Train Acc:0.9945,Test Loss:0.0242,Test Acc:0.9952
epoch:16,Train Loss:0.0278,Train Acc:0.9942,Test Loss:0.0242,Test Acc:0.9950
epoch:17,Train Loss:0.0272,Train Acc:0.9945,Test Loss:0.0248,Test Acc:0.9944
epoch:18,Train Loss:0.0274,Train Acc:0.9946,Test Loss:0.0236,Test Acc:0.9947
epoch:19,Train Loss:0.0273,Train Acc:0.9950,Test Loss:0.0240,Test Acc:0.9946
正确率: 0.9946
correct= 9946

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第6张图片

可视化LeNet中的部分特征图和卷积核,谈谈自己的看法。(选做)

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第7张图片
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第8张图片
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第9张图片
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第10张图片

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第11张图片
代码如下:

class Paddle_LeNet1(nn.Module):
    def __init__(self, in_channels, num_classes=10):
        super(Paddle_LeNet1, self).__init__()
        # 卷积层:输出通道数为6,卷积核大小为5*5
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为6,输出通道数为16,卷积核大小为5*5
        self.conv3 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)
        # 汇聚层:汇聚窗口为2*2,步长为2
        self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)
        # 卷积层:输入通道数为16,输出通道数为120,卷积核大小为5*5
        self.conv5 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)
        # 全连接层:输入神经元为120,输出神经元为84
        self.linear6 = nn.Linear(in_features=120, out_features=84)
        # 全连接层:输入神经元为84,输出神经元为类别数
        self.linear7 = nn.Linear(in_features=84, out_features=num_classes)
 
    def forward(self, x):
        image=[]
        # C1:卷积层+激活函数
        output = F.relu(self.conv1(x))
        image.append(output)
        # S2:汇聚层
        output = self.pool2(output)
 
        # C3:卷积层+激活函数
        output = F.relu(self.conv3(output))
        image.append(output)
        # S4:汇聚层
        output = self.pool4(output)
        # C5:卷积层+激活函数
        output = F.relu(self.conv5(output))
        image.append(output)
        # 输入层将数据拉平[B,C,H,W] -> [B,CxHxW]
        output = torch.squeeze(output, dim=3)
        output = torch.squeeze(output, dim=2)
        # F6:全连接层
        output = F.relu(self.linear6(output))
        # F7:全连接层
        output = self.linear7(output)
        return image
 
 
 
 
 
# create model
model1 = Paddle_LeNet1(in_channels=1, num_classes=10)
 
 
# model_weight_path ="./AlexNet.pth"
model_weight_path = 'best_model.pdparams'
model1.load_state_dict(torch.load(model_weight_path))
 
 
# forward正向传播过程
out_put = model1(X)
print(out_put[0].shape)
for i in range(0,3):
 for feature_map in out_put[i]:
    # [N, C, H, W] -> [C, H, W]    维度变换
    im = np.squeeze(feature_map.detach().numpy())
    # [C, H, W] -> [H, W, C]
    im = np.transpose(im, [1, 2, 0])
    print(im.shape)
 
    # show 9 feature maps
    plt.figure()
    for i in range(6):
        ax = plt.subplot(2, 3, i + 1)  # 参数意义:3:图片绘制行数,5:绘制图片列数,i+1:图的索引
        # [H, W, C]
        # 特征矩阵每一个channel对应的是一个二维的特征矩阵,就像灰度图像一样,channel=1
        # plt.imshow(im[:, :, i])i,,
        plt.imshow(im[:, :, i], cmap='gray')
    plt.show()
    break

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第12张图片
NNDL 实验六 卷积神经网络(3)LeNet实现MNIST_第13张图片


总结

心得体会

此次实验完成了基于LeNet实现手写体字数字识别实验,对LeNet网络的构建和参数量、计算量有了更深的了解,与LeNet效果进行对比,得出在准确率和loss上,LeNet要明显好于FNN。

参考链接

NNDL 实验六 卷积神经网络(3)LeNet实现MNIST
NNDL 实验5(上) - HBU_DAVID - 博客园 (cnblogs.com)
7. 现代卷积神经网络 — 动手学深度学习 2.0.0-beta1 documentation (d2l.ai)

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