簡要聲明

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1. 學習相關網址
   1. [雙語字幕]吳恩達深度學習deeplearning.ai
   2. Papers With Code
   3. Datasets
2. 深度學習網絡基于PyTorch學習架構，代碼測試可跑。
3. 本學習筆記單純是為了能對學到的內容有更深入的理解，如果有錯誤的地方，懇請包容和指正。

參考文獻

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1. PyTorch Tutorials [https://pytorch.org/tutorials/]
2. PyTorch Docs [https://pytorch.org/docs/stable/index.html]
3. LeNet (1998) [Gradient-based learning applied to document recognition]

簡要介紹

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LeNet

Dataset	MNIST
Input (feature maps)	32×32 (28×28)
CONV Layers	2
FC Layers	2
Activation	Sigmoid
Output	10

代碼分析

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函數庫調用

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

處理數據

數據下載

# 從開放數據集中下載訓練數據
train_data = datasets.MNIST(root="data",train=True,download=True,transform=ToTensor(),
)# 從開放數據集中下載測試數據
test_data = datasets.MNIST(root="data",train=False,download=True,transform=ToTensor(),
)print(f'Number of training examples: {len(train_data)}')
print(f'Number of testing examples: {len(test_data)}')

Number of training examples: 60000
Number of testing examples: 10000

數據加載器（可選）

batch_size = 64# 創建數據加載器
train_dataloader = DataLoader(train_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)for X, y in test_dataloader:print(f"Shape of X [N, C, H, W]: {X.shape}")print(f"Shape of y: {y.shape} {y.dtype}")break

Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64

創建模型

# 選擇訓練設備
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using {device} device")

Using cuda device

class LeNet(nn.Module):def __init__(self, output_dim):super().__init__()self.conv_1 = nn.Sequential(nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2, stride=2))self.conv_2 = nn.Sequential(nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),nn.Sigmoid(),nn.MaxPool2d(kernel_size=2, stride=2))self.fc_1 = nn.Sequential(nn.Linear(16*5*5, 120),nn.Sigmoid())self.fc_2 = nn.Sequential(nn.Linear(120, 84),nn.Sigmoid())self.fc_3 = nn.Linear(84, output_dim)def forward(self, x):x = self.conv_1(x)x = self.conv_2(x)x = x.view(x.size(0), -1)x = self.fc_1(x)x = self.fc_2(x)x = self.fc_3(x)return xmodel = LeNet(10).to(device)
print(model)

LeNet(
(conv_1): Sequential(
(0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): Sigmoid()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv_2): Sequential(
(0): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(1): Sigmoid()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(fc_1): Sequential(
(0): Linear(in_features=400, out_features=120, bias=True)
(1): Sigmoid()
)
(fc_2): Sequential(
(0): Linear(in_features=120, out_features=84, bias=True)
(1): Sigmoid()
)
(fc_3): Linear(in_features=84, out_features=10, bias=True)
)

訓練模型

選擇損失函數和優化器

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

訓練循環

def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)model.train()for batch, (X, y) in enumerate(dataloader):X, y = X.to(device), y.to(device)# Compute prediction errorpred = model(X)loss = loss_fn(pred, y)# Backpropagationloss.backward()optimizer.step()optimizer.zero_grad()if batch % 100 == 0:loss, current = loss.item(), (batch + 1) * len(X)print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

測試循環

def test(dataloader, model, loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for X, y in dataloader:X, y = X.to(device), y.to(device)pred = model(X)test_loss += loss_fn(pred, y).item()correct += (pred.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchescorrect /= sizeprint(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

訓練模型

epochs = 10.
for t in range(epochs):print(f"Epoch {t+1}\n-------------------------------")train(train_dataloader, model, loss_fn, optimizer)test(test_dataloader, model, loss_fn)
print("Done!")

Epoch 10
loss: 0.015569 [ 64/60000]
loss: 0.029817 [ 6464/60000]
loss: 0.043169 [12864/60000]
loss: 0.027709 [19264/60000]
loss: 0.021492 [25664/60000]
loss: 0.011533 [32064/60000]
loss: 0.045418 [38464/60000]
loss: 0.042875 [44864/60000]
loss: 0.152001 [51264/60000]
loss: 0.040214 [57664/60000]
Test Error:
Accuracy: 98.6%, Avg loss: 0.044844

模型處理

保存模型

model_name = 'LeNet'
model_file = model_name + ".pth"
torch.save(model.state_dict(), model_file)
print("Saved PyTorch Model State to " + model_file)

Saved PyTorch Model State to LeNet.pth

Summary

---

安裝torchsummary

pip install torchsummary

調用summary

from torchsummary import summarymodel = LeNet(10).to(device)
summary(model, (1, 28, 28))

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1            [-1, 6, 28, 28]             156Sigmoid-2            [-1, 6, 28, 28]               0MaxPool2d-3            [-1, 6, 14, 14]               0Conv2d-4           [-1, 16, 10, 10]           2,416Sigmoid-5           [-1, 16, 10, 10]               0MaxPool2d-6             [-1, 16, 5, 5]               0Linear-7                  [-1, 120]          48,120Sigmoid-8                  [-1, 120]               0Linear-9                   [-1, 84]          10,164Sigmoid-10                   [-1, 84]               0Linear-11                   [-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): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
----------------------------------------------------------------