PyTorch 04： 用PyTorch實現Fashion-MNIST

好了，我們來實戰演練。你將使用 Fashion-MNIST 數據集。對於神經網絡來說，MNIST 的規模很小，可以輕鬆地達到 97% 的準確率。Fashion-MNIST 包括很多 28x28 灰色服飾圖像。它比 MNIST 更復雜，也更貼近實際現實需求。

你要在這個 notebook 裏構建神經網絡。其實你可以用 Part 3 notebook 裏的代碼，但是這樣就學不到任何知識了。只有自己編寫代碼並確保代碼能運行，才能實現最佳學習效果。當然，你可以參考 Part 3 notebook。

首先，通過 torchvision 加載數據集。

import torch
from torchvision import datasets, transforms
import helper

# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.5,), (0.5,))])
# Download and load the training data
trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# Download and load the test data
testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz

Processing…

Done!

下面是一個圖像示例。

image, label = next(iter(trainloader))
helper.imshow(image[0,:]);

構建網絡

你要先定義網絡。和 MNIST 一樣，每個圖像是 28x28 = 784 像素，並且有 10 個類別。你應該至少包含一個隱藏層。建議對層級使用 ReLU 激活函數，並返回前向傳播的對數或 log-softmax。你可以決定添加多少個層級以及這些層級的大小。

# TODO: Define your network architecture here
from torch import nn, optim
import torch.nn.functional as F

class Classifier_fashion(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(784,256)
        self.layer2 = nn.Linear(256,128)
        self.layer3 = nn.Linear(128,64)
        self.layer4 = nn.Linear(64,10)
    def forward(self, x):
        x = x.view(x.shape[0],-1)
        
        x = F.relu(self.layer1(x))
        x = F.relu(self.layer2(x))
        x = F.relu(self.layer3(x))
        x = F.log_softmax(self.layer4(x), dim=1)
        
        return x

訓練網絡

現在，來試試創建並訓練網絡。首先，你需要定義條件（例如 nn.CrossEntropyLoss）和優化器（通常是 optim.SGD 或 optim.Adam）。

然後編寫訓練代碼。你可以參考這個流程：

*通過網絡進行正向傳遞以獲取logits
*使用 logits 計算損失

• 通過 loss.backward() 在網絡中反向傳播以計算梯度
• 使用優化器更新權重

通過調整參數（隱藏單元、學習速率等），你應該能夠使訓練損失低於 0.4。

# TODO: Create the network, define the criterion and optimizer
model = Classifier_fashion()
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(),lr=0.003)

# TODO: Train the network here
epochs = 5

for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        log_ps = model(images)
        loss = criterion(log_ps, labels)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    print("Trainning loss:{}".format(running_loss/len(trainloader)))

Trainning loss:0.521082957575062
Trainning loss:0.3933480036442976
Trainning loss:0.358206258828579
Trainning loss:0.3316666531219665
Trainning loss:0.31710777687492653

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import helper

# Test out your network!

dataiter = iter(testloader)
images, labels = dataiter.next()
img = images[0]
# Convert 2D image to 1D vector
img = img.resize_(1, 784)

# TODO: Calculate the class probabilities (softmax) for img
ps = torch.exp(model(img))

# Plot the image and probabilities
helper.view_classify(img.resize_(1, 28, 28), ps, version='Fashion')