[pytorch、學習] - 3.9 多重感知機的從零開始實現

參考

3.9 多重感知機的從零開始實現

import torch
import numpy as np
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l

3.9.1. 獲取和讀取數據

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

3.9.2. 定義模型參數

num_inputs, num_outputs, num_hiddens  = 784, 10, 256

W1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float)
b1 = torch.zeros(num_hiddens, dtype=torch.float)
W2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float)
b2 = torch.zeros(num_outputs, dtype=torch.float)

params = [W1, b1, W2, b2]
for param in params:
    param.requires_grad_(requires_grad=True)

3.9.3. 定義激活函數

def relu(X):
    return torch.max(input=X, other=torch.tensor(0.0))

3.9.4. 定義模型

def net(X):
    X = X.view((-1, num_inputs))
    H = relu(torch.matmul(X, W1) + b1)
    return torch.matmul(H, W2) + b2

3.9.5. 定義損失函數

loss = torch.nn.CrossEntropyLoss()

3.9.6. 訓練模型

num_epochs, lr = 5, 100.0
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)

3.9.7. 預測

X, y  = iter(test_iter).next()

true_labels = d2l.get_fashion_mnist_labels(y.numpy())
pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())
titles = [true + '\n' + pred for true, pred in zip(true_labels, pred_labels)]

d2l.show_fashion_mnist(X[0:9], titles[0:9])