def main():
# 3.6.1 获取和读取数据
batch_size = 256
train_iter, test_iter = d2l.load_data_from_fashion_mnist(batch_size)
# 3.6.2 初始化模型参数
num_inputs = 784
num_outputs = 10
W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)),
dtype=torch.float32)
b = torch.zeros(num_outputs, dtype=torch.float32)
W.requires_grad_(True)
b.requires_grad_(True)
# 3.6.3 实现 softmax 运算
def softmax(X):
X_exp = X.exp()
partition = X_exp.sum(dim=1, keepdim=True)
return X_exp / partition
# 3.6.4 定义模型
def net(X):
return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)
# 3.6.5 定义损失函数
def cross_entropy(y_hat, y):
return -torch.log(y_hat.gather(1, y.view(-1, 1)))
# 3.6.6 计算分类准确率（已在 d2lzh 包中实现）
def accuracy(y_hat, y):
return (y_hat.argmax(dim=1) == y).float().mean().item()
# 3.6.7 训练模型
num_epochs, lr = 5, 0.1
d2l.train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs,
batch_size, [W, b], lr)
# 3.6.8 预测
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])
def main():
# 3.5.1 获取数据集
mnist_train = torchvision.datasets.FashionMNIST(
root="./data/FashionMNIST",
train=True,
download=True,
transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(
root="./data/FashionMNIST",
train=False,
download=True,
transform=transforms.ToTensor())
feature, label = mnist_train[0]
print(feature.shape, label)
X, y = [], []
for i in range(10):
X.append(mnist_train[i][0])
y.append(mnist_train[i][1])
d2l.show_fashion_mnist(X, d2l.get_fashion_mnist_labels(y))
# 3.5.2 读取小批量
batch_size = 256
train_iter = Data.DataLoader(mnist_train,
batch_size=batch_size,
shuffle=True,
num_workers=0)
test_iter = Data.DataLoader(mnist_test,
batch_size=batch_size,
shuffle=True,
num_workers=0)
# 查看读取一遍数据需要的时间
start = time.time()
for X, y in train_iter:
continue
for X, y in test_iter:
continue
print("%.2f secs" % (time.time() - start))
if optimizer is not None:
optimizer.zero_grad()
elif params is not None and params[0].grad is not None:
for param in params:
param.grad.data.zero_()
l.backward()
if optimizer is None:
d2l.sgd(params, lr, batch_size)
else:
optimizer.step() # “softmax回归的简洁实现”一节将用到
train_l_sum += l.item()
train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
n += y.shape[0]
test_acc = evaluate_accuracy(test_iter, net)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' %
(epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))
train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size,
[W, b], lr)
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])
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import time
import sys
sys.path.append("..") # 为了导⼊上层⽬录的d2lzh_pytorch
import d2lzh_pytorch as d2l
# %%
mnist_train = torchvision.datasets.FashionMNIST(
root='Datasets',
train=True,
download=True,
transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='Datasets',
train=False,
download=True,
transform=transforms.ToTensor())
print(type(mnist_train))
print(len(mnist_train), len(mnist_test))
# %%
feature, label = mnist_train[0]
print(feature.shape, label)
# %%
X, y = [], []
for i in range(10):
X.append(mnist_train[i][0])
y.append(mnist_train[i][1])
d2l.show_fashion_mnist(X, d2l.get_fashion_mnist_labels(y))
# %%
import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
print(torch.__version__)
batch_size = 256
train_iter, test_iter = d2l.get_fashion_mnist_labels(batch_size)
num_inputs = 784
num_outputs = 10
# class LinearNet(nn.Module):
# def __init__(self, num_inputs, num_outputs):
# super(LinearNet, self).__init__()
# self.linear = nn.Linear(num_inputs, num_outputs)
# def forward(self, x): # x shape: (batch, 1, 28, 28)
# y = self.linear(x.view(x.shape[0], -1))
# return y
# net = LinearNet(num_inputs, num_outputs)
class FlattenLayer(nn.Module):
def __init__(self):
super(FlattenLayer).__init__()
