内容包含:
多类图像分类数据集Fashion-MNIST
torchvision包,用来构建计算机视觉模型,主要由以下几部分构成:
# import needed package
%matplotlib inline
from IPython import display
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
import time
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
print(torchvision.__version__)
输出
1.3.0
0.4.1a0+d94043a
## get dataset
mnist_train = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=False, download=True, transform=transforms.ToTensor())
# show result
print(type(mnist_train))
print(len(mnist_train), len(mnist_test))
输出
60000 10000
如果不做变换输入的数据是图像,我们可以看一下图片的类型参数:
mnist_PIL = torchvision.datasets.FashionMNIST(root='/home/kesci/input/FashionMNIST2065', train=True, download=True)
PIL_feature, label = mnist_PIL[0]
print(PIL_feature)
输出
# 本函数已保存在d2lzh包中方便以后使用
def get_fashion_mnist_labels(labels):
text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
return [text_labels[int(i)] for i in labels]
def show_fashion_mnist(images, labels):
d2l.use_svg_display()
# 这里的_表示我们忽略(不使用)的变量
_, figs = plt.subplots(1, len(images), figsize=(12, 12))
for f, img, lbl in zip(figs, images, labels):
f.imshow(img.view((28, 28)).numpy())
f.set_title(lbl)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
plt.show()
X, y = [], []
for i in range(10):
X.append(mnist_train[i][0]) # 将第i个feature加到X中
y.append(mnist_train[i][1]) # 将第i个label加到y中
show_fashion_mnist(X, get_fashion_mnist_labels(y))
# 读取数据
batch_size = 256
num_workers = 4
train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)
import torch
import torchvision
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
print(torchvision.__version__)
#获取训练集数据和测试集数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, root='/home/kesci/input/FashionMNIST2065')
#模型参数初始化
num_inputs = 784
print(28*28)
num_outputs = 10
W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)
b = torch.zeros(num_outputs, dtype=torch.float)
W.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True)
# 对多维Tensor按维度操作
X = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(X.sum(dim=0, keepdim=True)) # dim为0,按照相同的列求和,并在结果中保留列特征
print(X.sum(dim=1, keepdim=True)) # dim为1,按照相同的行求和,并在结果中保留行特征
print(X.sum(dim=0, keepdim=False)) # dim为0,按照相同的列求和,不在结果中保留列特征
print(X.sum(dim=1, keepdim=False)) # dim为1,按照相同的行求和,不在结果中保留行特征
tensor([[5, 7, 9]])
tensor([[ 6],
[15]])
tensor([5, 7, 9])
tensor([ 6, 15])
y ^ j = exp ( o j ) ∑ i = 1 3 exp ( o i ) \hat{y}_j = \frac{ \exp(o_j)}{\sum_{i=1}^3 \exp(o_i)} y^j=∑i=13exp(oi)exp(oj)
def softmax(X):
X_exp = X.exp()
partition = X_exp.sum(dim=1, keepdim=True)
# print("X size is ", X_exp.size())
# print("partition size is ", partition, partition.size())
return X_exp / partition # 这里应用了广播机制
X = torch.rand((2, 5))
X_prob = softmax(X)
print(X_prob, '\n', X_prob.sum(dim=1))
tensor([[0.2441, 0.2439, 0.1263, 0.1885, 0.1972],
[0.2408, 0.1987, 0.1400, 0.1199, 0.3006]])
tensor([1.0000, 1.0000])
o ( i ) = x ( i ) W + b , y ^ ( i ) = softmax ( o ( i ) ) . \begin{aligned} \boldsymbol{o}^{(i)} &= \boldsymbol{x}^{(i)} \boldsymbol{W} + \boldsymbol{b},\\ \boldsymbol{\hat{y}}^{(i)} &= \text{softmax}(\boldsymbol{o}^{(i)}). \end{aligned} o(i)y^(i)=x(i)W+b,=softmax(o(i)).
def net(X):
return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)
H ( y ( i ) , y ^ ( i ) ) = − ∑ j = 1 q y j ( i ) log y ^ j ( i ) , H\left(\boldsymbol y^{(i)}, \boldsymbol {\hat y}^{(i)}\right ) = -\sum_{j=1}^q y_j^{(i)} \log \hat y_j^{(i)}, H(y(i),y^(i))=−j=1∑qyj(i)logy^j(i),
ℓ ( Θ ) = 1 n ∑ i = 1 n H ( y ( i ) , y ^ ( i ) ) , \ell(\boldsymbol{\Theta}) = \frac{1}{n} \sum_{i=1}^n H\left(\boldsymbol y^{(i)}, \boldsymbol {\hat y}^{(i)}\right ), ℓ(Θ)=n1i=1∑nH(y(i),y^(i)),
ℓ ( Θ ) = − ( 1 / n ) ∑ i = 1 n log y ^ y ( i ) ( i ) \ell(\boldsymbol{\Theta}) = -(1/n) \sum_{i=1}^n \log \hat y_{y^{(i)}}^{(i)} ℓ(Θ)=−(1/n)i=1∑nlogy^y(i)(i)
y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])
y = torch.LongTensor([0, 2])
y_hat.gather(1, y.view(-1, 1))
tensor([[0.1000],
[0.5000]])
def cross_entropy(y_hat, y):
return - torch.log(y_hat.gather(1, y.view(-1, 1)))
def accuracy(y_hat, y):
return (y_hat.argmax(dim=1) == y).float().mean().item()
print(accuracy(y_hat, y))
0.5
# 本函数已保存在d2lzh_pytorch包中方便以后使用。该函数将被逐步改进:它的完整实现将在“图像增广”一节中描述
def evaluate_accuracy(data_iter, net):
acc_sum, n = 0.0, 0
for X, y in data_iter:
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
n += y.shape[0]
return acc_sum / n
print(evaluate_accuracy(test_iter, net))
0.0745
num_epochs, lr = 5, 0.1
# 本函数已保存在d2lzh_pytorch包中方便以后使用
def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
params=None, lr=None, optimizer=None):
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat, y).sum()
# 梯度清零
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()
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)
epoch 1, loss 0.7870, train acc 0.746, test acc 0.792
epoch 2, loss 0.5707, train acc 0.813, test acc 0.814
epoch 3, loss 0.5242, train acc 0.826, test acc 0.819
epoch 4, loss 0.5017, train acc 0.832, test acc 0.825
epoch 5, loss 0.4848, train acc 0.837, test acc 0.828
模型训练完,进行预测。
给定一系列图像(第三行图像输出),比较它们的真实标签(第一行文本输出)和模型预测结果(第二行文本输出)。
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])
# 加载各种包或者模块
import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
sys.path.append("/home/kesci/input")
import d2lzh1981 as d2l
print(torch.__version__)
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, root='/home/kesci/input/FashionMNIST2065')
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 的形状: (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, self).__init__()
def forward(self, x): # x 的形状: (batch, *, *, ...)
return x.view(x.shape[0], -1)
from collections import OrderedDict
net = nn.Sequential(
# FlattenLayer(),
# LinearNet(num_inputs, num_outputs)
OrderedDict([
('flatten', FlattenLayer()),
('linear', nn.Linear(num_inputs, num_outputs))]) # 或者写成我们自己定义的 LinearNet(num_inputs, num_outputs) 也可以
)
init.normal_(net.linear.weight, mean=0, std=0.01)
init.constant_(net.linear.bias, val=0)
Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)
loss = nn.CrossEntropyLoss() # 下面是他的函数原型
# class torch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean')
optimizer = torch.optim.SGD(net.parameters(), lr=0.1) # 下面是函数原型
# class torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
epoch 1, loss 0.0031, train acc 0.750, test acc 0.746
epoch 2, loss 0.0022, train acc 0.813, test acc 0.810
epoch 3, loss 0.0021, train acc 0.825, test acc 0.814
epoch 4, loss 0.0020, train acc 0.832, test acc 0.820
epoch 5, loss 0.0019, train acc 0.836, test acc 0.804