花店网站建设论文,河南网站建设途径有什么,wordpress dux 主题,wordpress入门使用教程Torch安装的方法 学习方法
1.边用边学#xff0c;torch只是一个工具#xff0c;真正用#xff0c;查的过程才是学习的过程2.直接就上案例就行#xff0c;先来跑#xff0c;遇到什么来解决什么
Mnist分类任务#xff1a; 网络基本构建与训练方法#xff0c;常用函数解析…Torch安装的方法 学习方法
1.边用边学torch只是一个工具真正用查的过程才是学习的过程2.直接就上案例就行先来跑遇到什么来解决什么
Mnist分类任务 网络基本构建与训练方法常用函数解析 torch.nn.functional模块 nn.Module模块
读取Mnist数据集
会自动进行下载
# 查看自己的torch的版本
import torch
print(torch.__version__)%matplotlib inline# 前两步不用管是在网上下载数据后续的我们都是在本地的数据进行操作
from pathlib import Path
import requestsDATA_PATH Path(data)
PATH DATA_PATH / mnistPATH.mkdir(parentsTrue, exist_okTrue)URL http://deeplearning.net/data/mnist/
FILENAME mnist.pkl.gzif not (PATH / FILENAME).exists():content requests.get(URL FILENAME).content(PATH / FILENAME).open(wb).write(content)import pickle
import gzipwith gzip.open((PATH / FILENAME).as_posix(), rb) as f:((x_train, y_train), (x_valid, y_valid), _) pickle.load(f, encodinglatin-1)784是mnist数据集每个样本的像素点个数
from matplotlib import pyplot
import numpy as nppyplot.imshow(x_train[0].reshape((28, 28)), cmapgray)
print(x_train.shape)全连接神经网络的结构 注意数据需转换成tensor才能参与后续建模训练
import torchx_train, y_train, x_valid, y_valid map(torch.tensor, (x_train, y_train, x_valid, y_valid)
)
n, c x_train.shape
x_train, x_train.shape, y_train.min(), y_train.max()
print(x_train, y_train)
print(x_train.shape)
print(y_train.min(), y_train.max())torch.nn.functional 很多层和函数在这里都会见到
torch.nn.functional中有很多功能后续会常用的。那什么时候使用nn.Module什么时候使用nn.functional呢一般情况下如果模型有可学习的参数最好用nn.Module其他情况nn.functional相对更简单一些
import torch.nn.functional as Floss_func F.cross_entropydef model(xb):return xb.mm(weights) biasbs 64
xb x_train[0:bs] # a mini-batch from x
yb y_train[0:bs]
weights torch.randn([784, 10], dtype torch.float, requires_grad True)
bs 64
bias torch.zeros(10, requires_gradTrue)print(loss_func(model(xb), yb))创建一个model来更简化代码
必须继承nn.Module且在其构造函数中需调用nn.Module的构造函数无需写反向传播函数nn.Module能够利用autograd自动实现反向传播Module中的可学习参数可以通过named_parameters()或者parameters()返回迭代器
from torch import nnclass Mnist_NN(nn.Module):# 构造函数def __init__(self):super().__init__()self.hidden1 nn.Linear(784, 128)self.hidden2 nn.Linear(128, 256)self.out nn.Linear(256, 10)self.dropout nn.Dropout(0.5)#前向传播自己定义反向传播是自动进行的def forward(self, x):x F.relu(self.hidden1(x))x self.dropout(x)x F.relu(self.hidden2(x))x self.dropout(x)#x F.relu(self.hidden3(x))x self.out(x)return x net Mnist_NN()
print(net)可以打印我们定义好名字里的权重和偏置项
for name,parameter in net.named_parameters():print(name, parameter,parameter.size())使用TensorDataset和DataLoader来简化
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoadertrain_ds TensorDataset(x_train, y_train)
train_dl DataLoader(train_ds, batch_sizebs, shuffleTrue)valid_ds TensorDataset(x_valid, y_valid)
valid_dl DataLoader(valid_ds, batch_sizebs * 2)def get_data(train_ds, valid_ds, bs):return (DataLoader(train_ds, batch_sizebs, shuffleTrue),DataLoader(valid_ds, batch_sizebs * 2),)一般在训练模型时加上model.train()这样会正常使用Batch Normalization和 Dropout测试的时候一般选择model.eval()这样就不会使用Batch Normalization和 Dropout
import numpy as npdef fit(steps, model, loss_func, opt, train_dl, valid_dl):for step in range(steps):model.train() # 训练的时候需要更新权重参数for xb, yb in train_dl:loss_batch(model, loss_func, xb, yb, opt)model.eval() # 验证的时候不需要更新权重参数with torch.no_grad():losses, nums zip(*[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl])val_loss np.sum(np.multiply(losses, nums)) / np.sum(nums)print(当前step:str(step), 验证集损失str(val_loss))zip的用法
a [1,2,3]
b [4,5,6]
zipped zip(a,b)
print(list(zipped))
a2,b2 zip(*zip(a,b))
print(a2)
print(b2)from torch import optim
def get_model():model Mnist_NN()return model, optim.SGD(model.parameters(), lr0.001)def loss_batch(model, loss_func, xb, yb, optNone):loss loss_func(model(xb), yb)if opt is not None:loss.backward()opt.step()opt.zero_grad()return loss.item(), len(xb)三行搞定
train_dl,valid_dl get_data(train_ds, valid_ds, bs)
model, opt get_model()
fit(100, model, loss_func, opt, train_dl, valid_dl)correct 0
total 0
for xb,yb in valid_dl:outputs model(xb)_,predicted torch.max(outputs.data,1)total yb.size(0)correct (predicted yb).sum().item()
print(fAccuracy of the network the 10000 test imgaes {100*correct/total})