准备数据

import torch
from torch import nn
from torchkeras import summary,Model import torchvision
from torchvision import transforms

transform = transforms.Compose([transforms.ToTensor()])ds_train = torchvision.datasets.MNIST(root="./data/minist/",train=True,download=True,transform=transform)
ds_valid = torchvision.datasets.MNIST(root="./data/minist/",train=False,download=True,transform=transform)dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)
dl_valid =  torch.utils.data.DataLoader(ds_valid, batch_size=128, shuffle=False, num_workers=4)print(len(ds_train))
print(len(ds_valid))

输出

60000
10000

显示部分数据：

%matplotlib inline
%config InlineBackend.figure_format = 'svg'#查看部分样本
from matplotlib import pyplot as plt plt.figure(figsize=(8,8))
for i in range(9):img,label = ds_train[i]img = torch.squeeze(img)ax=plt.subplot(3,3,i+1)ax.imshow(img.numpy())ax.set_title("label = %d"%label)ax.set_xticks([])ax.set_yticks([])
plt.show()

net = nn.Sequential()
net.add_module("conv1",nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3))
net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))
net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("dropout",nn.Dropout2d(p = 0.1))
net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
net.add_module("flatten",nn.Flatten())
net.add_module("linear1",nn.Linear(64,32))
net.add_module("relu",nn.ReLU())
net.add_module("linear2",nn.Linear(32,10))print(net)

输出

Sequential((conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))(pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))(pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(dropout): Dropout2d(p=0.1, inplace=False)(adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))(flatten): Flatten()(linear1): Linear(in_features=64, out_features=32, bias=True)(relu): ReLU()(linear2): Linear(in_features=32, out_features=10, bias=True)
)

summary(net,input_shape=(1,32,32))

import datetime
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_scoredef accuracy(y_pred,y_true):y_pred_cls = torch.argmax(nn.Softmax(dim=1)(y_pred),dim=1).datareturn accuracy_score(y_true,y_pred_cls)loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=net.parameters(),lr = 0.01)
metric_func = accuracy
metric_name = "accuracy"

epochs = 3
log_step_freq = 100dfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name])
print("Start Training...")
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("=========="*8 + "%s"%nowtime)for epoch in range(1,epochs+1):  # 1，训练循环-------------------------------------------------net.train()loss_sum = 0.0metric_sum = 0.0step = 1for step, (features,labels) in enumerate(dl_train, 1):# 梯度清零optimizer.zero_grad()# 正向传播求损失predictions = net(features)loss = loss_func(predictions,labels)metric = metric_func(predictions,labels)# 反向传播求梯度loss.backward()optimizer.step()# 打印batch级别日志loss_sum += loss.item()metric_sum += metric.item()if step%log_step_freq == 0:   print(("[step = %d] loss: %.3f, "+metric_name+": %.3f") %(step, loss_sum/step, metric_sum/step))# 2，验证循环-------------------------------------------------net.eval()val_loss_sum = 0.0val_metric_sum = 0.0val_step = 1for val_step, (features,labels) in enumerate(dl_valid, 1):with torch.no_grad():predictions = net(features)val_loss = loss_func(predictions,labels)val_metric = metric_func(predictions,labels)val_loss_sum += val_loss.item()val_metric_sum += val_metric.item()# 3，记录日志-------------------------------------------------info = (epoch, loss_sum/step, metric_sum/step, val_loss_sum/val_step, val_metric_sum/val_step)dfhistory.loc[epoch-1] = info# 打印epoch级别日志print(("\nEPOCH = %d, loss = %.3f,"+ metric_name + \"  = %.3f, val_loss = %.3f, "+"val_"+ metric_name+" = %.3f") %info)nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("\n"+"=========="*8 + "%s"%nowtime)print('Finished Training...')

二，函数风格

该风格在脚本形式上作了简单的函数封装。

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.layers = nn.ModuleList([nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,10)])def forward(self,x):for layer in self.layers:x = layer(x)return x
net = Net()
print(net)

summary(net,input_shape=(1,32,32))

import datetime
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_scoredef accuracy(y_pred,y_true):y_pred_cls = torch.argmax(nn.Softmax(dim=1)(y_pred),dim=1).datareturn accuracy_score(y_true,y_pred_cls)model = net
model.optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)
model.loss_func = nn.CrossEntropyLoss()
model.metric_func = accuracy
model.metric_name = "accuracy"

def train_step(model,features,labels):# 训练模式，dropout层发生作用model.train()# 梯度清零model.optimizer.zero_grad()# 正向传播求损失predictions = model(features)loss = model.loss_func(predictions,labels)metric = model.metric_func(predictions,labels)# 反向传播求梯度loss.backward()model.optimizer.step()return loss.item(),metric.item()@torch.no_grad()
def valid_step(model,features,labels):# 预测模式，dropout层不发生作用model.eval()predictions = model(features)loss = model.loss_func(predictions,labels)metric = model.metric_func(predictions,labels)return loss.item(), metric.item()# 测试train_step效果
features,labels = next(iter(dl_train))
train_step(model,features,labels)

def train_model(model,epochs,dl_train,dl_valid,log_step_freq):metric_name = model.metric_namedfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name]) print("Start Training...")nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("=========="*8 + "%s"%nowtime)for epoch in range(1,epochs+1):  # 1，训练循环-------------------------------------------------loss_sum = 0.0metric_sum = 0.0step = 1for step, (features,labels) in enumerate(dl_train, 1):loss,metric = train_step(model,features,labels)# 打印batch级别日志loss_sum += lossmetric_sum += metricif step%log_step_freq == 0:   print(("[step = %d] loss: %.3f, "+metric_name+": %.3f") %(step, loss_sum/step, metric_sum/step))# 2，验证循环-------------------------------------------------val_loss_sum = 0.0val_metric_sum = 0.0val_step = 1for val_step, (features,labels) in enumerate(dl_valid, 1):val_loss,val_metric = valid_step(model,features,labels)val_loss_sum += val_lossval_metric_sum += val_metric# 3，记录日志-------------------------------------------------info = (epoch, loss_sum/step, metric_sum/step, val_loss_sum/val_step, val_metric_sum/val_step)dfhistory.loc[epoch-1] = info# 打印epoch级别日志print(("\nEPOCH = %d, loss = %.3f,"+ metric_name + \"  = %.3f, val_loss = %.3f, "+"val_"+ metric_name+" = %.3f") %info)nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("\n"+"=========="*8 + "%s"%nowtime)print('Finished Training...')return dfhistory

epochs = 3
dfhistory = train_model(model,epochs,dl_train,dl_valid,log_step_freq = 100)

三，类风格

此处使用torchkeras中定义的模型接口构建模型，并调用compile方法和fit方法训练模型。
使用该形式训练模型非常简洁明了。推荐使用该形式。

class CnnModel(nn.Module):def __init__(self):super().__init__()self.layers = nn.ModuleList([nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,10)])def forward(self,x):for layer in self.layers:x = layer(x)return x
model = torchkeras.Model(CnnModel())
print(model)

model.summary(input_shape=(1,32,32))

from sklearn.metrics import accuracy_scoredef accuracy(y_pred,y_true):y_pred_cls = torch.argmax(nn.Softmax(dim=1)(y_pred),dim=1).datareturn accuracy_score(y_true.numpy(),y_pred_cls.numpy())model.compile(loss_func = nn.CrossEntropyLoss(),optimizer= torch.optim.Adam(model.parameters(),lr = 0.02),metrics_dict={"accuracy":accuracy})

dfhistory = model.fit(3,dl_train = dl_train, dl_val=dl_valid, log_step_freq=100)

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准备数据

二，函数风格

三，类风格

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