深度神经网络 —— 使用RNN循环神经网络进行手写数字识别分类

代码：

import torch
import torchvision
from torchvision import datasets, transforms
#from torch.autograd import Variable
import matplotlib.pyplot as plttransform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.5],std=[0.5])])dataset_train = datasets.MNIST(root = "./data",transform = transform,train = True,download = True)dataset_test = datasets.MNIST(root = "./data",transform = transform,train = False)train_load = torch.utils.data.DataLoader(dataset = dataset_train,batch_size = 64,shuffle = True)test_load = torch.utils.data.DataLoader(dataset = dataset_test,batch_size = 64,shuffle = True)images, label = next(iter(train_load))
images_example = torchvision.utils.make_grid(images)
images_example = images_example.numpy().transpose(1,2,0)mean = [0.5,0.5,0.5]
std = [0.5,0.5,0.5]
images_example = images_example*std + mean# plt.imshow(images_example)
# plt.show()
plt.imsave("images_example.png", images_example)class RNN(torch.nn.Module):def __init__(self):super(RNN, self).__init__()self.rnn = torch.nn.RNN(input_size = 28,hidden_size = 128,num_layers = 1,batch_first = True)self.output = torch.nn.Linear(128,10)def forward(self, input):output,_ = self.rnn(input, None)output = self.output(output[:,-1,:])return outputmodel = RNN()
optimizer = torch.optim.Adam(model.parameters())
loss_f = torch.nn.CrossEntropyLoss()
epoch_n =10
if torch.cuda.is_available():device = torch.device("cuda:0")
else:device = torch.device("cpu")for epoch in range(epoch_n):running_loss = 0.0running_correct = 0testing_correct = 0print("Epoch {}/{}".format(epoch, epoch_n))print("-"*10)for data in train_load:X_train,y_train = dataX_train = X_train.view(-1,28,28)#X_train,y_train = Variable(X_train),Variable(y_train)X_train,y_train = X_train.to(device),y_train.to(device)y_pred = model(X_train)loss = loss_f(y_pred, y_train)_,pred = torch.max(y_pred.data,1)optimizer.zero_grad()loss.backward()optimizer.step()running_loss +=loss.datarunning_correct += torch.sum(pred == y_train.data)for data in test_load:X_test, y_test = dataX_test = X_test.view(-1,28,28)#X_test, y_test = Variable(X_test), Variable(y_test)X_test,y_test = X_test.to(device),y_test.to(device)outputs = model(X_test)_, pred = torch.max(outputs.data, 1)testing_correct += torch.sum(pred == y_test.data)print("Loss is:{:.4f}, Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}".format(running_loss/len(dataset_train),100*running_correct/len(dataset_train),100*testing_correct/len(dataset_test)))