基本的神经网络前向传播pytorch实现

基本的神经网络前向传播pytorch实现

记录一下自己学习pytorch的过程，这里是自己利用pytorch实现的神经网络。

import torch
from torch import nn
import torchvision
import torch.nn.functional as F
import torchvision.transforms as transforms

#Device configuration
device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')

#Hyper-parameters
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

#MNist dataset
train_dataset = torchvision.datasets.MNIST(root='../../data',
train=False,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='../../data',
train=False,
transform=transforms.ToTensor())
# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
def __init__(self,input_size,hidden_size,num_classes):
super(NeuralNet,self).__init__()
self.fc1 = nn.Linear(input_size,hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size,num_classes)

def forward(self,x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out

model = NeuralNet(input_size,hidden_size,num_classes).to(device)

#loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr = learning_rate)

#train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i,(images,labels) in enumerate(train_loader):
#moves tensors  to the configured device
images = images.reshape(-1,28*28).to(device)
labels = labels.to(device)

#forward pass
outputs = model(images)
loss = criterion(outputs,labels)

#backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()

if(i+1)%100 == 0:
print('Epoch [{}/{}], step [{}/{}],Loss: {:.4f}'.format(epoch+1,num_epochs,i+1,total_step,loss.item()))

#test the model
with torch.no_grad():
correct = 0
total = 0
for images,labels in test_loader:
images = images.reshape(-1,28*28)
labels = labels.to(device)
outputs = model(images)
_,predicted = torch.max(outputs.data,1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))

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