mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-20 01:27:06 -05:00
107 lines
3.0 KiB
Python
107 lines
3.0 KiB
Python
import os
|
|
import torchvision
|
|
import torchvision.transforms as transforms
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.optim as optim
|
|
|
|
from azureml.core import Dataset, Run
|
|
import azureml.contrib.dataset
|
|
from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
|
|
|
|
run = Run.get_context()
|
|
|
|
# get input dataset by name
|
|
labeled_dataset = run.input_datasets['crack_labels']
|
|
pytorch_dataset = labeled_dataset.to_torchvision()
|
|
|
|
|
|
indices = torch.randperm(len(pytorch_dataset)).tolist()
|
|
dataset_train = torch.utils.data.Subset(pytorch_dataset, indices[:40])
|
|
dataset_test = torch.utils.data.Subset(pytorch_dataset, indices[-10:])
|
|
|
|
trainloader = torch.utils.data.DataLoader(dataset_train, batch_size=4,
|
|
shuffle=True, num_workers=0)
|
|
|
|
testloader = torch.utils.data.DataLoader(dataset_test, batch_size=4,
|
|
shuffle=True, num_workers=0)
|
|
|
|
|
|
class Net(nn.Module):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.conv1 = nn.Conv2d(3, 6, 5)
|
|
self.pool = nn.MaxPool2d(2, 2)
|
|
self.conv2 = nn.Conv2d(6, 16, 5)
|
|
self.fc1 = nn.Linear(16 * 71 * 71, 120)
|
|
self.fc2 = nn.Linear(120, 84)
|
|
self.fc3 = nn.Linear(84, 10)
|
|
|
|
def forward(self, x):
|
|
x = self.pool(F.relu(self.conv1(x)))
|
|
x = self.pool(F.relu(self.conv2(x)))
|
|
x = x.view(x.size(0), 16 * 71 * 71)
|
|
x = F.relu(self.fc1(x))
|
|
x = F.relu(self.fc2(x))
|
|
x = self.fc3(x)
|
|
return x
|
|
|
|
|
|
net = Net()
|
|
|
|
criterion = nn.CrossEntropyLoss()
|
|
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
|
|
|
|
|
for epoch in range(2): # loop over the dataset multiple times
|
|
|
|
running_loss = 0.0
|
|
for i, data in enumerate(trainloader, 0):
|
|
# get the inputs; data is a list of [inputs, labels]
|
|
inputs, labels = data
|
|
|
|
# zero the parameter gradients
|
|
optimizer.zero_grad()
|
|
|
|
# forward + backward + optimize
|
|
outputs = net(inputs)
|
|
loss = criterion(outputs, labels)
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# print statistics
|
|
running_loss += loss.item()
|
|
if i % 5 == 4: # print every 5 mini-batches
|
|
print('[%d, %5d] loss: %.3f' %
|
|
(epoch + 1, i + 1, running_loss / 5))
|
|
running_loss = 0.0
|
|
|
|
print('Finished Training')
|
|
classes = trainloader.dataset.dataset.labels
|
|
PATH = './cifar_net.pth'
|
|
torch.save(net.state_dict(), PATH)
|
|
|
|
dataiter = iter(testloader)
|
|
images, labels = dataiter.next()
|
|
|
|
net = Net()
|
|
net.load_state_dict(torch.load(PATH))
|
|
|
|
outputs = net(images)
|
|
|
|
_, predicted = torch.max(outputs, 1)
|
|
|
|
correct = 0
|
|
total = 0
|
|
with torch.no_grad():
|
|
for data in testloader:
|
|
images, labels = data
|
|
outputs = net(images)
|
|
_, predicted = torch.max(outputs.data, 1)
|
|
total += labels.size(0)
|
|
correct += (predicted == labels).sum().item()
|
|
|
|
print('Accuracy of the network on the 10 test images: %d %%' % (100 * correct / total))
|
|
pass
|