<jupyter_start><jupyter_text>Training a Classifier  What about data?Generally, when you have to deal with image, text, audio or video data, you can use standard python packages that load data into a numpy array. Then you can convert this array into a torch.*Tensor.* For images, packages such as Pillow, OpenCV are useful* For audio, packages such as scipy and librosa* For text, either raw Python or Cython based loading, or NLTK and SpaCy are usefulSpecifically for vision, we have created a package called `torchvision`, that has data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz., `torchvision.datasets` and `torch.utils.data.DataLoader`.This provides a huge convenience and avoids writing boilerplate code.For this tutorial, we will use the CIFAR10 dataset. It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.  Training an image classifierWe will do the following steps in order:1. Load and normalizing the CIFAR10 training and test datasets using torchvision2. Define a Convolutional Neural Network3. Define a loss function4. Train the network on the training data5. Test the network on the test data  1. Loading and normalizing CIFAR10Using `torchvision`, it’s extremely easy to load CIFAR10.<jupyter_code>import torch
import torchvision
import torchvision.transforms as transforms<jupyter_output><empty_output><jupyter_text>TORCHVISION.TRANSFORMSTransforms are common image transformations. They can be chained together using `transforms.Compose`. Additionally, there is the `torchvision.transforms.functional` module. Functional transforms give fine-grained control over the transformations. This is useful if you have to build a more complex transformation pipeline (e.g. in the case of segmentation tasks).`transforms.Compose` just clubs all the transforms provided to it. So, all the transforms in the `transforms.Compose` are applied to the input one by one.* Refer this [link](https://stackoverflow.com/questions/50002543/what-are-transforms-in-pytorch-used-for) for better understanding of `transforms`.* Read more about `transforms` in its [offical documentation](https://pytorch.org/docs/stable/torchvision/transforms.html).* Refer this [link](https://discuss.pytorch.org/t/understanding-transform-normalize/21730) if you want more insights about `transforms.Normalize`  TORCHVISION.DATASETSAll datasets are subclasses of `torch.utils.data.Dataset` i.e, they have `__getitem__` and `__len__` methods implemented. Hence, they can all be passed to a `torch.utils.data.DataLoader` which can load multiple samples parallelly using `torch.multiprocessing` workers. For example:`imagenet_data = torchvision.datasets.ImageNet('path/to/imagenet_root/')``data_loader = torch.utils.data.DataLoader(imagenet_data,                                          batch_size=4,                                          shuffle=True,                                          num_workers=args.nThreads)`      Refer this [link](https://pytorch.org/docs/stable/torchvision/datasets.html) for more information on `torchvision.datasets`  TORCH.UTILS.DATAAt the heart of PyTorch data loading utility is the `torch.utils.data.DataLoader` class. It represents a Python iterable over a dataset, with support for* map-style and iterable-style datasets,* customizing data loading order,* automatic batching,* single- and multi-process data loading,* automatic memory pinning.These options are configured by the constructor arguments of a `DataLoader`, which has signature:`DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,           batch_sampler=None, num_workers=0, collate_fn=None,           pin_memory=False, drop_last=False, timeout=0,           worker_init_fn=None)`Refer this [link](https://pytorch.org/docs/stable/data.html) for more information on `dataloaders`. The output of torchvision datasets are PILImage images of range [0, 1]. We transform them to Tensors of normalized range [-1, 1].<jupyter_code>transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')<jupyter_output>Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz<jupyter_text>Let us show some of the training images, for fun.<jupyter_code>import matplotlib.pyplot as plt
import numpy as np

# functions to show an image

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))<jupyter_output><empty_output><jupyter_text>2. Define a Convolutional Neural NetworkCopy the neural network from the Neural Networks section before and modify it to take 3-channel images (instead of 1-channel images as it was defined).<jupyter_code>import torch.nn as nn
import torch.nn.functional as F

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 * 5 * 5, 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(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()<jupyter_output><empty_output><jupyter_text>3. Define a Loss function and optimizerLet’s use a Classification Cross-Entropy loss and SGD with momentum.<jupyter_code>import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)<jupyter_output><empty_output><jupyter_text>4. Train the networkThis is when things start to get interesting. We simply have to loop over our data iterator, and feed the inputs to the network and optimize.<jupyter_code>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 % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')<jupyter_output>[1,  2000] loss: 2.178
[1,  4000] loss: 1.843
[1,  6000] loss: 1.674
[1,  8000] loss: 1.567
[1, 10000] loss: 1.515
[1, 12000] loss: 1.469
[2,  2000] loss: 1.400
[2,  4000] loss: 1.386
[2,  6000] loss: 1.366
[2,  8000] loss: 1.312
[2, 10000] loss: 1.296
[2, 12000] loss: 1.286
Finished Training<jupyter_text>Let’s quickly save our trained model:<jupyter_code>PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)<jupyter_output><empty_output><jupyter_text>See [here](https://pytorch.org/docs/stable/notes/serialization.html) for more details on saving PyTorch models.  5. Test the network on the test dataWe have trained the network for 2 passes over the training dataset. But we need to check if the network has learnt anything at all.We will check this by predicting the class label that the neural network outputs, and checking it against the ground-truth. If the prediction is correct, we add the sample to the list of correct predictions.Okay, first step. Let us display an image from the test set to get familiar.<jupyter_code>dataiter = iter(testloader)
images, labels = dataiter.next()

# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))<jupyter_output><empty_output><jupyter_text>Next, let’s load back in our saved model (note: saving and re-loading the model wasn’t necessary here, we only did it to illustrate how to do so):<jupyter_code>net = Net()
net.load_state_dict(torch.load(PATH))<jupyter_output><empty_output><jupyter_text>Okay, now let us see what the neural network thinks these examples above are:<jupyter_code>outputs = net(images)<jupyter_output><empty_output><jupyter_text>The outputs are energies for the 10 classes. The higher the energy for a class, the more the network thinks that the image is of the particular class. So, let’s get the index of the highest energy:<jupyter_code>_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))<jupyter_output>Predicted:    cat   car  ship plane<jupyter_text>The results seem pretty good.Let us look at how the network performs on the whole dataset.<jupyter_code>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 10000 test images: %d %%' % (
    100 * correct / total))<jupyter_output>Accuracy of the network on the 10000 test images: 53 %<jupyter_text>That looks way better than chance, which is 10% accuracy (randomly picking a class out of 10 classes). Seems like the network learnt something.Hmmm, what are the classes that performed well, and the classes that did not perform well:<jupyter_code>class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))<jupyter_output>Accuracy of plane : 61 %
Accuracy of   car : 69 %
Accuracy of  bird : 44 %
Accuracy of   cat : 27 %
Accuracy of  deer : 61 %
Accuracy of   dog : 36 %
Accuracy of  frog : 50 %
Accuracy of horse : 67 %
Accuracy of  ship : 64 %
Accuracy of truck : 49 %<jupyter_text>Okay, so what next?How do we run these neural networks on the GPU?  Training on GPUJust like how you transfer a Tensor onto the GPU, you transfer the neural net onto the GPU.Let’s first define our device as the first visible cuda device if we have CUDA available:<jupyter_code>device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)<jupyter_output>cuda:0<jupyter_text>The rest of this section assumes that `device` is a CUDA device.Then these methods will recursively go over all modules and convert their parameters and buffers to CUDA tensors:<jupyter_code>net.to(device)<jupyter_output><empty_output><jupyter_text>Remember that you will have to send the inputs and targets at every step to the GPU too:<jupyter_code>inputs, labels = data[0].to(device), data[1].to(device)<jupyter_output><empty_output>
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