Simple ConvNet

Train a simple Convnet

Neural networks can be constructed using the torch.nn package

An nn.Module contains layers, and a method forward(input)that returns the output.

A typical training procedure for a neural network is as follows:

• Define the neural network that has some learnable parameters (or weights)

• Iterate over a dataset of inputs

• Process input through the network

• Compute the loss (how far is the output from being correct)

• Propagate gradients back into the network’s parameters

• Update the weights of the network, typically using a simple update rule: weight = weight - learning_rate * gradient

convnet

input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d -> view -> linear -> relu -> linear -> relu -> linear -> MSELoss -> loss

Define the network

Let’s define this network:

tensor.view(-1,n), Returns a new tensor with the same data as the self tensor but of a different shape. the size -1 is inferred from other dimensions

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # input ch, output ch, convolution
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension 
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)

Inputs

torch.nn only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, nn.Conv2d will take in a 4D Tensor of nSamples x nChannels x Height x Width.

If you have a single sample, just use input.unsqueeze(0) to add a fake batch dimension.

# Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. 
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)

# Zero the gradient buffers of all parameters and backprops with random gradients
net.zero_grad()
out.backward(torch.randn(1, 10))

Datasets (MNIST)

torchvision.datasets.MNIST(root, train=True, transform=None, target_transform=None, download=False)

• root (string) – Root directory of dataset where MNIST/processed/training.pt and MNIST/processed/test.pt exist.

#  a batch_size of 64, size 1000 for testing
#  mean 0.1307, std 0.3081 used for the Normalize() 
batch_size_train=64
batch_size_test=64

# transform = transforms.Compose(
#     [transforms.ToTensor(),
#      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])   
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.1307,), (0.3081,))])  

# Train set    
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size_train,
                                          shuffle=True, num_workers=2)

# Test set
testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size_test,
                                         shuffle=True, num_workers=2)

Loss Function

A loss function takes the (output, target) pair of inputs. There are several different loss functions https://pytorch.org/docs/nn.html#loss-functions``

output = net(input)
target = torch.randn(10)  # a dummy target, for example
target = target.view(1, -1)  # make it the same shape as output
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)

Update the weights(Optimization)

various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.

import torch.optim as optim

# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)

# in your training loop:
optimizer.zero_grad()   # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step()    # Does the update