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Neural networks can be constructed using the torch.nn
package.
Now that you had a glimpse of autograd
, nn
depends on autograd
to define models and differentiate them. An nn.Module
contains layers, and a method forward(input)
that returns the output
.
For example, look at this network that classifies digit images:
convnet
It is a simple feedforward network. It takes the input, feeds it through several layers one after the other, and then finally gives 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
Define the network
Let’s define this network:
import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 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)
Out:
Net( (conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1)) (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1)) (fc1): Linear(in_features=576, out_features=120, bias=True) (fc2): Linear(in_features=120, out_features=84, bias=True) (fc3): Linear(in_features=84, out_features=10, bias=True) )
You just have to define the forward
function, and the backward
function (where gradients are computed) is automatically defined for you using autograd
. You can use any of the Tensor operations in the forward
function.
The learnable parameters of a model are returned by net.parameters()
params = list(net.parameters()) print(len(params)) print(params[0].size()) # conv1's .weight
Out:
10 torch.Size([6, 1, 3, 3])
Let try a random 32×32 input. Note: expected input size of this net (LeNet) is 32×32. To use this net on MNIST dataset, please resize the images from the dataset to 32×32.
input = torch.randn(1, 1, 32, 32) out = net(input) print(out)
Out:
tensor([[ 0.0522, 0.1175, 0.0718, 0.0602, 0.0790, 0.0436, 0.0198, 0.0458, 0.0427, 0.0112]], grad_fn=<AddmmBackward>)
Zero the gradient buffers of all parameters and backprops with random gradients:
net.zero_grad() out.backward(torch.randn(1, 10))
NOTE
torch.nn
only supports minibatches. The entire torch.nn
package only supports inputs that are a minibatch 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.
Before proceeding further, let’s recap all the classes you’ve seen so far.Recap:
torch.Tensor
– A multidimensional array with support for autograd operations likebackward()
. Also holds the gradient w.r.t. the tensor.nn.Module
– Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.nn.Parameter
– A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to aModule
.autograd.Function
– Implements forward and backward definitions of an autograd operation. EveryTensor
operation creates at least a singleFunction
node that connects to functions that created aTensor
and encodes its history.
At this point, we covered:
 Defining a neural network
 Processing inputs and calling backward
Still Left:
 Computing the loss
 Updating the weights of the network
Loss Function
A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.
There are several different loss functions under the nn package . A simple loss is: nn.MSELoss
which computes the meansquared error between the input and the target.
For example:
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)
Out:
tensor(1.8860, grad_fn=<MseLossBackward>)
Now, if you follow loss
in the backward direction, using its .grad_fn
attribute, you will see a graph of computations that looks like this:
input > conv2d > relu > maxpool2d > conv2d > relu > maxpool2d > view > linear > relu > linear > relu > linear > MSELoss > loss
So, when we call loss.backward()
, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True
will have their .grad
Tensor accumulated with the gradient.
For illustration, let us follow a few steps backward:
print(loss.grad_fn) # MSELoss print(loss.grad_fn.next_functions[0][0]) # Linear print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU
Out:
<MseLossBackward object at 0x7f1465107ef0> <AddmmBackward object at 0x7f1465107f98> <AccumulateGrad object at 0x7f1465107f98>
Backprop
To backpropagate the error all we have to do is to loss.backward()
. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.
Now we shall call loss.backward()
, and have a look at conv1’s bias gradients before and after the backward.
net.zero_grad() # zeroes the gradient buffers of all parameters print('conv1.bias.grad before backward') print(net.conv1.bias.grad) loss.backward() print('conv1.bias.grad after backward') print(net.conv1.bias.grad)
Out:
conv1.bias.grad before backward tensor([0., 0., 0., 0., 0., 0.]) conv1.bias.grad after backward tensor([0.0189, 0.0574, 0.0527, 0.0099, 0.0280, 0.0115])
Now, we have seen how to use loss functions.
Read Later:The neural network package contains various modules and loss functions that form the building blocks of deep neural networks. A full list with documentation is here.
The only thing left to learn is:
 Updating the weights of the network
Update the weights
The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):weight = weight  learning_rate * gradient
We can implement this using simple python code:
learning_rate = 0.01 for f in net.parameters(): f.data.sub_(f.grad.data * learning_rate)
However, as you use neural networks, you want to use various different update rules such as SGD, NesterovSGD, Adam, RMSProp, etc. To enable this, we built a small package: torch.optim
that implements all these methods. Using it is very simple:
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
NOTE
Observe how gradient buffers had to be manually set to zero using optimizer.zero_grad()
. This is because gradients are accumulated as explained in Backprop section.
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