.. _sphx_glr_beginner_blitz_neural_networks_tutorial.py: Neural Networks =============== 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 classfies digit images: .. figure:: /_static/img/mnist.png :alt: convnet convnet It is a simple feed-forward 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: .. code-block:: python import torch from torch.autograd import Variable 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, 5x5 square convolution # kernel self.conv1 = nn.Conv2d(1, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) # an affine operation: y = Wx + b self.fc1 = nn.Linear(16 * 5 * 5, 120) 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) 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()`` .. code-block:: python params = list(net.parameters()) print(len(params)) print(params[0].size()) # conv1's .weight The input to the forward is an ``autograd.Variable``, and so is the output. .. code-block:: python input = Variable(torch.randn(1, 1, 32, 32)) out = net(input) print(out) Zero the gradient buffers of all parameters and backprops with random gradients: .. code-block:: python net.zero_grad() out.backward(torch.randn(1, 10)) .. note:: ``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. Before proceeding further, let's recap all the classes you’ve seen so far. **Recap:** - ``torch.Tensor`` - A *multi-dimensional array*. - ``autograd.Variable`` - *Wraps a Tensor and records the history of operations* applied to it. Has the same API as a ``Tensor``, with some additions like ``backward()``. 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 Variable, that is *automatically registered as a parameter when assigned as an attribute to a* ``Module``. - ``autograd.Function`` - Implements *forward and backward definitions of an autograd operation*. Every ``Variable`` operation, creates at least a single ``Function`` node, that connects to functions that created a ``Variable`` 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 mean-squared error between the input and the target. For example: .. code-block:: python output = net(input) target = Variable(torch.arange(1, 11)) # a dummy target, for example criterion = nn.MSELoss() loss = criterion(output, target) print(loss) Now, if you follow ``loss`` in the backward direction, using it’s ``.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 Variables in the graph will have their ``.grad`` Variable accumulated with the gradient. For illustration, let us follow a few steps backward: .. code-block:: python 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 Backprop -------- To backpropogate 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. .. code-block:: python 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) 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: .. code:: python 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, Nesterov-SGD, Adam, RMSProp, etc. To enable this, we built a small package: ``torch.optim`` that implements all these methods. Using it is very simple: .. code-block:: python 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 **Total running time of the script:** ( 0 minutes 0.000 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: neural_networks_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: neural_networks_tutorial.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_