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.. _sphx_glr_beginner_basics_buildmodel_tutorial.py:
`Learn the Basics <intro.html>`_ ||
`Quickstart <quickstart_tutorial.html>`_ ||
`Tensors <tensorqs_tutorial.html>`_ ||
`Datasets & DataLoaders <data_tutorial.html>`_ ||
`Transforms <transforms_tutorial.html>`_ ||
**Build Model** ||
`Autograd <autogradqs_tutorial.html>`_ ||
`Optimization <optimization_tutorial.html>`_ ||
`Save & Load Model <saveloadrun_tutorial.html>`_
Build the Neural Network
===================
Neural networks comprise of layers/modules that perform operations on data.
The `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ namespace provides all the building blocks you need to
build your own neural network. Every module in PyTorch subclasses the `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_.
A neural network is a module itself that consists of other modules (layers). This nested structure allows for
building and managing complex architectures easily.
In the following sections, we'll build a neural network to classify images in the FashionMNIST dataset.
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.. code-block:: default
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
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Get Device for Training
-----------------------
We want to be able to train our model on a hardware accelerator like the GPU or MPS,
if available. Let's check to see if `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_
or `torch.backends.mps <https://pytorch.org/docs/stable/notes/mps.html>`_ are available, otherwise we use the CPU.
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.. code-block:: default
device = (
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
print(f"Using {device} device")
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Define the Class
-------------------------
We define our neural network by subclassing ``nn.Module``, and
initialize the neural network layers in ``__init__``. Every ``nn.Module`` subclass implements
the operations on input data in the ``forward`` method.
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.. code-block:: default
class NeuralNetwork(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28*28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10),
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
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We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print
its structure.
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.. code-block:: default
model = NeuralNetwork().to(device)
print(model)
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To use the model, we pass it the input data. This executes the model's ``forward``,
along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
Do not call ``model.forward()`` directly!
Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.
We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
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.. code-block:: default
X = torch.rand(1, 28, 28, device=device)
logits = model(X)
pred_probab = nn.Softmax(dim=1)(logits)
y_pred = pred_probab.argmax(1)
print(f"Predicted class: {y_pred}")
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--------------
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Model Layers
-------------------------
Let's break down the layers in the FashionMNIST model. To illustrate it, we
will take a sample minibatch of 3 images of size 28x28 and see what happens to it as
we pass it through the network.
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.. code-block:: default
input_image = torch.rand(3,28,28)
print(input_image.size())
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nn.Flatten
^^^^^^^^^^^^^^^^^^^^^^
We initialize the `nn.Flatten <https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html>`_
layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values (
the minibatch dimension (at dim=0) is maintained).
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.. code-block:: default
flatten = nn.Flatten()
flat_image = flatten(input_image)
print(flat_image.size())
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nn.Linear
^^^^^^^^^^^^^^^^^^^^^^
The `linear layer <https://pytorch.org/docs/stable/generated/torch.nn.Linear.html>`_
is a module that applies a linear transformation on the input using its stored weights and biases.
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.. code-block:: default
layer1 = nn.Linear(in_features=28*28, out_features=20)
hidden1 = layer1(flat_image)
print(hidden1.size())
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nn.ReLU
^^^^^^^^^^^^^^^^^^^^^^
Non-linear activations are what create the complex mappings between the model's inputs and outputs.
They are applied after linear transformations to introduce *nonlinearity*, helping neural networks
learn a wide variety of phenomena.
In this model, we use `nn.ReLU <https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html>`_ between our
linear layers, but there's other activations to introduce non-linearity in your model.
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.. code-block:: default
print(f"Before ReLU: {hidden1}\n\n")
hidden1 = nn.ReLU()(hidden1)
print(f"After ReLU: {hidden1}")
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nn.Sequential
^^^^^^^^^^^^^^^^^^^^^^
`nn.Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ is an ordered
container of modules. The data is passed through all the modules in the same order as defined. You can use
sequential containers to put together a quick network like ``seq_modules``.
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.. code-block:: default
seq_modules = nn.Sequential(
flatten,
layer1,
nn.ReLU(),
nn.Linear(20, 10)
)
input_image = torch.rand(3,28,28)
logits = seq_modules(input_image)
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nn.Softmax
^^^^^^^^^^^^^^^^^^^^^^
The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the
`nn.Softmax <https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html>`_ module. The logits are scaled to values
[0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along
which the values must sum to 1.
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.. code-block:: default
softmax = nn.Softmax(dim=1)
pred_probab = softmax(logits)
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Model Parameters
-------------------------
Many layers inside a neural network are *parameterized*, i.e. have associated weights
and biases that are optimized during training. Subclassing ``nn.Module`` automatically
tracks all fields defined inside your model object, and makes all parameters
accessible using your model's ``parameters()`` or ``named_parameters()`` methods.
In this example, we iterate over each parameter, and print its size and a preview of its values.
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.. code-block:: default
print(f"Model structure: {model}\n\n")
for name, param in model.named_parameters():
print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n")
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Further Reading
--------------
- `torch.nn API <https://pytorch.org/docs/stable/nn.html>`_
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