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.

"""

import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms


#############################################
# Get Device for Training
# -----------------------
# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.

device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")

##############################################
# 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.

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

##############################################
# We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print
# its structure.

model = NeuralNetwork().to(device)
print(model)


##############################################
# 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.

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}")


######################################################################
# --------------
#


##############################################
# 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.

input_image = torch.rand(3,28,28)
print(input_image.size())

##################################################
# 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).

flatten = nn.Flatten()
flat_image = flatten(input_image)
print(flat_image.size())

##############################################
# 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.
#
layer1 = nn.Linear(in_features=28*28, out_features=20)
hidden1 = layer1(flat_image)
print(hidden1.size())


#################################################
# 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.

print(f"Before ReLU: {hidden1}\n\n")
hidden1 = nn.ReLU()(hidden1)
print(f"After ReLU: {hidden1}")



#################################################
# 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``.

seq_modules = nn.Sequential(
    flatten,
    layer1,
    nn.ReLU(),
    nn.Linear(20, 10)
)
input_image = torch.rand(3,28,28)
logits = seq_modules(input_image)

################################################################
# 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.

softmax = nn.Softmax(dim=1)
pred_probab = softmax(logits)


#################################################
# 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.
#


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")

######################################################################
# --------------
#

#################################################################
# Further Reading
# -----------------
# - `torch.nn API <https://pytorch.org/docs/stable/nn.html>`_