.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "beginner/basics/buildmodel_tutorial.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_beginner_basics_buildmodel_tutorial.py: `Learn the Basics `_ || `Quickstart `_ || `Tensors `_ || `Datasets & DataLoaders `_ || `Transforms `_ || **Build Model** || `Autograd `_ || `Optimization `_ || `Save & Load Model `_ Build the Neural Network =================== Neural networks comprise of layers/modules that perform operations on data. The `torch.nn `_ namespace provides all the building blocks you need to build your own neural network. Every module in PyTorch subclasses the `nn.Module `_. 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. .. GENERATED FROM PYTHON SOURCE LINES 24-32 .. code-block:: default import os import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets, transforms .. GENERATED FROM PYTHON SOURCE LINES 33-38 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 `_ or `torch.backends.mps `_ are available, otherwise we use the CPU. .. GENERATED FROM PYTHON SOURCE LINES 38-48 .. 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") .. GENERATED FROM PYTHON SOURCE LINES 49-54 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. .. GENERATED FROM PYTHON SOURCE LINES 54-72 .. 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 .. GENERATED FROM PYTHON SOURCE LINES 73-75 We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print its structure. .. GENERATED FROM PYTHON SOURCE LINES 75-80 .. code-block:: default model = NeuralNetwork().to(device) print(model) .. GENERATED FROM PYTHON SOURCE LINES 81-87 To use the model, we pass it the input data. This executes the model's ``forward``, along with some `background operations `_. 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. .. GENERATED FROM PYTHON SOURCE LINES 87-95 .. 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}") .. GENERATED FROM PYTHON SOURCE LINES 96-98 -------------- .. GENERATED FROM PYTHON SOURCE LINES 101-107 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. .. GENERATED FROM PYTHON SOURCE LINES 107-111 .. code-block:: default input_image = torch.rand(3,28,28) print(input_image.size()) .. GENERATED FROM PYTHON SOURCE LINES 112-117 nn.Flatten ^^^^^^^^^^^^^^^^^^^^^^ We initialize the `nn.Flatten `_ layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values ( the minibatch dimension (at dim=0) is maintained). .. GENERATED FROM PYTHON SOURCE LINES 117-122 .. code-block:: default flatten = nn.Flatten() flat_image = flatten(input_image) print(flat_image.size()) .. GENERATED FROM PYTHON SOURCE LINES 123-128 nn.Linear ^^^^^^^^^^^^^^^^^^^^^^ The `linear layer `_ is a module that applies a linear transformation on the input using its stored weights and biases. .. GENERATED FROM PYTHON SOURCE LINES 128-133 .. code-block:: default layer1 = nn.Linear(in_features=28*28, out_features=20) hidden1 = layer1(flat_image) print(hidden1.size()) .. GENERATED FROM PYTHON SOURCE LINES 134-142 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 `_ between our linear layers, but there's other activations to introduce non-linearity in your model. .. GENERATED FROM PYTHON SOURCE LINES 142-149 .. code-block:: default print(f"Before ReLU: {hidden1}\n\n") hidden1 = nn.ReLU()(hidden1) print(f"After ReLU: {hidden1}") .. GENERATED FROM PYTHON SOURCE LINES 150-155 nn.Sequential ^^^^^^^^^^^^^^^^^^^^^^ `nn.Sequential `_ 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``. .. GENERATED FROM PYTHON SOURCE LINES 155-165 .. 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) .. GENERATED FROM PYTHON SOURCE LINES 166-172 nn.Softmax ^^^^^^^^^^^^^^^^^^^^^^ The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the `nn.Softmax `_ 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. .. GENERATED FROM PYTHON SOURCE LINES 172-177 .. code-block:: default softmax = nn.Softmax(dim=1) pred_probab = softmax(logits) .. GENERATED FROM PYTHON SOURCE LINES 178-187 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. .. GENERATED FROM PYTHON SOURCE LINES 187-194 .. 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") .. GENERATED FROM PYTHON SOURCE LINES 195-197 -------------- .. GENERATED FROM PYTHON SOURCE LINES 199-202 Further Reading -------------- - `torch.nn API `_ .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_beginner_basics_buildmodel_tutorial.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: buildmodel_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: buildmodel_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_