Learning about the building of neural networks
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IntroductionToPytorch/BuildTheNeuralNetwork.ipynb
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398
IntroductionToPytorch/BuildTheNeuralNetwork.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"from torch import nn\n",
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"from torch.utils.data import DataLoader\n",
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"from torchvision import datasets, transforms"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We want to be able to train our model on a hardware accelerator like the GPU, if it is available. Let’s check to see if torch.cuda is available, else we continue to use the CPU.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using cuda device\n"
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]
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}
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],
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"source": [
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"print(f\"Using {device} device\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NeuralNetwork(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.flatten = nn.Flatten()\n",
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" self.linear_relu_stack = nn.Sequential(\n",
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" nn.Linear(28*28, 512),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512,512),\n",
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" nn.ReLU(),\n",
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" nn.Linear(512,10)\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.flatten(x)\n",
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" logits = self.linear_relu_stack(x)\n",
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" return logits"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"NeuralNetwork(\n",
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" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
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" (linear_relu_stack): Sequential(\n",
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" (0): Linear(in_features=784, out_features=512, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=512, out_features=512, bias=True)\n",
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" (3): ReLU()\n",
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" (4): Linear(in_features=512, out_features=10, bias=True)\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"# Create an instance of NeuralNetwork , and move it to the device \n",
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"model = NeuralNetwork().to(device)\n",
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"# Print its structure\n",
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"print(model)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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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. Do not call model.forward() directly!\n",
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"\n",
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Predicted class: tensor([9], device='cuda:0')\n"
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]
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}
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],
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"source": [
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"X = torch.rand(1,28,28, device=device)\n",
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"logits = model(X)\n",
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"pred_probab = nn.Softmax(dim=1)(logits)\n",
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"y_pred = pred_probab.argmax(1)\n",
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"print(f\"Predicted class: {y_pred}\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([3, 28, 28])\n"
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]
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}
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],
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"source": [
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"input_image = torch.rand(3,28,28)\n",
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"print(input_image.size())"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([3, 784])\n"
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]
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}
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],
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"source": [
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"flatten = nn.Flatten()\n",
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"flat_image = flatten(input_image)\n",
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"print(flat_image.size())"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The linear layer is a module that applies a linear transformation on the input using its stored weights and biases."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([3, 20])\n"
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]
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}
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],
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"source": [
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"layer1 = nn.Linear(in_features=28*28, out_features=20)\n",
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"hidden1 = layer1(flat_image)\n",
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"print(hidden1.size())"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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.\n",
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"\n",
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"In this model, we use nn.ReLU between our linear layers, but there’s other activations to introduce non-linearity in your model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Before ReLU: tensor([[ 0.5346, -0.0702, 0.3048, -0.2614, -0.5357, 0.2483, -0.1851, 0.2514,\n",
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" 0.6062, -0.5254, 0.5784, 0.0366, 0.5520, 0.4593, -0.3674, -0.0193,\n",
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" 0.1552, 0.5918, 0.0862, 0.0201],\n",
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" [ 0.0443, -0.2323, 0.2446, 0.3351, -0.3610, 0.3804, -0.2164, 0.3526,\n",
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" 0.2713, -0.4427, 0.3077, -0.2736, 0.9974, 0.2289, -0.1414, 0.0126,\n",
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" 0.4709, 0.0131, -0.2745, 0.6273],\n",
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" [-0.0822, -0.1703, 0.6460, -0.1720, -0.5338, 0.1044, -0.3253, 0.4759,\n",
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" 0.7618, -0.7128, 0.5533, 0.0803, 0.7058, 0.8018, -0.3034, 0.1789,\n",
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" 0.1912, 0.1924, 0.0492, 0.6753]], grad_fn=<AddmmBackward0>)\n",
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"\n",
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"\n",
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"After ReLU: tensor([[0.5346, 0.0000, 0.3048, 0.0000, 0.0000, 0.2483, 0.0000, 0.2514, 0.6062,\n",
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" 0.0000, 0.5784, 0.0366, 0.5520, 0.4593, 0.0000, 0.0000, 0.1552, 0.5918,\n",
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" 0.0862, 0.0201],\n",
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" [0.0443, 0.0000, 0.2446, 0.3351, 0.0000, 0.3804, 0.0000, 0.3526, 0.2713,\n",
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" 0.0000, 0.3077, 0.0000, 0.9974, 0.2289, 0.0000, 0.0126, 0.4709, 0.0131,\n",
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" 0.0000, 0.6273],\n",
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" [0.0000, 0.0000, 0.6460, 0.0000, 0.0000, 0.1044, 0.0000, 0.4759, 0.7618,\n",
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" 0.0000, 0.5533, 0.0803, 0.7058, 0.8018, 0.0000, 0.1789, 0.1912, 0.1924,\n",
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" 0.0492, 0.6753]], grad_fn=<ReluBackward0>)\n"
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]
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}
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],
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"source": [
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"print(f\"Before ReLU: {hidden1}\\n\\n\")\n",
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"hidden1 = nn.ReLU()(hidden1)\n",
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"print(f\"After ReLU: {hidden1}\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"seq_modules = nn.Sequential(\n",
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" flatten,\n",
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" layer1,\n",
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" nn.ReLU(),\n",
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" nn.Linear(20,10)\n",
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")\n",
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"\n",
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"input_image = torch.rand(3,28,28)\n",
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"logits = seq_modules(input_image)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"softmax = nn.Softmax(dim=1)\n",
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"pred_probab = softmax(logits)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model Structure: NeuralNetwork(\n",
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" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
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" (linear_relu_stack): Sequential(\n",
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" (0): Linear(in_features=784, out_features=512, bias=True)\n",
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" (1): ReLU()\n",
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" (2): Linear(in_features=512, out_features=512, bias=True)\n",
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" (3): ReLU()\n",
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" (4): Linear(in_features=512, out_features=10, bias=True)\n",
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" )\n",
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")\n",
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"\n",
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"\n",
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"Layer: linear_relu_stack.0.weight | Size: torch.Size([512, 784]) | Values: tensor([[ 0.0008, -0.0301, 0.0043, ..., -0.0193, 0.0307, 0.0316],\n",
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" [ 0.0214, -0.0338, 0.0241, ..., 0.0107, -0.0228, -0.0153]],\n",
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" device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n",
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"Layer: linear_relu_stack.0.bias | Size: torch.Size([512]) | Values: tensor([0.0091, 0.0180], device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n",
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"Layer: linear_relu_stack.2.weight | Size: torch.Size([512, 512]) | Values: tensor([[ 0.0204, 0.0072, -0.0056, ..., 0.0131, 0.0127, 0.0308],\n",
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" [ 0.0283, 0.0042, -0.0219, ..., -0.0164, 0.0343, 0.0426]],\n",
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" device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n",
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"Layer: linear_relu_stack.2.bias | Size: torch.Size([512]) | Values: tensor([ 0.0314, -0.0035], device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n",
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"Layer: linear_relu_stack.4.weight | Size: torch.Size([10, 512]) | Values: tensor([[ 0.0233, 0.0182, -0.0222, ..., 0.0058, -0.0052, -0.0185],\n",
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" [-0.0088, -0.0054, -0.0018, ..., -0.0288, 0.0305, 0.0189]],\n",
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" device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n",
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"Layer: linear_relu_stack.4.bias | Size: torch.Size([10]) | Values: tensor([-0.0093, 0.0282], device='cuda:0', grad_fn=<SliceBackward0>)\n",
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"\n"
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]
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}
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],
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"source": [
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"print(f\"Model Structure: {model}\\n\\n\")\n",
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"\n",
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"for name, param in model.named_parameters():\n",
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" print(f\"Layer: {name} | Size: {param.size()} | Values: {param[:2]}\\n\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10 (tags/v3.10.10:aad5f6a, Feb 7 2023, 17:20:36) [MSC v.1929 64 bit (AMD64)]"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "4944a85e4459d92b06dc1c94852b4e8e8e6d0531f16bd543c843a0ca37cdfcdb"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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