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Image Classification using Logistic Regression in PyTorch

Part 3 of "PyTorch: Zero to GANs"

This post is the third in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library. Check out the full series:

  1. PyTorch Basics: Tensors & Gradients
  2. Linear Regression & Gradient Descent
  3. Image Classfication using Logistic Regression
  4. Training Deep Neural Networks on a GPU
  5. Image Classification using Convolutional Neural Networks
  6. Data Augmentation, Regularization and ResNets
  7. Generating Images using Generative Adverserial Networks

In this tutorial, we'll use our existing knowledge of PyTorch and linear regression to solve a very different kind of problem: image classification. We'll use the famous MNIST Handwritten Digits Database as our training dataset. It consists of 28px by 28px grayscale images of handwritten digits (0 to 9), along with labels for each image indicating which digit it represents. Here are some sample images from the dataset:

mnist-sample

System setup

This tutorial takes a code-first approach towards learning PyTorch, and you should try to follow along by running and experimenting with the code yourself. The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Kaggle". This will run the notebook on Kaggle, a free online service for running Jupyter notebooks (you might need to create an account).

Running on your computer locally

(Skip this if you're running on Kaggle) To run this notebook locally, clone this notebook, install the required dependencies using conda, and start Jupyter by running the following commands on the terminal / Conda prompt:

pip install jovian --upgrade # Install the jovian library jovian clone aakashns/03-logistic-regression # Download notebook & dependencies cd 03-logistic-regression # Enter the created directory conda create -n 03-logistic-regression python=3.8 # Create an environment conda activate 03-logistic-regression # Activate virtual env jupyter notebook # Start Jupyter

You can find the notebook_id by cliking the Clone button at the top of this page on Jovian. For a more detailed explanation of the above steps, check out the System setup section in the first notebook.

Exploring the Data

We begin by importing torch and torchvision. torchvision contains some utilities for working with image data. It also contains helper classes to automatically download and import popular datasets like MNIST.

# Uncomment and run the commands below if imports fail
!conda install numpy pytorch torchvision cpuonly -c pytorch -y
!pip install matplotlib --upgrade --quiet
Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.8.2 latest version: 4.8.3 Please update conda by running $ conda update -n base conda ## Package Plan ## environment location: /srv/conda/envs/notebook added / updated specs: - cpuonly - numpy - pytorch - torchvision The following packages will be downloaded: package | build ---------------------------|----------------- blas-2.15 | mkl 10 KB conda-forge ca-certificates-2020.4.5.1 | hecc5488_0 146 KB conda-forge certifi-2020.4.5.1 | py37hc8dfbb8_0 151 KB conda-forge cpuonly-1.0 | 0 2 KB pytorch freetype-2.10.2 | he06d7ca_0 905 KB conda-forge intel-openmp-2020.1 | 217 780 KB defaults jpeg-9c | h14c3975_1001 251 KB conda-forge libblas-3.8.0 | 15_mkl 10 KB conda-forge libcblas-3.8.0 | 15_mkl 10 KB conda-forge libgfortran-ng-7.5.0 | hdf63c60_6 1.7 MB conda-forge liblapack-3.8.0 | 15_mkl 10 KB conda-forge liblapacke-3.8.0 | 15_mkl 10 KB conda-forge libpng-1.6.37 | hed695b0_1 308 KB conda-forge libtiff-4.1.0 | hc7e4089_6 668 KB conda-forge libwebp-base-1.1.0 | h516909a_3 845 KB conda-forge lz4-c-1.8.3 | he1b5a44_1001 187 KB conda-forge mkl-2020.1 | 217 129.0 MB defaults ninja-1.10.0 | hc9558a2_0 1.9 MB conda-forge numpy-1.18.4 | py37h8960a57_0 5.2 MB conda-forge olefile-0.46 | py_0 31 KB conda-forge openssl-1.1.1g | h516909a_0 2.1 MB conda-forge pillow-7.1.2 | py37h718be6c_0 658 KB conda-forge python_abi-3.7 | 1_cp37m 4 KB conda-forge pytorch-1.5.0 | py3.7_cpu_0 90.5 MB pytorch torchvision-0.6.0 | py37_cpu 11.0 MB pytorch zstd-1.4.4 | h3b9ef0a_2 982 KB conda-forge ------------------------------------------------------------ Total: 247.2 MB The following NEW packages will be INSTALLED: blas conda-forge/linux-64::blas-2.15-mkl cpuonly pytorch/noarch::cpuonly-1.0-0 freetype conda-forge/linux-64::freetype-2.10.2-he06d7ca_0 intel-openmp pkgs/main/linux-64::intel-openmp-2020.1-217 jpeg conda-forge/linux-64::jpeg-9c-h14c3975_1001 libblas conda-forge/linux-64::libblas-3.8.0-15_mkl libcblas conda-forge/linux-64::libcblas-3.8.0-15_mkl libgfortran-ng conda-forge/linux-64::libgfortran-ng-7.5.0-hdf63c60_6 liblapack conda-forge/linux-64::liblapack-3.8.0-15_mkl liblapacke conda-forge/linux-64::liblapacke-3.8.0-15_mkl libpng conda-forge/linux-64::libpng-1.6.37-hed695b0_1 libtiff conda-forge/linux-64::libtiff-4.1.0-hc7e4089_6 libwebp-base conda-forge/linux-64::libwebp-base-1.1.0-h516909a_3 lz4-c conda-forge/linux-64::lz4-c-1.8.3-he1b5a44_1001 mkl pkgs/main/linux-64::mkl-2020.1-217 ninja conda-forge/linux-64::ninja-1.10.0-hc9558a2_0 numpy conda-forge/linux-64::numpy-1.18.4-py37h8960a57_0 olefile conda-forge/noarch::olefile-0.46-py_0 pillow conda-forge/linux-64::pillow-7.1.2-py37h718be6c_0 python_abi conda-forge/linux-64::python_abi-3.7-1_cp37m pytorch pytorch/linux-64::pytorch-1.5.0-py3.7_cpu_0 torchvision pytorch/linux-64::torchvision-0.6.0-py37_cpu zstd conda-forge/linux-64::zstd-1.4.4-h3b9ef0a_2 The following packages will be UPDATED: ca-certificates 2019.11.28-hecc5488_0 --> 2020.4.5.1-hecc5488_0 certifi 2019.11.28-py37_0 --> 2020.4.5.1-py37hc8dfbb8_0 openssl 1.1.1d-h516909a_0 --> 1.1.1g-h516909a_0 Downloading and Extracting Packages certifi-2020.4.5.1 | 151 KB | ##################################### | 100% pytorch-1.5.0 | 90.5 MB | ##################################### | 100% libblas-3.8.0 | 10 KB | ##################################### | 100% liblapack-3.8.0 | 10 KB | ##################################### | 100% libpng-1.6.37 | 308 KB | ##################################### | 100% libcblas-3.8.0 | 10 KB | ##################################### | 100% olefile-0.46 | 31 KB | ##################################### | 100% libgfortran-ng-7.5.0 | 1.7 MB | ##################################### | 100% jpeg-9c | 251 KB | ##################################### | 100% ninja-1.10.0 | 1.9 MB | ##################################### | 100% python_abi-3.7 | 4 KB | ##################################### | 100% liblapacke-3.8.0 | 10 KB | ##################################### | 100% numpy-1.18.4 | 5.2 MB | ##################################### | 100% libtiff-4.1.0 | 668 KB | ##################################### | 100% zstd-1.4.4 | 982 KB | ##################################### | 100% blas-2.15 | 10 KB | ##################################### | 100% pillow-7.1.2 | 658 KB | ##################################### | 100% intel-openmp-2020.1 | 780 KB | ##################################### | 100% lz4-c-1.8.3 | 187 KB | ##################################### | 100% mkl-2020.1 | 129.0 MB | ##################################### | 100% freetype-2.10.2 | 905 KB | ##################################### | 100% torchvision-0.6.0 | 11.0 MB | ##################################### | 100% libwebp-base-1.1.0 | 845 KB | ##################################### | 100% cpuonly-1.0 | 2 KB | ##################################### | 100% openssl-1.1.1g | 2.1 MB | ##################################### | 100% ca-certificates-2020 | 146 KB | ##################################### | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.