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Classifying images of everyday objects using a neural network

The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression etc.

In this assignment, you will:

  1. Explore the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
  2. Set up a training pipeline to train a neural network on a GPU
  3. Experiment with different network architectures & hyperparameters

As you go through this notebook, you will find a ??? in certain places. Your job is to replace the ??? with appropriate code or values, to ensure that the notebook runs properly end-to-end. Try to experiment with different network structures and hypeparameters to get the lowest loss.

You might find these notebooks useful for reference, as you work through this notebook:

# Uncomment and run the commands below if imports fail
!conda install numpy pandas 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 - pandas - pytorch - torchvision The following packages will be downloaded: package | build ---------------------------|----------------- blas-2.15 | mkl 10 KB conda-forge ca-certificates-2020.4.5.2 | hecda079_0 147 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-9d | h516909a_0 266 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-1.9.2 | he1b5a44_1 226 KB conda-forge mkl-2020.1 | 217 129.0 MB defaults ninja-1.10.0 | hc9558a2_0 1.9 MB conda-forge numpy-1.18.5 | py37h8960a57_0 5.1 MB conda-forge olefile-0.46 | py_0 31 KB conda-forge pandas-1.0.4 | py37h0da4684_0 10.1 MB conda-forge pillow-7.1.2 | py37h718be6c_0 658 KB conda-forge pytorch-1.5.0 | py3.7_cpu_0 90.5 MB pytorch pytz-2020.1 | pyh9f0ad1d_0 227 KB conda-forge torchvision-0.6.0 | py37_cpu 11.0 MB pytorch zstd-1.4.4 | h6597ccf_3 991 KB conda-forge ------------------------------------------------------------ Total: 255.2 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-9d-h516909a_0 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-1.9.2-he1b5a44_1 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-1.18.5-py37h8960a57_0 olefile conda-forge/noarch::olefile-0.46-py_0 pandas conda-forge/linux-64::pandas-1.0.4-py37h0da4684_0 pillow conda-forge/linux-64::pillow-7.1.2-py37h718be6c_0 pytorch pytorch/linux-64::pytorch-1.5.0-py3.7_cpu_0 pytz conda-forge/noarch::pytz-2020.1-pyh9f0ad1d_0 torchvision pytorch/linux-64::torchvision-0.6.0-py37_cpu zstd conda-forge/linux-64::zstd-1.4.4-h6597ccf_3 The following packages will be UPDATED: ca-certificates 2020.4.5.1-hecc5488_0 --> 2020.4.5.2-hecda079_0 Downloading and Extracting Packages lz4-c-1.9.2 | 226 KB | ##################################### | 100% libtiff-4.1.0 | 668 KB | ##################################### | 100% jpeg-9d | 266 KB | ##################################### | 100% libpng-1.6.37 | 308 KB | ##################################### | 100% liblapacke-3.8.0 | 10 KB | ##################################### | 100% libgfortran-ng-7.5.0 | 1.7 MB | ##################################### | 100% libwebp-base-1.1.0 | 845 KB | ##################################### | 100% freetype-2.10.2 | 905 KB | ##################################### | 100% libblas-3.8.0 | 10 KB | ##################################### | 100% blas-2.15 | 10 KB | ##################################### | 100% libcblas-3.8.0 | 10 KB | ##################################### | 100% mkl-2020.1 | 129.0 MB | ##################################### | 100% pillow-7.1.2 | 658 KB | ##################################### | 100% torchvision-0.6.0 | 11.0 MB | ##################################### | 100% ca-certificates-2020 | 147 KB | ##################################### | 100% pytz-2020.1 | 227 KB | ##################################### | 100% numpy-1.18.5 | 5.1 MB | ##################################### | 100% zstd-1.4.4 | 991 KB | ##################################### | 100% pandas-1.0.4 | 10.1 MB | ##################################### | 100% ninja-1.10.0 | 1.9 MB | ##################################### | 100% intel-openmp-2020.1 | 780 KB | ##################################### | 100% pytorch-1.5.0 | 90.5 MB | ##################################### | 100% liblapack-3.8.0 | 10 KB | ##################################### | 100% olefile-0.46 | 31 KB | ##################################### | 100% cpuonly-1.0 | 2 KB | ##################################### | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done
import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
%matplotlib inline
print("Modules Imported")
Modules Imported
# Project name used for jovian.commit
project_name = '03-cifar10-feedforward'

Exploring the CIFAR10 dataset