Linear Regression

Herein are implementations of models that solve linear regression (or it's more specific form; logistic regression) problems.

Data Sets

Various Data sets will be used

Hyperparameters

I explore the effects of certain hyperparameters on the model's learning process.
The hyperparameters that I'll examine are:

learning rate
number of epochs
training set size
validation set size

!conda install pytorch -y

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:
    - pytorch


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    _pytorch_select-0.2        |            gpu_0           2 KB  defaults
    blas-1.0                   |              mkl           6 KB  defaults
    ca-certificates-2020.4.5.1 |       hecc5488_0         146 KB  conda-forge
    certifi-2020.4.5.1         |   py37hc8dfbb8_0         151 KB  conda-forge
    cudatoolkit-10.1.243       |       h6bb024c_0       347.4 MB  defaults
    cudnn-7.6.5                |       cuda10.1_0       179.9 MB  defaults
    intel-openmp-2020.1        |              217         780 KB  defaults
    libgfortran-ng-7.5.0       |       hdf63c60_6         1.7 MB  conda-forge
    mkl-2020.1                 |              217       129.0 MB  defaults
    mkl-service-2.3.0          |   py37he904b0f_0         218 KB  defaults
    mkl_fft-1.0.15             |   py37ha843d7b_0         154 KB  defaults
    mkl_random-1.1.1           |   py37h0da4684_0         366 KB  conda-forge
    ninja-1.10.0               |       hc9558a2_0         1.9 MB  conda-forge
    numpy-1.18.1               |   py37h4f9e942_0           5 KB  defaults
    numpy-base-1.18.1          |   py37hde5b4d6_1         4.2 MB  defaults
    openssl-1.1.1g             |       h516909a_0         2.1 MB  conda-forge
    pytorch-1.4.0              |cuda101py37h02f0884_0       167.4 MB  defaults
    ------------------------------------------------------------
                                           Total:       835.4 MB

The following NEW packages will be INSTALLED:

  _pytorch_select    pkgs/main/linux-64::_pytorch_select-0.2-gpu_0
  blas               pkgs/main/linux-64::blas-1.0-mkl
  cudatoolkit        pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0
  cudnn              pkgs/main/linux-64::cudnn-7.6.5-cuda10.1_0
  intel-openmp       pkgs/main/linux-64::intel-openmp-2020.1-217
  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-7.5.0-hdf63c60_6
  mkl                pkgs/main/linux-64::mkl-2020.1-217
  mkl-service        pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0
  mkl_fft            pkgs/main/linux-64::mkl_fft-1.0.15-py37ha843d7b_0
  mkl_random         conda-forge/linux-64::mkl_random-1.1.1-py37h0da4684_0
  ninja              conda-forge/linux-64::ninja-1.10.0-hc9558a2_0
  numpy              pkgs/main/linux-64::numpy-1.18.1-py37h4f9e942_0
  numpy-base         pkgs/main/linux-64::numpy-base-1.18.1-py37hde5b4d6_1
  pytorch            pkgs/main/linux-64::pytorch-1.4.0-cuda101py37h02f0884_0

The following packages will be UPDATED:

  ca-certificates                     2019.11.28-hecc5488_0 --> 2020.4.5.1-hecc5488_0
  certifi                         2019.11.28-py37hc8dfbb8_1 --> 2020.4.5.1-py37hc8dfbb8_0
  openssl                                 1.1.1d-h516909a_0 --> 1.1.1g-h516909a_0



Downloading and Extracting Packages
ca-certificates-2020 | 146 KB    | ##################################### | 100% 
blas-1.0             | 6 KB      | ##################################### | 100% 
mkl_fft-1.0.15       | 154 KB    | ##################################### | 100% 
numpy-base-1.18.1    | 4.2 MB    | ##################################### | 100% 
mkl_random-1.1.1     | 366 KB    | ##################################### | 100% 
_pytorch_select-0.2  | 2 KB      | ##################################### | 100% 
numpy-1.18.1         | 5 KB      | ##################################### | 100% 
certifi-2020.4.5.1   | 151 KB    | ##################################### | 100% 
intel-openmp-2020.1  | 780 KB    | ##################################### | 100% 
mkl-2020.1           | 129.0 MB  | ##################################### | 100% 
pytorch-1.4.0        | 167.4 MB  | ##################################### | 100% 
cudnn-7.6.5          | 179.9 MB  | ##################################### | 100% 
cudatoolkit-10.1.243 | 347.4 MB  | ##################################### | 100% 
openssl-1.1.1g       | 2.1 MB    | ##################################### | 100% 
mkl-service-2.3.0    | 218 KB    | ##################################### | 100% 
ninja-1.10.0         | 1.9 MB    | ##################################### | 100% 
libgfortran-ng-7.5.0 | 1.7 MB    | ##################################### | 100% 
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

import torch
import csv

with open('dataset.csv') as input_file:
    reader = csv.DictReader(input_file)


for row in reader:

import jovian
jovian.commit()

[jovian] Attempting to save notebook..