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Updated 4 years ago
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..