https://github.com/michaelmendoza/learning-tensorflow

Simple Tensorflow tutorials for learning by example
https://github.com/michaelmendoza/learning-tensorflow

cifar10 convolutional-networks deep-learning example fft fourier-transform keras keras-tensorflow keras-tutorials learning-algorithms learning-tensorflow mnist-classification neural-network python segmentation tensorflow tensorflow-tutorial tensorflow2 tutorial unet

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Simple Tensorflow tutorials for learning by example

• Host: GitHub
• URL: https://github.com/michaelmendoza/learning-tensorflow
• Owner: michaelmendoza
• Created: 2017-11-15T20:29:36.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-08-04T16:48:17.000Z (over 2 years ago)
• Last Synced: 2024-09-30T17:05:14.261Z (4 months ago)
• Topics: cifar10, convolutional-networks, deep-learning, example, fft, fourier-transform, keras, keras-tensorflow, keras-tutorials, learning-algorithms, learning-tensorflow, mnist-classification, neural-network, python, segmentation, tensorflow, tensorflow-tutorial, tensorflow2, tutorial, unet
• Language: Jupyter Notebook
• Homepage:
• Size: 391 KB
• Stars: 18
• Watchers: 5
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Learning Tensorflow

![Supports TFv2.0](https://img.shields.io/badge/Supports-tensorflow%20v2.0-blue.svg)

This tutorial was created for learning tensorflow by example. Currently this repo contains examples for a simple single-layer neural network, a multi-layered perception neural network, and a convolutional neural network. Tensorflow implementations in this repo work with a variety of data sets. Included are simple examples using keras sequential API and more advanced examples using the imperative style of creating networks with model subclassing API.

### Getting Started

Installation requires python 3 with tensorflow. The easiest method to install the requisite libraries is to install the [conda package manager](https://conda.io/miniconda.html). Then run the following command to install necessary libraries and create a virtual environment call `tf`:

```

conda create -n tf python=3.6 numpy matplotlib scikit-image tqdm pycairo

```

If you have a CUDA-enabled GPU install tensorflow-gpu: `pip install tensorflow-gpu`

Otherwise use: `pip install tensorflow`

Activate this virtual environment with `source activate tf` (Mac) or `activate tf` (PC).

### Notes

**Update**: Updated examples for use with tensorflow 2.0!

If you are using older version of Tensorflow like 1.12, please look [here](https://github.com/michaelmendoza/learning-tensorflow/blob/tf-v1.12/README.md)

# Examples

Examples of tensorflow implementations for Classification, Segmentation, Regression and Modeling Fourier Transform

## Basics

Python basics ([tutorial](notebooks/0a%20-%20Python%20Basics.ipynb))

## Regression

Linear regression from scatch with Tensorflow 2.0 ([tutorial](/notebooks/0b%20-%20Regression%20from%20Scratch%20with%20Tensorflow.ipynb))

Non-linear regression with Tensorflow 2.0 and Keras API ([tutorial](notebooks/0c%20-%20Regression%20with%20Tensorflow%20and%20Keras%20API.ipynb))

## Classification

A variety of neural network implementations for MNIST, and CFAR-10 datasets for classification

### MNIST

Classifying using MNIST Dataset

- Basic Neural Network from scatch with Tensorflow 2.0 ([tutorial](notebooks/1a%20-%20Simple%20Neural%20Network.ipynb))

- Basic Neural Network with simple Keras APIs ([tutorial](notebooks/1b%20-%20Simple%20Neural%20Network%20with%20Keras.ipynb))

- Multi-layer Neural Nework ([simple](examples/mnist/mnist1.py), [advanced](examples/mnist/mnist1_imperative.py)) - A simple (multi-layer preception) network for classifying MNIST dataset 

- Convolutional Neural Nework ([simple](examples/mnist/mnist2.py), [advanced](examples/mnist/mnist2_imperative.py)) - A convolutional network for classifying MNIST dataset 

### CIFAR-10

- Basic Neural Network ([code](examples/cifar/cifar0.py)) - A simple (single layer preception) network for classifying CIFAR-10 dataset 

- Multi-layer Neural Nework ([code](examples/cifar/cifar1.py)) - A simple (multi-layer preception) network for classifying CIFAR-10 dataset 

- Convolutional Neural Nework ([code](examples/cifar/cifar2.py)) - A convolutional network for classifying CIFAR-10 dataset

- Convolutional Neural Nework ([code](examples/cifar/cifar3.py)) - A convolutional network (6-conv, 3 max pool, 2 fully-connected layers) with Dropout for classifying CIFAR-10 dataset 

- VGG network ([code](examples/cifar/cifar4.py), [paper](https://arxiv.org/pdf/1409.1556v6.pdf)) - A very deep convolutional network for large-scale image recongition

## Segmentation

Tensorflow implementation for simple color segmentation using a Unet ([tutorial](notebooks/Segmentation.ipynb))

## Modeling Fourier Transform / FFT

Neural network implementation for learning a fourier transform ([code](examples/fft/fft.py))