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https://github.com/yungshenglu/tensroflow-mnist

This repository is going to use TensorFlow to train the MNIST network.
https://github.com/yungshenglu/tensroflow-mnist

mnist python tensroflow

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This repository is going to use TensorFlow to train the MNIST network.

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README 

        
# TensorFlow MNIST



This repository is going to use TensorFlow to train the MNIST network.



---

## File Structure



```

mnist/

    |--- out/                       # For the ouput files

    |--- src/                       # Source code are in this directory

        |--- mnist_fully.py         # 2-Hidden Layer Fully Connected NN with TensorFlow

        |--- mnist_fully_model.py   # Add save and restore in mnist_fully.py

    |--- README.md      

    |--- LICENSE

    |--- .gitignore     

```



---

## Installation



> The following instructions are for installing on **Ubuntu Linux 16.04**



### TensorFlow



> TensorFlow is tested and supported on the following 64-bit systems:

> * Ubuntu 16.04 or later

> * Windows 7 or later

> * macOS 10.12.6 (Sierra) or later (no GPU support)

> * Raspbian 9.0 or later



1. Install TensorFlow

    > For Python 2.7, you can follow the instructions [here](https://www.tensorflow.org/install/pip?lang=python2).

    * Prerequisite (for **Python 3 (Python 3.4, 3.5, 3.6)**)

        ```bash

        # Check if your Python environment is already configured

        $ python3 --version

        $ pip3 --version

        $ virtualenv --version

        # If the above packages are already installed, skip to the next step

        $ sudo apt-get update

        $ sudo apt-get install python3-dev python3-pip

        # Install for system-wide

        $ sudo pip3 install -U virtualenv

        ```

    * Create a virtual environment (recommended)

        ```bash

        # Create a new vitrual environment by choosing a Python interpreter and making a ./env directory to hold it

        $ virtualenv --system-site-packages -p python3 ./venv

        # Activate the virtual environement using a shell-specific command (e.g., sg, bash, etc.)

        $ source ./venv/bin/activate

        # When virtualenv is active, your shell prompt is prefixed with (venv).

        (venv) $

        # Install packages within a virtual environment without affecting the host system setup. Start by upgrading pip:

        (venv) $ pip install --upgrade pip

        # Show packages installed within the virtual environment

        (venv) $ pip list

        # To exit virtualenv later

        (venv) $ deactivate

        ```

    * Install TensorFlow with Python's pip package manager 

        ```bash

        # Current release for GPU-only (Python 2.7)

        (venv) $ pip install --upgrade tensorflow

        # GPU package for CUDA-enabled GPU cards (Python 2.7)

        (venv) $ pip install --upgrade tensorflow-gpu

        ```

2. Run the example program `hello.py`

    ```bash

    # Make sure your current directory is src/

    (venv) $ python hello.py

    b'Hello, TensorFlow!'

    ```



---

## Execution



* `./src/mnist_fully.py`

    ```bash

    $ python mnist_fully.py

    2018-12-11 18:57:18.102112: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

    Step 1, Minibatch Loss= 8470.1895, Training Accuracy= 0.328

    Step 100, Minibatch Loss= 250.3591, Training Accuracy= 0.875

    Step 200, Minibatch Loss= 173.7493, Training Accuracy= 0.828

    Step 300, Minibatch Loss= 155.4185, Training Accuracy= 0.820

    Step 400, Minibatch Loss= 81.8946, Training Accuracy= 0.859

    Step 500, Minibatch Loss= 18.5791, Training Accuracy= 0.938

    Optimization Finished!

    Testing Accuracy: 0.8563

    ```

* `./src/mnist_fully_model.py`

    ```bash

    $ python mnist_fully_model.py

    2018-12-11 19:18:36.799674: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

    Step 1, Minibatch Loss= 9184.1309, Training Accuracy= 0.391

    Step 100, Minibatch Loss= 365.5018, Training Accuracy= 0.852

    Step 200, Minibatch Loss= 153.3021, Training Accuracy= 0.820

    Step 300, Minibatch Loss= 24.0616, Training Accuracy= 0.898

    Step 400, Minibatch Loss= 56.0503, Training Accuracy= 0.875

    Step 500, Minibatch Loss= 74.1631, Training Accuracy= 0.844

    Optimization Finished!

    Model saved in file: /tmp/model.ckpt

    Model restored from file: /tmp/model.ckpt

    Testing Accuracy: 0.8559

    ```

* `./src/mnist_fully_input.py`

    ```bash

    $ python mnist_fully_input.py

    2018-12-11 19:25:24.784290: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

    Model restored from file: /tmp/model.ckpt

    Answer: [7 2 1 ..., 4 5 6]

    ```



---

## Contributor



* [David Lu](https://gitbib.com/yungshenglu)



---

## License



Apache License 2.0