This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255.
In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).
Python 3.6–3.8 Python 3.8 support requires TensorFlow 2.2 or later. pip 19.0 or later (requires manylinux2010 support) Ubuntu 16.04 or later (64-bit) macOS 10.12.6 (Sierra) or later (64-bit) (no GPU support) macOS requires pip 20.3 or later Windows 7 or later (64-bit) Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 GPU support requires a CUDA®-enabled card (Ubuntu and Windows)
- Install the Python development environment on your system Check if your Python environment is already configured:
Requires Python 3.6–3.8, pip and venv >= 19.0
Use the package manager pip to install Tensorflow.
python3 --version
pip3 --version- Install Tensorflow with the PIP Command line
pip3 install --user --upgrade tensorflow # install in $HOME
- Setup the input parameters of the Neural network to increase/decrease performance.
- Choose the dataset that must be executed
- Run the main.py file