Overview

The MNIST dataset, which consists of a collection of 70,000 handwritten digits, is a benchmark dataset commonly used for image classification in machine learning. TensorFlow, a popular open-source machine learning framework, provides a range of examples to help users understand how to build and train models using varied datasets, including MNIST. The fully_connected_feed.py script is one such example, which employs a fully connected (dense) neural network to train on the MNIST dataset.

Issues with fully_connected_feed.py

Despite its usefulness, users occasionally encounter issues when running fully_connected_feed.py . This article discusses some common pitfalls and how to troubleshoot them.

Python Environment and Dependencies

One of the most frequent causes for the script not running successfully is an incorrectly configured Python environment. Ensure you have a compatible version of Python and TensorFlow installed. Here are the steps to set up your environment correctly:

1. Python Version:
   • TensorFlow has version-specific dependencies. TensorFlow 2.x requires Python 3.5–3.8.
   • Use version managers like pyenv to manage different Python versions easily.
2. Installing TensorFlow:
   • Use pip to install the required TensorFlow version: pip install tensorflow==2.x.y
   • Verify the installation with:
   • fully_connected_feed.py relies on TensorFlow flags that could differ across versions.
   • Some flags might be deprecated or removed. Check TensorFlow's official documentation for alternatives.
   • TensorFlow 2.x uses eager execution by default. If fully_connected_feed.py was designed for TensorFlow 1.x, consider disabling eager execution or refactoring the code for compatibility.
   • Import necessary modules explicitly, e.g., from tensorflow.keras or tf.compat.v1 .
   • Functions like tf.placeholder and tf.Session are not compatible with eager execution.
   • Adapt them using tf.function or TensorFlow's high-level APIs such as TensorFlow Keras models.
   • Ensure the MNIST dataset is being correctly downloaded and loaded.
   • Use tensorflow.keras.datasets.mnist.load_data() to load data efficiently.
   • Paths may need adjusting if leveraging checkpoints or visualization tools.
   • Ensure directories exist for storing checkpoints or logs.
   • Verify the architecture within fully_connected_feed.py , especially the layer setup and data shapes.
   • Misalignment between input data shape and model expectations can result in cryptic shape errors.
   • Catch exceptions and provide clear logging for debugging.
   • Use try-except blocks to handle any anticipated errors gracefully.