import input_data MNIST tensorflow not working

Troubleshooting “Import input_data MNIST TensorFlow Not Working”

Encountering the error “ImportError: cannot import name ‘input_data’ from ‘tensorflow.examples.tutorials.mnist'” when working with MNIST dataset in TensorFlow is a common issue. This typically arises due to changes in TensorFlow’s structure over time. Here’s a comprehensive guide to resolving this problem.

Understanding the Issue

The input_data.py file, previously included in TensorFlow for convenient access to MNIST data, is no longer present in newer versions. This means you’ll need to use an alternative method to load the MNIST dataset.

Solutions

1. Using `tf.keras.datasets`

TensorFlow Keras provides a straightforward way to load MNIST data.

Code Example:

import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

2. Downloading MNIST from Keras

You can download the MNIST dataset directly from Keras and use it with TensorFlow.

Code Example:

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

3. Using `tf.data.Dataset.from_tensor_slices`

For more control over data loading and preprocessing, you can use `tf.data.Dataset.from_tensor_slices`.

Code Example:

import tensorflow as tf
import tensorflow_datasets as tfds

# Load MNIST dataset using tensorflow_datasets
mnist_data = tfds.load('mnist', as_supervised=True)
train_data = mnist_data['train']
test_data = mnist_data['test']

# Convert to TensorFlow dataset for training
train_ds = train_data.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y))
train_ds = train_ds.cache().shuffle(10000).batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)

# Convert to TensorFlow dataset for testing
test_ds = test_data.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y))
test_ds = test_ds.cache().batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)

Additional Tips

• Ensure you have TensorFlow and TensorFlow Datasets installed: pip install tensorflow tensorflow-datasets
• Check your TensorFlow version: Older versions might have `input_data` available. Use print(tf.__version__) to see your current version.
• Use an Integrated Development Environment (IDE) with code completion and error highlighting for easier debugging.