Fix ImportError: Cannot Import 'ImageDataGenerator' from keras.preprocessing.image

Problem Statement

You encounter the following error when running code in Kaggle environments using Keras:

ImportError: cannot import name 'ImageDataGenerator' from 'keras.preprocessing.image'
(/opt/conda/lib/python3.10/site-packages/keras/preprocessing/image/__init__.py)

This error occurs specifically when attempting to import the ImageDataGenerator class using the traditional import path. The confusing aspect is that the identical code might work in Google Colab but fail in Kaggle, indicating environment-specific differences in Keras implementations.

The core issue stems from:

• Keras version differences between environments
• Restructured modules in newer Keras releases
• Backend variations (TensorFlow vs. standalone Keras)

Why Does This Error Occur?

This import failure is directly related to architectural changes in Keras 3.x and later versions:

1. Keras transitioned to a multi-backend architecture (TensorFlow, JAX, PyTorch)
2. Legacy preprocessing tools were moved to dedicated legacy modules
3. The traditional import path keras.preprocessing.image no longer contains ImageDataGenerator in newer versions
4. Kaggle environments are often updated earlier than Colab, resulting in version mismatches

Preferred Solution: Use tensorflow.keras Import Path

Updated Import Statement

TensorFlow Implementation

# Replace outdated import
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Continue with your code normally
datagen = ImageDataGenerator(rescale=1./255)

Why This Works

• Maintains compatibility with TensorFlow's implementation
• Preserves identical functionality to original Keras
• Works across TensorFlow 2.x versions and Keras 3 backends
• Recommended solution for mixed Colab/Kaggle workflows

Additional Verification

Ensure you have compatible versions in your environment:

pip install tensorflow==2.16.1  # Minimum recommended version

Alternative Solution: Legacy Keras Import Path (Less Recommended)

WARNING

This solution targets specific Keras 3 implementations and might become obsolete in future updates.

Import Path for Standalone Keras

Pure Keras Implementation

from keras.src.legacy.preprocessing.image import ImageDataGenerator

When to Use This

• Only when explicitly using standalone Keras ≥3.0 without TensorFlow
• When backend flexibility is required (JAX, PyTorch)
• Avoid when targeting production environments

Verify Installation

Confirm Keras version compatibility:

pip install keras=3.0.5  # Specific version where this path exists

Conclusion and Recommendations

• Prefer tensorflow.keras imports for maximum compatibility and future-proofing
• Avoid direct keras package imports for ImageDataGenerator in new code
• Pin your environment dependencies to prevent breaking changes:

tensorflow>=2.16.1,<3.0.0
keras==2.15.0  # For legacy support if needed

• For new projects, consider Keras 3's image preprocessing layers as a modern alternative to ImageDataGenerator

The core issue resolves around Keras' modular restructuring. By switching to TensorFlow's implementation, you ensure compatibility across Kaggle, Colab, and local environments while maintaining identical functionality to traditional image preprocessing workflows.