How to Fix ModuleNotFoundError: No module named 'keras' in Python

When working with deep learning in Python, you might encounter the error ModuleNotFoundError: No module named 'keras'. This error occurs when Keras is not properly installed or when there are version compatibility issues.

For more information about similar errors, check out our article on How To Solve ModuleNotFoundError: No module named in Python.

Understanding the Error

This error typically appears when trying to import Keras:

import keras

# This will raise the error if Keras is not installed
model = keras.Sequential()

Traceback (most recent call last):
  File "your_script.py", line 1, in 
    import keras
ModuleNotFoundError: No module named 'keras'

How to Fix the Error

Solution 1: Install Keras with TensorFlow

The recommended way to install Keras is through TensorFlow:

pip install tensorflow

Solution 2: Install Standalone Keras

If you need the standalone version:

pip install keras

Solution 3: Install in Virtual Environment

For a clean, isolated installation:

python -m venv myenv
source myenv/bin/activate  # On Windows: myenv\Scripts\activate
pip install tensorflow

Different Ways to Import Keras

There are multiple ways to import Keras, depending on your setup:

# Method 1: Direct import (standalone Keras)
import keras

# Method 2: Import from TensorFlow (recommended)
from tensorflow import keras

# Method 3: Import specific modules
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

Verifying the Installation

After installation, verify that Keras is working correctly:

import tensorflow as tf
from tensorflow import keras

# Print versions
print(f"TensorFlow version: {tf.__version__}")
print(f"Keras version: {keras.__version__}")

# Test basic functionality
model = keras.Sequential([
    keras.layers.Dense(1, input_shape=(1,))
])
model.compile(optimizer='adam', loss='mean_squared_error')

Common Issues and Solutions

GPU Support

For GPU support, install the GPU version of TensorFlow:

pip install tensorflow-gpu

Version Compatibility

Ensure Python and package versions are compatible:

• Python version: 3.7-3.10 recommended
• TensorFlow version: 2.x recommended
• CUDA version: Check compatibility matrix if using GPU

Basic Usage Example

Here's a simple example of creating and training a neural network with Keras:

import numpy as np
from tensorflow import keras

# Create sample data
X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])

# Create model
model = keras.Sequential([
    keras.layers.Dense(1, input_shape=(1,))
])

# Compile model
model.compile(
    optimizer='adam',
    loss='mean_squared_error'
)

# Train model
model.fit(X, y, epochs=100, verbose=0)

# Make prediction
print(model.predict([6]))

Best Practices

• Use virtual environments for project isolation
• Install TensorFlow first before using Keras
• Check GPU compatibility if using GPU acceleration
• Keep versions consistent across all dependencies

Common Problems and Solutions

• ImportError with CUDA: Install correct CUDA and cuDNN versions
• Memory Errors: Reduce batch size or model complexity
• Version Mismatch: Ensure all deep learning packages are compatible

Advanced Example

Here's a more complex example using Keras for image classification:

from tensorflow import keras
from tensorflow.keras import layers

# Create a CNN model
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# Model summary
model.summary()

Conclusion

The ModuleNotFoundError: No module named 'keras' can be resolved by properly installing TensorFlow or standalone Keras. Most users should install TensorFlow and use tensorflow.keras, as this is the recommended approach since TensorFlow 2.0.

Remember to check compatibility between Python, TensorFlow, and Keras versions, and consider using GPU support for better performance with large models. If you continue to experience issues, verify your installation and ensure all dependencies are properly configured.