Loading a Keras Model from an HDF5 File
Introduction
Keras provides a convenient way to save and load models using the HDF5 format. This allows you to easily store and reuse trained models without the need to retrain them every time. This article will guide you through the process of loading a Keras model from an HDF5 file.
Saving a Keras Model
Before you can load a model, you need to save it first. Here’s how:
“`python
from keras.models import load_model
# Assuming your model is named ‘model’
model.save(‘my_model.h5’)
“`
This code saves your model to a file named “my_model.h5”.
Loading a Keras Model
To load a saved Keras model, you can use the `load_model()` function:
“`python
from keras.models import load_model
# Load the model from the HDF5 file
loaded_model = load_model(‘my_model.h5’)
“`
This will create a new model object named `loaded_model` which is a replica of your saved model.
Using the Loaded Model
Once you’ve loaded the model, you can use it just like any other Keras model. For example, you can use it to make predictions on new data:
“`python
# Load the model
loaded_model = load_model(‘my_model.h5’)
# Make predictions
predictions = loaded_model.predict(new_data)
“`
Benefits of Using HDF5
* **Convenience:** It simplifies the process of saving and loading models.
* **Portability:** HDF5 files can be easily transferred and shared across different platforms.
* **Efficiency:** HDF5 is a highly efficient format for storing large datasets and model structures.
Additional Tips
* **Custom Objects:** If your model includes custom objects like custom layers or loss functions, you need to provide the necessary definitions when loading the model. This can be done by passing a custom `custom_objects` dictionary to the `load_model()` function.
* **Weights Only:** You can also load only the weights of a model using the `load_weights()` method. This can be useful if you want to use the same model architecture with different weights.
Example
Here’s a complete example demonstrating how to save, load, and use a Keras model:
“`python
from keras.models import Sequential
from keras.layers import Dense
from keras.models import load_model
# Define a simple model
model = Sequential()
model.add(Dense(10, activation=’relu’, input_shape=(10,)))
model.add(Dense(1, activation=’sigmoid’))
# Compile the model
model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])
# Save the model
model.save(‘my_model.h5’)
# Load the model
loaded_model = load_model(‘my_model.h5’)
# Make predictions using the loaded model
predictions = loaded_model.predict(new_data)
“`
Conclusion
Saving and loading Keras models using HDF5 files is a crucial aspect of model development and deployment. It allows for efficient model storage and reuse, making your workflow more streamlined and manageable. By following the steps outlined in this article, you can seamlessly integrate model saving and loading into your Keras projects.