How to load a model from an HDF5 file in Keras?

Keras is a powerful and easy-to-use open source deep learning library for Python. It provides a high-level interface for working with neural networks and allows you to quickly and easily build, train, and evaluate deep learning models.

One useful feature of Keras is the ability to save and load models. This can be particularly useful if you have trained a model on a large dataset and want to use it for inference at a later time, or if you want to share your trained model with others.

In this post, we will look at how to load a model from an HDF5 file in Keras. HDF5 is a popular file format for storing large, complex data sets, and it is often used for storing deep learning models.

Saving a Keras model to an HDF5 file

Before we can load a model from an HDF5 file, we need to know how to save a model to an HDF5 file. This is actually quite simple to do in Keras.

First, let's build and compile a simple model:

import keras
 
model = keras.Sequential()
model.add(keras.layers.Dense(32, input_shape=(10,)))
model.add(keras.layers.Dense(1))
 
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Next, we can use the save() method of the model to save it to an HDF5 file:

model.save('my_model.h5')

That's it! The model has now been saved to an HDF5 file called my_model.h5.

Loading a Keras model from an HDF5 file

Now that we know how to save a Keras model to an HDF5 file, let's look at how to load it back into memory.

To load a model from an HDF5 file, we can use the keras.models.load_model() function. This function takes a single argument, the file path of the HDF5 file. It returns a compiled Keras model, which can be used for inference or further training.

Here is an example of how to load a model from an HDF5 file:

import keras
 
model = keras.models.load_model('my_model.h5')

That's all there is to it! The model has now been loaded back into memory and is ready to be used.

Using the loaded model

Once the model has been loaded, you can use it as you would any other Keras model. This means you can use it to make predictions, evaluate its performance, or continue training it on new data.

Here is an example of using the loaded model to make predictions on new data:

import numpy as np
 
# Generate some test data
x_test = np.random.random((10, 10))
 
# Make predictions on the test data
y_pred = model.predict(x_test)
 

You can also evaluate the model's performance on test data using the evaluate() method:

x_test = np.random