How to save and load keras models

by Abdul Rawoof A R Updated: Aug 29, 2023

Solution Kit

Keras is a high-level neural network library. The design is modular and extensible. Developers can use it to create and test various deep-learning models.

It provides a simplified interface for defining and training neural networks. It makes it accessible to beginners and experienced deep-learning practitioners.

Keras supports many backends, including TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). It acts as a wrapper around these backends. The API is consistent and easy to use. It helps build and train deep learning models. With Keras, you can create various types of neural networks. It also supports more advanced techniques like transfer learning and reinforcement learning.

In electrical power systems, load models show how electrical loads behave on the grid. These models study how the load affects the system and make predictions about it.

Two types of load models are linear load models and nonlinear load models.

Linear Load Model: It assumes that the load behaves with voltage. It is a simplified representation of the load. It does not capture the nonlinear behavior. This model is often used in preliminary studies and system-level analyses. The most used linear load model is the constant power model.
Nonlinear Load Model: It is more sophisticated. It aims to capture the nonlinear behavior of various loads. Nonlinear loads are devices that do not have a direct link between voltage and current. They introduce harmonic currents into the power system, which can cause distortion. It affects the system's performance.

You can use various types of inputs, including images, text, and time series. Here's an overview of the different input types:

Images: Multi-dimensional arrays represent image data. It often has three or four dimensions: width, height, color channels, and batch size. In Keras, image data generators help you load and process images from folders. The ImageDataGenerator class provides useful methods for data augmentation.
Text: Text data is usually represented as sequences of words or characters. You can preprocess text data using tokenization, padding, and one-hot encoding techniques. Keras provides the Tokenizer class to tokenize text data. The pad_sequences function for sequence padding.
Time Series: It consists of observations recorded at regular intervals over time. A one-dimensional array can represent it. You can preprocess time series data by normalizing or standardizing the values.

It saves everything into a single archive in the TensorFlow SavedModel format. The Keras API makes it possible to save all these pieces to disk once or only to save some. Deep learning models can be complex and take days to train and test. An image classification project might include making a Python model that uses CNN. The restored model compiles with the same arguments as the original model.

We can also inspect the model's attributes and loss by calling on the loaded model. Saving a functional model is very useful. You can load them in TensorFlow.js. Then, you can train and run them in web browsers or convert them. The new model of TensorFlow Lite runs on mobile devices. It only loads the model architecture and pre-trained weights. However, the model compilation details are missing, so we need to compile the model.

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution, we are using Keras and NumPy libraries.

Save/load a keras model with constants

PythonLines of Code : 49License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

import numpy as np
from keras.layers import Input, Lambda
from keras.models import Model, load_model

def add_five(a):
    return a + 5

inputs = Input(shape=(1,))
output = Lambda(add_five)(inputs)

model = Model(inputs, output)
model.compile(loss='mse', optimizer='adam')

X = np.array([1,2,3,4,5,6,7,8,9,10])
model.predict(X)

array([[ 6.],
       [ 7.],
       [ 8.],
       [ 9.],
       [10.],
       [11.],
       [12.],
       [13.],
       [14.],
       [15.]], dtype=float32)

def add_constants(a):
    return a + [5, 10]  

# ... the same as above (just change the function name and input shape)

X = np.array([1,2,3,4,5,6,7,8,9,10]).reshape(5, 2)
model.predict(X)

# X
array([[ 1,  2],
       [ 3,  4],
       [ 5,  6],
       [ 7,  8],
       [ 9, 10]])

# predictions
array([[ 6., 12.],
       [ 8., 14.],
       [10., 16.],
       [12., 18.],
       [14., 20.]], dtype=float32)

Instructions

Follow the steps carefully to get the output easily.

Install PyCharm Community Edition on your computer.
Open the terminal and install the required libraries with the following commands.
Install keras - pip install keras.
Install NumPy - pip install numpy.
Create a new Python file(e.g. test.py).
Copy the snippet using the 'copy' button and paste it into that file(use only the first 11 lines of code and add a print statement to the end of the line i.e print(model. predict(X)).
Run the file using the run button.

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

I found this code snippet by searching for 'Save/load a keras model with constants' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

The solution is created in PyCharm 2022.3.3.
The solution is tested on Python 3.9.7.
keras version 2.12.0.
NumPy version 1.24.2.

Using this solution, we are able to save and load Keras models with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to save and load Keras models.

Dependent Libraries

numpyby numpy

Python

23755

Version:v1.25.0rc1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

Support

Quality

Security

License

Reuse

numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

Support

Quality

Security

License

Reuse

kerasby keras-team

Python

58594

Version:v2.13.1-rc0

License: Permissive (Apache-2.0)

Deep Learning for humans

Support

Quality

Security

License

Reuse

kerasby keras-team

Python 58594 Version:v2.13.1-rc0 License: Permissive (Apache-2.0)

Deep Learning for humans

Support

Quality

Security

License

Reuse

You can also search for any dependent libraries on kandi like 'keras' and 'NumPy'.

FAQ:

1. What is the Tensorflow import keras command used for?

Users import the Keras library, which is a high-level deep-learning framework. Keras provides an interface to build and train neural networks. It makes it easier to develop deep learning models.

2. Are deep learning models supported by Keras H5?

Yes, Keras supports deep learning models, including various types of neural networks. It can save and load these models using the Hierarchical Data Format file format. Keras is a high-level deep-learning library. It provides an interface to build, train, and deploy deep learning models. You can use it with different backend engines.

3. Can you reload a before-saved model in TensorFlow into Keras H5?

Yes, it is possible to reload a before-saved TensorFlow model into Keras. It is a high-level API that can run on top of TensorFlow. It provides compatibility with TensorFlow models.

4. When you load a model with Keras H5, what compilation details do you need?

To load a Keras model from an H5 file, you must provide the following details for model compilation:

Loss Function: It measures the model's performance during training. It should be compatible with your specific task.
Optimizer: It updates the model's weights using the computed gradients during training. Common choices include Adam, RMSprop, or SGD. You may need to adjust your optimizer's learning rate or other settings.
Metrics: Metrics test the model's performance during training and evaluation. Examples include accuracy for classification tasks or mean absolute error for regression tasks. You can specify many metrics as a list.

5. Does bytecode play any role in loading and executing models using Keras H5?

No, bytecode does not directly load and execute models using Keras and the H5 file format. Keras is a high-level deep-learning library. It provides an API for building and training neural networks. Keras uses the H5 file format to save and store trained models.

Support

For any support on kandi solution kits, please use the chat
For further learning resources, visit the Open Weaver Community learning page.

See similar Kits and Libraries

Open Weaver – Develop Applications Faster with Open Source

Terms
Privacy policy

How to save and load keras models

Code

Instructions

Environment Tested

Dependent Libraries

FAQ:

Support

Open Weaver – Develop Applications Faster with Open Source

kandi

Community and Support

Company

Follow