how to load keras model

Keras is a popular open-source library that is widely used for building deep learning models. It has a user-friendly, modular, and flexible interface that allows users to build and train complex models quickly and easily. Once a model is trained, it is necessary to save and reload it for future use, especially if it is a large model that requires considerable computation resources. In this article, we will discuss how to load a Keras model in Python.

Load a Keras model with the load_model() function

The easiest way to load a Keras model is to use the load_model() function, which is part of the Keras API. To use this function, first, you need to save your Keras model using the save() function. Once the model is saved, you can load it by calling the load_model() function and passing the file path of the saved model as an argument.

Loading a Keras model in Python

To load a Keras model in Python, you first need to install the Keras library. You can do this by using pip or conda:`pip install keras`or`conda install keras`Once the library is installed, you can load your model as follows:```from keras.models import load_model# load the modelmodel = load_model('path/to/saved/model')```In the example above, we first import the load_model() function from the Keras API. Then we use the function to load the Keras model by passing the path to the saved model file as an argument. The loaded model is then stored as a variable named “model”.

Saving a Keras model

Before you can load a Keras model, you need to save it. You can save a Keras model using the save() function, which is also part of the Keras API.To save a Keras model, you can use the following code:```from keras.models import Sequentialfrom keras.layers import Densefrom keras.models import save_model# create a modelmodel = Sequential()model.add(Dense(10, input_dim=2, activation='relu'))model.add(Dense(1, activation='sigmoid'))# save the modelsave_model(model, 'path/to/saved/model')```In the example above, we first create a Keras model with two dense layers, and we save it using the save_model() function. This function takes two arguments: the trained model and the file path where you want to save the model.

Conclusion

In conclusion, loading a Keras model is a simple process once you know how to use the relevant functions. By saving and reloading your models, you can avoid retraining your model every time you want to use it, saving valuable time and computational resources. Remember to save your models after training, and use the load_model() function to load them when you need them again.