How can Keras be used to save weights for model after specific number of epochs in Python?

Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.

It has optimization techniques that help in performing complicated mathematical operations quickly.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list.

Keras was developed as a part of research for the project ONEIROS (Open ended Neuro−Electronic Intelligent Robot Operating System). Keras is a deep learning API, which is written in Python. It is a high−level API that has a productive interface that helps solve machine learning problems.

It is highly scalable, and comes with cross platform abilities. This means Keras can be run on TPU or clusters of GPUs. Keras models can also be exported to run in a web browser or a mobile phone as well.

Keras is already present within the Tensorflow package. It can be accessed using the below line of code.

import tensorflow
from tensorflow import keras

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Following is the code −


checkpoint_path = "training_2/cp−{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

batch_size = 32
print("Callback being created to save the model's weight after every 4 epoch")
cp_callback = tf.keras.callbacks.ModelCheckpoint(

print("A new model instance is created")
model = create_model()
print("The weights are saved using 'checkpoint_path'")

Code credit −


Callback being created to save the model's weight after every 4 epoch
A new model instance is created
The weight are saved using 'checkpoint_path'


  • The callback has many options such as providing unqiue names for checkpoints, adjusting the frequency of checkpointing, and so on.

  • The new model is trained.

  • This new model is saved with a unique name for every checkpoint after every 4 epochs.