- Related Questions & Answers
- How can Tensorflow and pre-trained model be used for feature extraction?
- How can Tensorflow be used to create a sequential model using Python?
- How can Keras be used to compile the built sequential model in Python?
- How can Keras be used with a pre-trained model using Python?
- How can Keras be used to plot the model using Python?
- How can Keras be used to train the model using Python?
- How can Keras be used to evaluate the model using Python?
- How can Keras be used to plot the model using Python Program?
- How can Keras be used to train the model using Python Program?
- How can Keras be used to save the entire model using Python?
- How can Keras be used to evaluate the restored model using Python?
- How can Keras be used to reload a fresh model from the saved model using Python?
- How can Keras be used to remove a layer from the model using Python?
- How can Keras be used to save and serialize the model using Python?
- How can Keras be used to save model using hdf5 format in Python?

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

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 a multidimensional array or a list. They can be identified using three main attributes −

**Rank**− It tells about the dimensionality of the tensor. It can be understood as the order of the tensor or the number of dimensions in the tensor that has been defined.**Type**− It tells about the data type associated with the elements of the Tensor. It can be a one dimensional, two dimensional or n-dimensional tensor.**Shape**− It is the number of rows and columns together.

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 runs on top of Tensorflow framework. It was built to help experiment in a quick manner. It provides essential abstractions and building blocks that are essential in developing and encapsulating machine learning solutions.

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 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 snippet −

print("Sequential model created") initial_model = keras.Sequential( [ keras.Input(shape=(250, 250, 3)), layers.Conv2D(32, 5, strides=2, activation="relu"), layers.Conv2D(32, 3, activation="relu"), layers.Conv2D(32, 3, activation="relu"), ] ) print("Feature extraction from the model") feature_extractor = keras.Model( inputs=initial_model.inputs, outputs=[layer.output for layer in initial_model.layers], ) print("The feature extractor method is called on test data") x = tf.ones((1, 250, 250, 3)) features = feature_extractor(x)

Code credit − https://www.tensorflow.org/guide/keras/sequential_model

Sequential modal created Feature extraction form the model The feature extractor method is called on test data

Once the architecture of the model is ready, it is trained.

Once the training is completed, it is evaluated.

This model is saved to disk.

This can be restored when required.

Multiple GPU’s can be used to speed up the training of the model.

Once a model has been built, it behaves like a functional API model.

This indicates that every layer has an input and output.

Advertisements