How can a sequential model be created incrementally with Tensorflow in Python?

A sequential model is relevant when there is a plain stack of layers. In this stack, every layer has exactly one input tensor and one output tensor. It is not appropriate when the model has multiple inputs or multiple outputs. It is not appropriate when the layers need to be shared. It is not appropriate when the layer has multiple inputs or multiple outputs. It is not appropriate when a non-linear architecture is required.

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.

This is because it uses NumPy and multi-dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries, and is well−supported and documented. The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets.

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. 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 means ‘horn’ in Greek. 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.

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.

Let us see an example to create a sequential model with Tensorflow, including Keras −


print("A sequential model is being created")
model = keras.Sequential()
model.add(layers.Dense(2, activation="relu"))
model.add(layers.Dense(3, activation="relu"))
print("Dense layers have been added to the model")

Code credit −


A sequenital model is being created
Dense layers have been added to the model


  • This is an alternate method to create a sequential model in Keras using Python and adding layers to it.

  • A variable is assigned the call to the ‘sequential’ method.

  • Along with this variable, the method ‘add’ is used to generate layers for the model.

  • Once the layers have been added, the data is displayed on the console.