In this article, we are going to learn about how to create Models in Keras Introduction Keras is an open-source library in Python which provides APIs for building Artificial Neural Network Models with great flexibility. Modelling in Keras can be done either using the Functional API or the Keras Sequential Model. Keras module is also available under the popular Tensorflow Library. Latest version and installation The latest version of keras as of writing this article is 2.1.0. Keras can be installed from PyPI repository using pip. Advantages of Keras for Modelling Keras is used for fast implementation due to ... Read More

In this article, you will understand the significant differences between Tensorflow and Keras libraries.TensorFlowTensorFlow is an open-source end-to-end platform to build machine learning applications. It was developed by researchers and developers at Google Brain. Let us now see the features of TensorFlow − Build and Train models easily − TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level Keras API, which makes beginning with TensorFlow and machine learning easy. Eager execution lets immediate iteration and intuitive debugging. For large ML training tasks, use the ... Read More

TensorFlow Hub is a repository that contains trained machine learning models. These models are ready to be fine-tuned and deployed anywhere. The trained models such as BERT and Faster R-CNN can be reused with a few lines of code. It is an open-repository, which means it can be used and modified by anyone.The tfhub.dev repository contains many pre-trained models. Some of them include text embeddings, image classification models, TF.js/TFLite models and so on.It can be installed using the below code:!pip install --upgrade tensorflow_hubIt can be imported into the working environment as shown in the below code:import tensorflow_hub as hub Read More

A sequential model can be built using Keras Sequential API that is used to work with plain stack of layers. Here every layer has exactly one input tensor and one output tensor.A pre-trained model can be used as the base model on the specific dataset. This saves the time and resources of having to train the model again on the specific dataset.A pre-trained model is a saved network which would be previously trained on a large dataset. This large dataset would be a large-scale image-classification task. A pre-trained model can be used as it is or it can be customized ... Read More

TensorFlow Text contains collection of text related classes and ops that can be used with TensorFlow 2.0. The library helps in pre-processing which is required by text-based models, and includes other features that are needed for sequence modelling. These features are not present in TensorFlow.Using the ops during text pre-processing is similar to working with Tensorflow graph. This means the user wouldn’t need to worry about tokenization in training being different from tokenization at interference. Ops also helps in managing pre-processing scripts.It can be installed using the below command:pip install -q tensorflow-textTensorFlow Text requires TensorFlow 2.0, and is compatible with ... Read More

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.TensorFlow is used in research and for production purposes and 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 ... Read More

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 networks. 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 ... Read More

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 a 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 ... Read More

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.The ‘tensorflow’ package can be installed on Windows using the below line of code −pip install tensorflowTensor 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. We are using the Google Colaboratory to run the below code. ... Read More

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 a deep neural networks. It is highly scalable and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude ... Read More