Compile, Evaluate and Predict Model in Keras

Welcome to DataFlair Keras Tutorial series. This chapter explains how to compile, evaluate and make predictions from Model in Keras.

Keras Compile Models

After defining our model and stacking the layers, we have to configure our model. We do this configuration process in the compilation phase.

Before training the model we need to compile it and define the loss function, optimizers, and metrics for prediction.

We compile the model using .compile() method.

model.compile ( optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors)

Optimizer, loss, and metrics are the necessary arguments.

Keras provides various loss functions, optimizers, and metrics for the compilation phase.

#keras evaluate #keras predict #model in keras #keras

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Compile, Evaluate and Predict Model in Keras

Welcome to DataFlair Keras Tutorial series. This chapter explains how to compile, evaluate and make predictions from Model in Keras.

Keras Compile Models

After defining our model and stacking the layers, we have to configure our model. We do this configuration process in the compilation phase.

Before training the model we need to compile it and define the loss function, optimizers, and metrics for prediction.

We compile the model using .compile() method.

model.compile ( optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors)

Optimizer, loss, and metrics are the necessary arguments.

Keras provides various loss functions, optimizers, and metrics for the compilation phase.

#keras evaluate #keras predict #model in keras #keras

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

• It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
• Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
• Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
• It offers a consistent API that provides necessary feedback when an error occurs.
• Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

• The simple API structure of Keras is designed for both new developers and experts.
• The Keras interface is very user friendly and is pretty optimized for general use cases.
• In Keras, you can write custom blocks to extend it.
• Keras is the second most popular deep learning framework after TensorFlow.
• Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

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Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

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Keras Models - Types and Examples

A model is the basic data structure of Keras. Keras models define how to organize layers. In this article, we will discuss Keras Models and its two types with examples. We will also learn about Model subclassing through which we can create our own fully-customizable models.

Types of Keras Models

Models in keras are available in two types:

• Keras Sequential Model
• Keras Functional API

#keras tutorials #functional api in keras #keras models #models in keras

Keras Tutorial For Beginners | What is Keras | Keras Sequential Model | Keras Training

In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo.
Why Keras is important

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that.

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#keras tutorial for beginners #what is keras #keras sequential model #keras training