A Practical Introduction to Keras Callbacks in TensorFlow 2

When training a machine learning model, we would like to have the ability to monitor the model performance and perform certain actions depending on those performance measures. That’s where Keras Callbacks come in.

Callbacks are an important type of object TensorFlow and Keras that are designed to be able to monitor the performance in metrics at certain points in the training run and perform some action that might depend on those performance in metric values.

In this article, we’ll explore the following popular Keras Callbacks APIs with the help of some examples.

1. EarlyStopping: a callback designed for early stopping.
2. CSVLogger: a callback streams epoch results to a CSV file.
3. ModelCheckpoint : a callback to save the Keras model or model weight during training
4. ReduceLROnPlateau : a callback to reduce the learning rate when a metric has stopped improving.
5. LearningRateScheduler: a callback for learning rate schedules.
6. LambdaCallback: a callback for creating custom callbacks on-the-fly.

Please check out my Github repo for source code.

1. EarlyStopping

EarlyStopping is a built-in callback designed for early stopping. First, let’s import it and create an early stopping object:

from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping()

EarlyStopping() has a few options and by default:

• monitor='val_loss': to use validation loss as performance measure to terminate the training.
• patience=0: is the number of epochs with no improvement. The value 0 means the training is terminated as soon as the performance measure gets worse from one epoch to the next.

Next, we just need to pass the callback object to model.fit() method.

history = model.fit(
    X_train, 
    y_train, 
    epochs=50, 
    validation_split=0.20, 
    batch_size=64, 
    verbose=2,
    callbacks=[early_stopping]
)

You can see that early_stopping get passed in a list to the callbacks argument. It is a list because in practice we might be passing a number of callbacks for performing different tasks, for example, debugging and learning rate schedules.

#machine-learning #callback #keras #deep-learning #tensorflow

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A Practical Introduction to Keras Callbacks in TensorFlow 2

When training a machine learning model, we would like to have the ability to monitor the model performance and perform certain actions depending on those performance measures. That’s where Keras Callbacks come in.

Callbacks are an important type of object TensorFlow and Keras that are designed to be able to monitor the performance in metrics at certain points in the training run and perform some action that might depend on those performance in metric values.

In this article, we’ll explore the following popular Keras Callbacks APIs with the help of some examples.

1. EarlyStopping: a callback designed for early stopping.
2. CSVLogger: a callback streams epoch results to a CSV file.
3. ModelCheckpoint : a callback to save the Keras model or model weight during training
4. ReduceLROnPlateau : a callback to reduce the learning rate when a metric has stopped improving.
5. LearningRateScheduler: a callback for learning rate schedules.
6. LambdaCallback: a callback for creating custom callbacks on-the-fly.

Please check out my Github repo for source code.

1. EarlyStopping

EarlyStopping is a built-in callback designed for early stopping. First, let’s import it and create an early stopping object:

from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping()

EarlyStopping() has a few options and by default:

• monitor='val_loss': to use validation loss as performance measure to terminate the training.
• patience=0: is the number of epochs with no improvement. The value 0 means the training is terminated as soon as the performance measure gets worse from one epoch to the next.

Next, we just need to pass the callback object to model.fit() method.

history = model.fit(
    X_train, 
    y_train, 
    epochs=50, 
    validation_split=0.20, 
    batch_size=64, 
    verbose=2,
    callbacks=[early_stopping]
)

You can see that early_stopping get passed in a list to the callbacks argument. It is a list because in practice we might be passing a number of callbacks for performing different tasks, for example, debugging and learning rate schedules.

#machine-learning #callback #keras #deep-learning #tensorflow

Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

• The performance of Keras is smooth on both CPU and GPU.
• Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
• The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

• From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
• Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
• Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
• Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

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.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

Building Custom Callbacks with Keras and TensorFlow 2

Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.

Keras has provided a number of built-in callbacks, for example, EarlyStopping, CSVLogger, ModelCheckpoint, LearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called Callback which allows us to create our own callbacks and perform some custom actions. In this article, you will learn what is the Callback base class, what it can do, and how to build your own callbacks.

#keras #callback #machine-learning #deep-learning #tensorflow

Semantic Segmentation with TensorFlow Keras - Analytics India Magazine

(https://analyticsindiamag.com/google-arts-culture-uses-ai-to-preserve-endangered-languages/)

Semantic Segmentation laid down the fundamental path to advanced Computer Vision tasks such as object detection, shape recognition, autonomous driving, robotics, and virtual reality. Semantic segmentation can be defined as the process of pixel-level image classification into two or more Object classes. It differs from image classification entirely, as the latter performs image-level classification. For instance, consider an image that consists mainly of a zebra, surrounded by grass fields, a tree and a flying bird. Image classification tells us that the image belongs to the ‘zebra’ class. It can not tell where the zebra is or what its size or pose is. But, semantic segmentation of that image may tell that there is a zebra, grass field, a bird and a tree in the given image (classifies parts of an image into separate classes). And it tells us which pixels in the image belong to which class.

In this article, we discuss semantic segmentation using TensorFlow Keras. Readers are expected to have a fundamental knowledge of deep learning, image classification and transfer learning. Nevertheless, the following articles might fulfil these prerequisites with a quick and clear understanding:

1. Getting Started With Deep Learning Using TensorFlow Keras
2. Getting Started With Computer Vision Using TensorFlow Keras
3. Exploring Transfer Learning Using TensorFlow Keras

Let’s dive deeper into hands-on learning.

#developers corner #densenet #image classification #keras #object detection #object segmentation #pix2pix #segmentation #semantic segmentation #tensorflow #tensorflow 2.0 #unet