1594319580

Part I of this series, we have learned different approaches to save architecture only or save weights only or entire Keras model. However, we had covered only small part of saving entire model. In this article we will focus only on saving entire model when we have a custom metric/loss or a custom layer.

Outline of this article is as follows

- Overview of saving and loading entire model
- Saving entire model before or after training
- Saving entire model during training
- Saving entire model with a custom metric/loss
- Saving entire model with custom layer

As mentioned in Part I of this series, entire Keras model consist(the following is from TensorFlow website)

An architecture, or configuration, which specifies what layers the model contain, and how they’re connected

Avalues (the “state of the model”)set of weights

_ (defined by compiling the model)_An optimizer****state

A set of(defined by compiling the model)losses and metrics

Entire Keras model can be saved to a disk in two formats (i) TensorFlow SavedModel ( `tf`

) format, and (ii) H5 format.

Entire Keras model can be saved either during training or before/after training the model. We will see see more details and examples in the following sections.

How to save entire model?

Entire Keras model can be saved using Saved model API by `model.save(‘MyModel’,save_format='tf')`

or `model.save('MyModel_h5',save_format='h5')`

. The `tf`

format is default which means if you don’t provide `save_format`

argument, then the model is saved in TensorFlow SavedModel `tf`

format.

Why do we need to save entire model?

- Sharing entire model is simple and error prone. You can share it with your team or client so that they can reproduce exactly same result as you are
- As saving entire model includes optimizer state, you can restart the training where you left off.
- Entire model can be easily converted to TFLite format so that you can deploy the model on mobile devices
- Entire model can be converted to TensorFlow.js Layers format, which can be loaded directly into TensorFlow.js for inference or for further training.

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

1607579145

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.

Call Our Course Advisors IND: +91-7022374614 US: 1-800-216-8930 (Toll-Free) sales@intellipaat.com

Link: https://www.youtube.com/watch?v=nS1J-2uoKto

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

1591861777

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.

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

1595422560

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.

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.

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.

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.

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.

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.

It allows you to define more complex models.

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

1594319580

Part I of this series, we have learned different approaches to save architecture only or save weights only or entire Keras model. However, we had covered only small part of saving entire model. In this article we will focus only on saving entire model when we have a custom metric/loss or a custom layer.

Outline of this article is as follows

- Overview of saving and loading entire model
- Saving entire model before or after training
- Saving entire model during training
- Saving entire model with a custom metric/loss
- Saving entire model with custom layer

As mentioned in Part I of this series, entire Keras model consist(the following is from TensorFlow website)

An architecture, or configuration, which specifies what layers the model contain, and how they’re connected

Avalues (the “state of the model”)set of weights

_ (defined by compiling the model)_An optimizer****state

A set of(defined by compiling the model)losses and metrics

Entire Keras model can be saved to a disk in two formats (i) TensorFlow SavedModel ( `tf`

) format, and (ii) H5 format.

Entire Keras model can be saved either during training or before/after training the model. We will see see more details and examples in the following sections.

How to save entire model?

Entire Keras model can be saved using Saved model API by `model.save(‘MyModel’,save_format='tf')`

or `model.save('MyModel_h5',save_format='h5')`

. The `tf`

format is default which means if you don’t provide `save_format`

argument, then the model is saved in TensorFlow SavedModel `tf`

format.

Why do we need to save entire model?

- Sharing entire model is simple and error prone. You can share it with your team or client so that they can reproduce exactly same result as you are
- As saving entire model includes optimizer state, you can restart the training where you left off.
- Entire model can be easily converted to TFLite format so that you can deploy the model on mobile devices
- Entire model can be converted to TensorFlow.js Layers format, which can be loaded directly into TensorFlow.js for inference or for further training.

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

1598424619

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

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