In this post we’ll learn how to use the Keras library for deep learning in conjunction with the TensorFlow backend. We’ll also take a look at some of the benefits of using Keras over other libraries.

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## What is Keras?

Keras is a high-level neural networks API that was written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

As a result, Keras has gained favor as a tool among fast-moving research teams.6 Keras is also widely known for its ease of use; it’s a very user-friendly API.7 This ease of use does come at the cost of reduced flexibility; however, Keras provides good enough abstraction that it is still widely used even among teams who have the resources to build their own libraries.

Keras was developed and maintained by Google engineer François Chollet,8 who also authored the popular Deep Learning textbook called Deep Learning with Python. Chollet also happens to be the creator of the XCeption deep learning model,9 one of the best image classification models available today.

## What is TensorFlow?

TensorFlow is a popular open-source platform for machine learning that enables developers to easily create sophisticated deep learning models. Keras is a high-level neural networks API that is designed to be user-friendly and easy to use. Keras can be used with a TensorFlow backend and can run on top of either TensorFlow or Theano. In this tutorial, we will show you how to use the TensorFlow backend with the Keras library.

## Why use Keras with TensorFlow?

There are a number of reasons why you might want to use Keras with a TensorFlow backend. The first is that TensorFlow is more widely used in the industry, so if you want to work with Keras in a professional setting, it’s good to be familiar with both frameworks.

Second, Keras is a high-level framework, which means that it is easier to use than TensorFlow. If you’re just getting started with deep learning, Keras will save you a lot of time and effort.

Finally, using Keras with TensorFlow gives you the best of both worlds. You get the flexibility of TensorFlow, plus the simplicity of Keras.

## How to use Keras with TensorFlow?

In this guide, we’ll show you how to use the TensorFlow backend with Keras. Keras is a high-level neural networks API that is used for making powerful and sophisticated Deep Learning models. It’s written in Python and can run on top of either TensorFlow or Theano.

Using Keras with a TensorFlow backend is a lot simpler than you might think. In fact, all you need to do is install the TensorFlow backend and then specify it when you create your Keras model. Let’s take a look at how to do this.

First, you’ll need to install the TensorFlow backend. You can do this using pip:

pip install tensorflow

Next, you’ll need to specify the TensorFlow backend when you create your Keras model. You can do this by passing the argument “backend” to the keras_model_sequential function:

from keras import backend as K

K.set_image_dim_ordering(‘th’)

model = Sequential() # Now using TensorFlow as the backend!

## What are the benefits of using Keras with TensorFlow?

There are a number of benefits to using Keras with a TensorFlow backend. First, TensorFlow is a very powerful and efficient library for performing mathematical operations on tensors (i.e. data structures that can be represented as arrays). This means that Keras can take advantage of the speed and efficiency of TensorFlow while still providing a high-level API that is easy to use.

Second, Keras integrates well with other TensorFlow libraries, such as thetf.contrib and tf.layers libraries. This allows you to easily build complex models by combining the different capabilities of these libraries.

Third, Keras provides a number of convenient features that make working with TensorFlow models easier, such as automatic generation of model diagrams and visualizations, pre-trained model weights, and more.

Overall, using Keras with a TensorFlow backend can help you get the most out of your TensorFlow models while still providing an easy-to-use API.

## What are the drawbacks of using Keras with TensorFlow?

There are a few potential drawbacks of using Keras with TensorFlow:

1. Keras is a high-level wrapper around TensorFlow (and other libraries), which means that it can be slower to execute than pure TensorFlow code.

2. Keras is relatively new, and thus may not have all the features or functionality that you need.

3. Some developers prefer to use TensorFlow directly, rather than through a wrapper like Keras.

## How does Keras compare to other frameworks?

There are many different deep learning frameworks out there, each with its own advantages and disadvantages. So how does Keras compare to other frameworks?

Keras is a high-level framework that makes it easy to build deep learning models. It’s simple to use, yet powerful enough to build complex models. Keras also has a number of advantages over other frameworks:

-It’s easy to get started with Keras – you can create a simple model in just a few lines of code.

-Keras has a simple, consistent interface, making it easy to learn and use.

-Keras is compatible with both Theano and TensorFlow, so you can use whichever backend you prefer.

-Keras models can be deployed on mobile devices (iOS, Android) and embedded systems (Raspberry Pi, BeagleBone Black).

So if you’re looking for a deep learning framework that is easy to use and powerful, Keras is a good choice.

## What are some common problems when using Keras with TensorFlow?

Some common problems when using Keras with TensorFlow are that the TensorFlow backend can be hard to debug, and that Keras can sometimes be too high-level for TensorFlow.

## How can I get started with Keras?

Keras is a popular libraries for Deep Learning. It’s simple to use and can run on top of TensorFlow, Microsoft CNTK, or Theano.

If you’re just getting started with Keras, we recommend reading our Keras tutorial. For more advanced users, we recommend reading our more comprehensive guide toDevelop Your First Neural Network in Python With Keras Step-By-Step.

## Conclusion

In this article, we’ve seen how to use the Keras library with a TensorFlow backend. We’ve also seen how to use some of the TensorFlow features in Keras, such as creating custom layers and models.

Keyword: Using Keras with a TensorFlow Backend