Switching between TensorFlow and Theano on Keras

Keras speeds up the task of building Neural Networks by providing high-level simplified functions to create and manipulate neural models. It itself does not provide the lower level neural and deep learning functions, but it’s rather meant to be run on an engine – which Keras refers to as a “backend” - which would provide such low-level functions.

Currently, Keras supports two such backends – TensorFlow and Theano.

The current version of Keras (v1.1.0 at the time of this writing) uses TensorFlow by default.

Most models written on top of Keras can be switched to a different backend without changes – at least it’s what’s said in the documentation. I’m yet to test this.

Which backend Kesas will use is defined in the Keras config file, which is located in the .keras directory in your home directory:
e.g.: on linux it would be ~/.keras/keras.json and on windows you can get to it on %USERPROFILE%\.keras\keras.json

For the default of using the TensorFlow backend, use the following config,

 {  
   "image_dim_ordering": "tf",  
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "backend": "tensorflow"  
 }  

Notice the "backend" is set to "tensorflow" and "image_dim_ordering" is set to "tf".

To use the Theano backend, use the following,

 {  
   "image_dim_ordering": "th",   
   "epsilon": 1e-07,   
   "floatx": "float32",   
   "backend": "theano"  
 }  

Apart from the obvious "backend": "theano", note that "image_dim_ordering" is set to "th".

See my new post to see what the image_dim_ordering parameter in Keras does, and why is it important to set it properly.

Update: If you use Jupyter notebooks, and need to switch between TensorFlow and Theano backends quite often, fellow blogger desertnaut has a solution to dynamically switch the backend. Check out his solution at: Dynamically switch Keras backend in Jupyter notebooks

Related posts:
What is the image_dim_ordering parameter in Keras, and why is it important

Related links:
https://keras.io/backend/

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