I want to convert a model using the bidirectional RNN to a tensorflow lite model.
What can be an equivalent way of achieving the same effect as tf.keras.layers.Bidirectional by writing lower level code.
some of the derivations for that code can be found in this course.
Related
Is there's any way to convert a Tensorflow Lite model to a normal Tensorflow Model that i can use with Tensorflow.Javascript?
not supported by tfjs official converters as this is considered one-way conversion. but this tool is pretty amazing, although not the easiest to setup: https://github.com/PINTO0309/tflite2tensorflow
Is there any way to build object detection model using Tensorflow without any help of tf.keras module?
From Tensorflow documentation I'm not able to find any example which helps to create model without Keras.
Keras is a high level API. But if you want to use only Tensorflow then you have to implement the architecture using low level API. You can certainly implement but you have to code it yourself to build all the convolutional layers and dense layer by yourself.
I am newbie on deep learning and it happens to me to confuse between Keras and tensorflow. knowing that tensorflow is a framework and Keras is a library, what is the difference between using these two deep learning tools.
Keras purposes is to use a framework in backend like Tensorflow, Theano or CNTK in an easier way.
For example, create a simple convolutional model under Tensorflow can be hard.
While create the same model under keras is very instinctive.
The difference between Tensorflow/Theano/CNTK and Keras is the following :
Keras is a framework who use the functions of Tensorflow/Theano/CNTK.
So Keras needs one of them to do something.
Tensorflow/Theano/CNTK or other like coffee can do everything by themselves.
But, often, it's harder to develop a model with them.
I see that there are many similar functions between tensorflow and keras like argmax, boolean_mask...I wonder why people have to use keras as backend along with tensorflow instead of using tensorflow alone.
Keras is not a backend, but it is a high-level API for building and training Neural Networks. Keras is capable of running on top of Tensorflow, Theano and CNTK. Most of the people prefer Keras due to its simplicity compared to other libraries like Tensorflow. I recommend Keras for beginners in Deep Learning.
A Keras tensor is a tensor object from the underlying backend (Theano,
TensorFlow or CNTK), which we augment with certain attributes that
allow us to build a Keras model just by knowing the inputs and outputs
of the model.
Theano vs Tensorflow
Tensorflow is necessary if you wish to use coremltools. Apple has promised support for architectures created using Theano but I haven't seen it yet.
Keras will require unique syntax sugar depending on the backend in use. I like the flexibility of Tensorflow input layers and easy-access to strong Google neural networks.
I have models trained on tensorflow. Can I use mxnet in forward only mode to run these ?
https://github.com/dmlc/nnvm says this should be possible in future, but is the support available today ?
MXNet doesn't have tensorflow model converter yet. It does have a caffe-to-mxnet converter. So you can convert your tf model to caffe, that would work..
https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter