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.
Related
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.
As Keras becomes an API for TensorFlow, there are lots of old versions of Keras code, such as https://github.com/keiserlab/keras-neural-graph-fingerprint/blob/master/examples.py
from keras import models
With the current version of TensorFlow, do we need to change every Keras code as?
from tensorflow.keras import models
You are mixing things up:
Keras (https://keras.io/) is a library independent from TensorFlow, which specifies a high-level API for building and training neural networks and is capable of using one of multiple backends (among which, TensorFlow) for low-level tensor computation.
tf.keras (https://www.tensorflow.org/guide/keras) implements the Keras API specification within TensorFlow. In addition, the tf.keras API is optimized to work well with other TensorFlow modules: you can pass a tf.data Dataset to the .fit() method of a tf.keras model, for instance, or convert a tf.keras model to a TensorFlow estimator with tf.keras.estimator.model_to_estimator. Currently, the tf.keras API is the high-level API to look for when building models within TensorFlow, and the integration with other TensorFlow features will continue in the future.
So to answer your question: no, you don't need to convert Keras code to tf.keras code. Keras code uses the Keras library, potentially even runs on top of a different backend than TensorFlow, and will continue to work just fine in the future. Even more, it's important to not just mix up Keras and tf.keras objects within the same script, since this might produce incompatabilities, as you can see for example in this question.
Update: Keras will be abandoned in favor of tf.keras: https://twitter.com/fchollet/status/1174019423541157888
Tensorflow object detection API provides a number of pretrained object detection models to choose from. However, I would like to introduce modifications to the architecture of those models.
Particularly, I would like to make Faster RCNN into a more shallow network and use it to train my model. I want to gain in performance despite loss in accuracy. MobileNet is too inaccurate for my application.
Is it possible to achieve this without having to implement everything from scratch ?
Thank you.
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.