To my understanding tensorflow.contrib is removed in TF 2.0 API. What's the new 2.0 alternative for tensorflow.contrib.graph_editor in TF 1.0 API? https://www.tensorflow.org/api_docs/python/tf/contrib/graph_editor
The alternative as I understand from the documentation is the approach "Keep track of your variables!". When creating graph, you are now responsible for referencing it's nodes.
Related
What is difference between tf.keras.models.Sequential() vs tf.keras.Sequential()? I don't understand differences between them quite well. Can somebody explain it to me? I am new to TensorFlow but have some basic understanding on machine learning.
>>> tf.keras.models.Sequential==tf.keras.Sequential
True
Both are same as of TFv2. You could use the later.
Added in this commit.
tf.keras.models.Sequential
and
tf.keras.Sequential
Do the same thing but they are from different versions of tensorflow. By the documentation (TensorFlow 2.0), tf.keras.Sequential is the most recent way of called this function.
Keras (keras.io) is a library which is available on its own. It specifies the high-level api.
tf.keras (https://www.tensorflow.org/guide/keras) implements the Keras API specification within TensorFlow.
If you intend to stick to the Tensorflow implementation I would stick to tf.keras. Otherwise you have the advantage to be backend agnostic.
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update for updated question.
The renaming of the package for tf.keras.models.Sequential to tf.keras.Sequential must have happened from 1.15 to 2.x you can either downgrade your tensor flow version or update the code. I'd go for the latter
Previously, sess.graph was used as a handle to push things to tensorboard.
There is no current replacement AFAIK. Visualizing graphs is fundamental.
How can we viz graphs in tensorflow 2.0? Must be some hook into the functions.
Tensorboard works in 2.0. This example for keras and this without
How to replace from tensorflow.contrib import layers with new core functionality. I want to move my TF 1.4 code to 1.12 in preparation for TF 2.0.
The core functionality corresponding to tf.contrib.layers is in tf.layers. Some of the differences are discussed in this question. However this will not prepare you for TF 2.0.
If your goal is to prepare your code for TF 2.0, consider that tf.contrib will be removed entirely (either split from TF or integrated into it) and that tf.layers too will be removed and the high-level API will reside under tf.keras. So to best prepare for TF 2.0 you should start using tf.keras.layers instead.
Here is a blog post about some of the practical differences to expect with TF 2.0.
In TF 1.12 or TF 2.0 is there going to be a replacement for the following function:
from tensorflow.contrib.training.python.training import hparam
I read that contrib module will go away or merge into core.
In TF 2.0 there is a new API tensorboard.plugins.hparams.api that includes a class HParam
Usage of the new API is described in this guide: Hyperparameter Tuning with the HParams Dashboard
Recently found this Weights and Biases framework that seems to nail the job. It's a more comprehensive solution, and they've got a nicer dashboard.
How does one use KenLM with tensorflow as decoder?
I know about tensorflow-with-kenlm tf fork, but it is based on 1.1 tf version which doesn't have many important features for my project.