As I read in the source code, SessionBundlePredict uses collection_def in MetaGraphDef and SavedModelPredict uses signature_def in MetaGraphDef but I have no idea what is the difference between collection_def and signature_def.
My understanding of the two is that when I use Exporter.export then I should use SessionBundlePredict but when I use SavedModelBuilder then I should use SavedModelPredict?
That's essentially correct. Specifically, the signature information for session bundles is stored in a special collection in collection_def.
However, SessionBundle and Exporter.export have been unsupported since 2017-06-30. So please use SavedModel going forward.
Related
I am trying to change my code from edward to tensorflow_probability.edward2. The issue is that, whenever I define a posterior distribution, I use a_post = ed.copy(a, {u: qu}, scope='a_post') but .copy API seems no longer available:
module 'tensorflow_probability.python.edward2' has no attribute 'copy'
What's the tensorflow_probability way of doing the same operation?
In edward, copy depended on unsupported TF graph-walking and copying. In edward2, the approach is based on tracing, using 'interceptors'. Check out https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/deep_exponential_family.py for an example of VI using the 'tape' interceptor.
Update: this one might be a simpler and/or more familiar (LDA) example: https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/latent_dirichlet_allocation_edward2.py
How do I get the filename of the latest checkpoint's graph?
In the examples for tf.train.import_meta_graph I typically see the filename hard-coded to be something like checkpoint_dir + 'model.ckpt-1234.meta'.
After importing, .restore can load the latest training parameters as in:
saver.restore(*session*, tf.train.latest_checkpoint(*my_dir*))
However, what is a reliable way to get the graph's filename? In my case,
tf.train_import_meta_data(tf.train.latest_checkpoint(*my_dir*) + '.meta')
should do the job but I don't think its reliable as checkpoints don't necessarily save the metagraph every time, correct?
I can write a routine that looks through the checkpoint dir and walks back until I find a .meta. But, is there a better/built-in way such as tf.train.latest_metagraph(*my_dir*)?
I just found an article where someone implemented the routine I mentioned in my question. This isn't an "answer", but is what I'll use assuming there isn't a built-in solution.
This is from the very nice article at seaandsalor written by João Felipe Santos.
I don't know the usage rules, so I didn't want to directly link to his code. Follow that link if interested and then go to the gist mentioned on the bottom.
What I'm trying to do
I'm new to C++ and bazel and I want to make some change on the convolution operation in tensorflow, so I decide that my first step is to create an ops just like it.
What I have done
I copied conv_ops.cc from //tensorflow/core/kernels and change the name of the ops registrated in my new_conv_ops.cc. I also changed some name of the functions in the file to avoid duplication. And here is my BUILD file.
As you can see, I copy the deps attributes of conv_ops from //tensorflow/core/kernels/BUILD. Then I use "bazel build -c opt //tensorflow/core/user_ops:new_conv_ops.so" to build the new op.
What my problem is
Then I got this error.
I tried to delete bounds_check and got same error for the next deps. Then I realize that there is some problem for including h files in //tensorflow/core/kernels from //tensorflow/core/user_ops. So how can I perfectely create a new op excatcly like conv_ops?
Adding a custom operation to TensorFlow is covered in the tutorial here. You can also look at actual code examples.
To address your specific problem, note that the tf_custom_op_library macro adds most of the necessary dependencies to your target. You can simply write the following :
tf_custom_op_library(
name="new_conv_ops.so",
srcs=["new_conv_ops.cc"]
)
I have a checkpoint file:
checkpoint-20001 checkpoint-20001.meta
how do I extract variables from this space, without having to load the previous model and starting session etc.
I want to do something like
cp = load(checkpoint-20001)
cp.var_a
It's not documented, but you can inspect the contents of a checkpoint from Python using the class tf.train.NewCheckpointReader.
Here's a test case that uses it, so you can see how the class works.
https://github.com/tensorflow/tensorflow/blob/861644c0bcae5d56f7b3f439696eefa6df8580ec/tensorflow/python/training/saver_test.py#L1203
Since it isn't a documented class, its API may change in the future.
I've used the following command to generate a topic model from some documents:
bin/mallet train-topics --input topic-input.mallet --num-topics 100 --output-state topic-state.gz
I have not, however, used the --output-model option to generate a serialized topic trainer object. Is there any way I can use the state file to infer topics for new documents? Training is slow, and it'll take a couple of days for me to retrain, if I have to create the serialized model from scratch.
We did not use the command line tools shipped with mallet, we just use the mallet api to create the serialized model for inferences of the new document. Two point need special notice:
You need serialize out the pipes you used just after you finish the training (For my case, it is SerialPipes)
And of cause the model need also to be serialized after you finish the training(For my case, it is ParallelTopicModel)
Please check with the java doc:
http://mallet.cs.umass.edu/api/cc/mallet/pipe/SerialPipes.html
http://mallet.cs.umass.edu/api/cc/mallet/topics/ParallelTopicModel.html
Restoring a model from the state file appears to be a new feature in mallet 2.0.7 according to the release notes.
Ability to restore models from gzipped "state" files. From the new
TopicTrainer, use the --input-state [filename] argument. Note that you
can manually edit this file. Any token with topic set to -1 will be
immediately resampled upon loading.
If you mean you want to see how new documents fit into a previously trained topic model, then I'm afraid there is no simple command you can use to do it right.
The class cc.mallet.topics.LDA in mallet 2.0.7's source code provides such a utility, try to understand it and use it in your program.
P.S., If my memory serves, there is some problem with the implementation of the function in that class:
public void addDocuments(InstanceList additionalDocuments,
int numIterations, int showTopicsInterval,
int outputModelInterval, String outputModelFilename,
Randoms r)
You have to rewrite it.