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I'm trying to use the linear chain CRF in my work. I took the help of the example usage code provided in -- https://github.com/tensorflow/tensorflow/tree/r1.0/tensorflow/contrib/crf

My question is how to supply some initial value of "transition_params" in "crf_log_likelihood()". For concreteness of the example, say, I want to initialize it with standard random normal distribution. In the api doc, I saw that "transition_params" can, in fact, be passed as an input argument. Inside the method I see that if no "transition_params" is passed, it is obtained by doing a "vs.get_variable()" with name = "transitions".

So should I do something similar to this, before creating the 'crf_log_likelihood' op? Something like -- transition_params = vs.get_variable("transitions", [num_tags, num_tags], initializer=tf.random_normal_initializer()) -- and then change the call of "crf_log_likelihood()" to "log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(unary_scores, y_t, sequence_lengths_t, transition_params)"?

A Das
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1 Answers1

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The get_variable() inside the definition of crf_log_likelihood() will create a fresh, randomly-initialized variable to represent the transition parameters, if you don't provide one yourself. You only need to provide an explicit transition_params if you don't want the default behavior.

To understand the behavior of get_variable(), see here:

https://www.tensorflow.org/api_docs/python/state_ops/sharing_variables#get_variable

Hope that helps!

Peter Hawkins
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