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View Code? Open in Web Editor NEWTensorFlow-based implementation of (Gaussian) Mixture Model and some other examples.
License: Apache License 2.0
TensorFlow-based implementation of (Gaussian) Mixture Model and some other examples.
License: Apache License 2.0
Hi there,
I am confused about the part of applying prior to the computed variances. Would you by any chance explain it? Thanks ;)
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# applying prior to the computed variances
variances_ *= tf.expand_dims(gamma_sum, 1)
variances_ += (2.0 * beta)
variances_ /= tf.expand_dims(gamma_sum + (2.0 * (alpha + 1.0)), 1)
Also, does the log_shit
means the normalization here?
log_weighted = log_components + tf.expand_dims(tf.log(weights), 1)
log_shift = tf.expand_dims(tf.reduce_max(log_weighted, 0), 0)
exp_log_shifted = tf.exp(log_weighted - log_shift)
In addition, at line 82:
log_likelihood = tf.reduce_sum(tf.log(exp_log_shifted_sum)) + tf.reduce_sum(log_shift)
Why added above mentioned maximum value of each component log_shift
?
Hi,
Which article does the code implementation of guassian mixture model refer to? I mean the process of evaluating the maximum likelihood? There are two items of likelihood, i.e., tf.reduce_sum(tf.log(exp_log_shifted_sum)) and tf.reduce_sum(log_shift). From the results I run the code, the first item (i.e., tf.reduce_sum(tf.log(exp_log_shifted_sum))) always give a nearly zero value. And the second item (i.e., tf.reduce_sum(log_shift)) is really useful. I'm not sure that I obtain the right results. I also can not understand the implementation of this part. So would you tell me which article of the method refer to? Or would you explain the process briefly?
How do you actually get a result shown in gmm-best.png. I mean, among several algorithms, which one did you use to get the best result. By just running your codes (e.g. tf_gmm_diag.py, tf_gmm_full.py, tf_gmm_grad, tf_gmm.py), they mostly give incomplete result as below.
Is gmm sensitive to initial mean assignment?
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