Comments (2)
Hello, I think it is not difficult to get the value of the parameters as long as you perform something like sess.run(latent)
. About the log-joint probability, since in SGMCMC algorithm we do not need to compute it (unlike in HMC, since we need to perform the Metropolis-Hastings step there), by default the log_prob
is not returned (to reduce the size of computation graph).
If you want to get log_prob
given the value of parameters stored in a dictionary latent
, you could add some codes in your script as follows:
bn = model.observe(**merge_dicts(latent, observed))
log_prob = bn.log_joint()
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In the end, I modify my joint_prob function and the SGHMC class to store the logp and parameters at the half step. For my case, I only need to do one additional sum to get the logp after calculating the gradient. So it make sense to calculate the gradient and logp at the same time t.
I use SGHMC to fit 1000 chains with the approximate logp and gradient. For every n steps, I calculate the accurate logp for the approximately best chain.
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Related Issues (20)
- questions about dlgm_nf.py HOT 1
- Can't compute prior (local_log_prob) of a StochasticTensor inside tf.scan (in LSTM cell) HOT 11
- Clarifying the * N in log_joint? HOT 4
- Dirichlet + Categorical or Dirichlet + Multinomial toy example ? HOT 5
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- 请问哪里能找到zhusuan的中文文档? HOT 4
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- Why the std of y_mean is so small? HOT 7
- Memory leaks caused by VariationalObjective HOT 2
- How to use custom Hamiltonian? HOT 5
- Eager executation HOT 2
- module 'tensorflow' has no attribute 'make_template' HOT 1
- The examples of ‘semi_supervised_vae’ cannot run successfully HOT 1
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- Examples code is out dated and doesn't work with Tensorflow 2.x HOT 2
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