tfp.experimental.mcmc.ProgressBarReducer
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Reducer that displays a progress bar.
Inherits From: Reducer
tfp.experimental.mcmc.ProgressBarReducer(
num_results, progress_bar_fn=make_tqdm_progress_bar_fn()
)
Used in the notebooks
Note this is not XLA-compatible (tf.function(jit_compile=True)).
Numpy and JAX substrates are not supported.
Example usage:
kernel = ...
current_state = ...
num_results = ...
pbar = tfp.experimental.mcmc.ProgressBarReducer(num_results)
_, final_state, kernel_results = tfp.experimental.mcmc.sample_fold(
num_steps=num_results,
current_state=current_state,
kernel=kernel,
reducer=pbar,
)
Args |
|---|
num_results
|
Integer number of results to expect (as passed to sample
chain).
|
progress_bar_fn
|
A function that takes an integer num_results and
returns an iterator that advances a progress bar. Defaults to tqdm
progress bars (make sure they are pip installed befure using.)
|
Attributes |
|---|
num_results
|
|
parameters
|
|
progress_bar_fn
|
|
Methods
finalize
View source
finalize(
final_reducer_state
)
Finalizes target statistic calculation from the final_state.
This is an identity function of the final_state by default. Subclasses
can override it for streaming calculations whose running state is not the
same as the desired result.
| Args |
|---|
final_reducer_state
|
A tuple, namedtuple or list of Tensors
representing the final state of the reduced statistic.
|
| Returns |
|---|
statistic
|
An estimate of the target statistic
|
initialize
View source
initialize(
initial_chain_state, initial_kernel_results=None
)
Initialize progress bars.
All arguments are ignored.
| Args |
|---|
initial_chain_state
|
A (possibly nested) structure of Tensors or Python
lists of Tensors representing the current state(s) of the Markov
chain(s). It is used to infer the structure of future trace results.
|
initial_kernel_results
|
A (possibly nested) structure of Tensors
representing internal calculations made in a related TransitionKernel.
It is used to infer the structure of future trace results.
|
| Returns |
|---|
state
|
empty list.
|
one_step
View source
one_step(
new_chain_state, current_reducer_state, previous_kernel_results
)
Advance progress bar by one result.
All arguments are ignored.
| Args |
|---|
new_chain_state
|
A (possibly nested) structure of incoming chain state(s)
with shape and dtype compatible with those used to initialize the
TracingState.
|
current_reducer_state
|
TracingStates representing all previously traced
results.
|
previous_kernel_results
|
A (possibly nested) structure of Tensors
representing internal calculations made in a related
TransitionKernel.
|
| Returns |
|---|
new_reducer_state
|
empty list.
|
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Last updated 2023-11-21 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.experimental.mcmc.ProgressBarReducer\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/progress_bar_reducer.py#L49-L150) |\n\n`Reducer` that displays a progress bar.\n\nInherits From: [`Reducer`](../../../tfp/experimental/mcmc/Reducer) \n\n tfp.experimental.mcmc.ProgressBarReducer(\n num_results, progress_bar_fn=make_tqdm_progress_bar_fn()\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|----------------------------------------------------------------------------------------------------------------------|\n| - [TFP Release Notes notebook (0.12.1)](https://www.tensorflow.org/probability/examples/TFP_Release_Notebook_0_12_1) |\n\nNote this is not XLA-compatible ([`tf.function(jit_compile=True)`](https://www.tensorflow.org/api_docs/python/tf/function)).\nNumpy and JAX substrates are not supported.\n\n#### Example usage:\n\n kernel = ...\n current_state = ...\n num_results = ...\n pbar = tfp.experimental.mcmc.ProgressBarReducer(num_results)\n _, final_state, kernel_results = tfp.experimental.mcmc.sample_fold(\n num_steps=num_results,\n current_state=current_state,\n kernel=kernel,\n reducer=pbar,\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `num_results` | Integer number of results to expect (as passed to sample chain). |\n| `progress_bar_fn` | A function that takes an integer `num_results` and returns an iterator that advances a progress bar. Defaults to `tqdm` progress bars (make sure they are pip installed befure using.) |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------------|---------------|\n| `num_results` | \u003cbr /\u003e \u003cbr /\u003e |\n| `parameters` | \u003cbr /\u003e \u003cbr /\u003e |\n| `progress_bar_fn` | \u003cbr /\u003e \u003cbr /\u003e |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `finalize`\n\n[View source](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/reducer.py#L88-L102) \n\n finalize(\n final_reducer_state\n )\n\nFinalizes target statistic calculation from the `final_state`.\n\nThis is an identity function of the `final_state` by default. Subclasses\ncan override it for streaming calculations whose running state is not the\nsame as the desired result.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|-----------------------|-------------------------------------------------------------------------------------------------------|\n| `final_reducer_state` | A `tuple`, `namedtuple` or `list` of `Tensor`s representing the final state of the reduced statistic. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|-------------|-------------------------------------|\n| `statistic` | An estimate of the target statistic |\n\n\u003cbr /\u003e\n\n### `initialize`\n\n[View source](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/progress_bar_reducer.py#L89-L110) \n\n initialize(\n initial_chain_state, initial_kernel_results=None\n )\n\nInitialize progress bars.\n\nAll arguments are ignored.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `initial_chain_state` | A (possibly nested) structure of `Tensor`s or Python `list`s of `Tensor`s representing the current state(s) of the Markov chain(s). It is used to infer the structure of future trace results. |\n| `initial_kernel_results` | A (possibly nested) structure of `Tensor`s representing internal calculations made in a related `TransitionKernel`. It is used to infer the structure of future trace results. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---------|-------------|\n| `state` | empty list. |\n\n\u003cbr /\u003e\n\n### `one_step`\n\n[View source](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/progress_bar_reducer.py#L112-L138) \n\n one_step(\n new_chain_state, current_reducer_state, previous_kernel_results\n )\n\nAdvance progress bar by one result.\n\nAll arguments are ignored.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|---------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| `new_chain_state` | A (possibly nested) structure of incoming chain state(s) with shape and dtype compatible with those used to initialize the `TracingState`. |\n| `current_reducer_state` | `TracingState`s representing all previously traced results. |\n| `previous_kernel_results` | A (possibly nested) structure of `Tensor`s representing internal calculations made in a related `TransitionKernel`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---------------------|-------------|\n| `new_reducer_state` | empty list. |\n\n\u003cbr /\u003e"]]
View source on GitHub