tfp.substrates.numpy.math.MinimizeTraceableQuantities
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Namedtuple of quantities that may be traced from tfp.math.minimize.
tfp.substrates.numpy.math.MinimizeTraceableQuantities(
step,
loss,
gradients,
parameters,
has_converged,
convergence_criterion_state,
optimizer_state,
seed
)
These are (in order):
step: int Tensor index (starting from zero) of the current optimization
step.loss: float Tensor value returned from the user-provided loss_fn.gradients: list of Tensor gradients of loss with respect to the
parameters.parameters: list of Tensor values of parameters being optimized. This
corresponds to trainable_variables passed to minimize, or
init passed to minimize_stateless.has_converged: boolean Tensor of the same shape as loss_fn, with
True values corresponding to loss entries that have converged according
to the user-provided convergence criterion. If no convergence criterion
was specified, this is None.convergence_criterion_state: structure of Tensors containing any
auxiliary state (e.g., moving averages of loss or other quantities)
maintained by the user-provided convergence criterion.optimizer_state: structure of Tensors containing optional state from
a user-provided pure optimizer.
Attributes |
|---|
step
|
A namedtuple alias for field number 0
|
loss
|
A namedtuple alias for field number 1
|
gradients
|
A namedtuple alias for field number 2
|
parameters
|
A namedtuple alias for field number 3
|
has_converged
|
A namedtuple alias for field number 4
|
convergence_criterion_state
|
A namedtuple alias for field number 5
|
optimizer_state
|
A namedtuple alias for field number 6
|
seed
|
A namedtuple alias for field number 7
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21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.substrates.numpy.math.MinimizeTraceableQuantities\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/substrates/numpy/math/minimize.py#L30-L56) |\n\nNamedtuple of quantities that may be traced from [`tfp.math.minimize`](../../../../tfp/math/minimize).\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tfp.experimental.substrates.numpy.math.MinimizeTraceableQuantities`](https://www.tensorflow.org/probability/api_docs/python/tfp/substrates/numpy/math/MinimizeTraceableQuantities)\n\n\u003cbr /\u003e\n\n tfp.substrates.numpy.math.MinimizeTraceableQuantities(\n step,\n loss,\n gradients,\n parameters,\n has_converged,\n convergence_criterion_state,\n optimizer_state,\n seed\n )\n\nThese are (in order):\n\n- `step`: int `Tensor` index (starting from zero) of the current optimization step.\n- `loss`: float `Tensor` value returned from the user-provided `loss_fn`.\n- `gradients`: list of `Tensor` gradients of `loss` with respect to the parameters.\n- `parameters`: list of `Tensor` values of parameters being optimized. This corresponds to `trainable_variables` passed to `minimize`, or `init` passed to `minimize_stateless`.\n- `has_converged`: boolean `Tensor` of the same shape as `loss_fn`, with `True` values corresponding to loss entries that have converged according to the user-provided convergence criterion. If no convergence criterion was specified, this is `None`.\n- `convergence_criterion_state`: structure of `Tensor`s containing any auxiliary state (e.g., moving averages of loss or other quantities) maintained by the user-provided convergence criterion.\n- `optimizer_state`: structure of `Tensor`s containing optional state from a user-provided pure optimizer.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------------------------|-----------------------------------------|\n| `step` | A `namedtuple` alias for field number 0 |\n| `loss` | A `namedtuple` alias for field number 1 |\n| `gradients` | A `namedtuple` alias for field number 2 |\n| `parameters` | A `namedtuple` alias for field number 3 |\n| `has_converged` | A `namedtuple` alias for field number 4 |\n| `convergence_criterion_state` | A `namedtuple` alias for field number 5 |\n| `optimizer_state` | A `namedtuple` alias for field number 6 |\n| `seed` | A `namedtuple` alias for field number 7 |\n\n\u003cbr /\u003e"]]
View source on GitHub