Metric¶
Inheritance Diagram
-
class
ashpy.metrics.metric.
Metric
(name, metric, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.1.3/docs/source/log')[source]¶ Bases:
abc.ABC
Metric is the abstract class that every ash Metric must implement.
AshPy Metrics wrap and extend Keras Metrics.
Methods
__init__
(name, metric[, …])Initialize the Metric object.
json_read
(filename)Read a JSON file.
json_write
(filename, what_to_write)Write inside the specified JSON file the mean and stddev.
log
(step)Log the metric
model_selection
(checkpoint, global_step)Perform model selection.
Reset the state of the metric.
result
()Get the result of the metric.
update_state
(context)Update the internal state of the metric, using the information from the context object.
Attributes
Retrieve the folder used to save the best model when doing model selection.
Retrieve the path to JSON file containing the measured performance of the best model.
Retrieve the log directory.
Retrieve the
tf.keras.metrics.Metric
object.Retrieve the operator used for model selection.
Retrieve the metric name.
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__init__
(name, metric, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.1.3/docs/source/log')[source]¶ Initialize the Metric object.
- Parameters
name (str) – The name of the metric.
metric (
tf.keras.metrics.Metric
) – The Keras metric to use.model_selection_operator (
typing.Callable
) –The operation that will be used when model_selection is triggered to compare the metrics, used by the update_state. Any
typing.Callable
behaving like anoperator
is accepted.Note
Model selection is done ONLY if an model_selection_operator is specified here.
logdir (str) – Path to the log dir, defaults to a log folder in the current directory.
- Return type
None
-
property
best_folder
¶ Retrieve the folder used to save the best model when doing model selection.
- Return type
-
property
best_model_sel_file
¶ Retrieve the path to JSON file containing the measured performance of the best model.
- Return type
-
static
json_read
(filename)[source]¶ Read a JSON file.
- Parameters
filename (str) – The path to the JSON file to read.
- Return type
- Returns
typing.Dict
– Dictionary containing the content of the JSON file.
-
static
json_write
(filename, what_to_write)[source]¶ Write inside the specified JSON file the mean and stddev.
-
property
metric
¶ Retrieve the
tf.keras.metrics.Metric
object.- Return type
Metric
-
model_selection
(checkpoint, global_step)[source]¶ Perform model selection.
- Parameters
checkpoint (
tf.train.Checkpoint
) – Checkpoint object that contains the model status.global_step (
tf.Variable
) – current training step
- Return type
None
-
property
model_selection_operator
¶ Retrieve the operator used for model selection.
-
result
()[source]¶ Get the result of the metric.
- Returns
numpy.ndarray
– The current value of the metric.
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