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Adding add_metric API for layers/models in Keras.

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Supports
- adding metric tensor (both aggregated and not aggregated) on model/layer in graph mode.
- adding metric tensor (both aggregated and not aggregated) in model/layer call in graph and eager mode.

PiperOrigin-RevId: 221865769
This commit is contained in:
Pavithra Vijay 2018-11-16 15:46:27 -08:00 committed by TensorFlower Gardener
parent dd18b138ff
commit 8930d5aff5
256 changed files with 1732 additions and 124 deletions
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5
tensorflow/contrib/saved_model/BUILD
View File

@ -102,7 +102,10 @@ py_test(
size = "medium",
srcs = ["python/saved_model/keras_saved_model_test.py"],
srcs_version = "PY2AND3",
tags = ["no_windows"],
tags = [
"no_oss", # TODO(b/119349471): Re-enable
"no_windows",
],
deps = [
":keras_saved_model",
"//tensorflow/python:client_testlib",

12
tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py
View File

@ -348,12 +348,14 @@ class TestModelSavedModelExport(test.TestCase, parameterized.TestCase):
# feeding in the inputs and targets.
loss, predictions, _ = sess.run(
(outputs['loss'], outputs['predictions/' + output_name],
outputs['metrics/mae/update_op']),
{inputs[input_name]: input_arr, inputs[target_name]: target_arr})
outputs['metrics/mean_absolute_error/update_op']), {
inputs[input_name]: input_arr,
inputs[target_name]: target_arr
})
# The metric value should be run after the update op, to ensure that it
# reflects the correct value.
metric_value = sess.run(outputs['metrics/mae/value'])
metric_value = sess.run(outputs['metrics/mean_absolute_error/value'])
self.assertEqual(int(train_before_export),
sess.run(training_module.get_global_step()))
@ -368,8 +370,8 @@ class TestModelSavedModelExport(test.TestCase, parameterized.TestCase):
self.assertEqual(int(train_before_export),
sess.run(training_module.get_global_step()))
self.assertIn('loss', outputs)
self.assertIn('metrics/mae/update_op', outputs)
self.assertIn('metrics/mae/value', outputs)
self.assertIn('metrics/mean_absolute_error/update_op', outputs)
self.assertIn('metrics/mean_absolute_error/value', outputs)
self.assertIn('predictions/' + output_name, outputs)
# Train for a step

26
tensorflow/contrib/tpu/python/tpu/keras_support.py
View File

@ -1012,9 +1012,10 @@ class TPUFunction(object):
optimizer=_replicated_optimizer(self._cloned_optimizer),
loss=self.model.loss,
loss_weights=self.model.loss_weights,
metrics=metrics_module.clone_metrics(self.model.metrics),
metrics=metrics_module.clone_metrics(
self.model._compile_metrics),
weighted_metrics=metrics_module.clone_metrics(
self.model.weighted_metrics),
self.model._compile_weighted_metrics),
target_tensors=tpu_targets,
)
@ -1376,6 +1377,7 @@ class KerasTPUModel(models.Model):
self.train_function = None
self._fit_function = None
self._eval_function = None
self._stateful_metric_functions = []
cluster_resolver = strategy._tpu_cluster_resolver
self._tpu_name_or_address = cluster_resolver.get_master()
@ -1390,10 +1392,10 @@ class KerasTPUModel(models.Model):
self.compile(
self._cpu_model.optimizer,
self._cpu_model.loss,
self._cpu_model.metrics,
self._cpu_model._compile_metrics,
self._cpu_model.loss_weights,
self._cpu_model.sample_weight_mode,
self._cpu_model.weighted_metrics,
self._cpu_model._compile_weighted_metrics,
self._cpu_model.target_tensors,
)
@ -1697,7 +1699,7 @@ class KerasTPUModel(models.Model):
callbacks.on_train_begin()
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in self.stateful_metric_functions:
for m in self.metrics:
m.reset_states()
# Update callbacks
callbacks.on_epoch_begin(epoch)
@ -1995,14 +1997,14 @@ class KerasTPUModel(models.Model):
self._optimizer = optimizer
@property
def stateful_metric_functions(self):
def metrics(self):
if self._tpu_model:
return self._tpu_model.stateful_metric_functions
return self._tpu_model.metrics
return self._stateful_metric_functions
@stateful_metric_functions.setter
def stateful_metric_functions(self, stateful_metric_functions):
self._stateful_metric_functions = stateful_metric_functions
@metrics.setter
def metrics(self, metrics):
self._stateful_metric_functions = metrics
def _make_train_function(self):
if not self.train_function:
@ -2227,10 +2229,10 @@ def tpu_model(model, strategy=None):
cpu_model.compile(
_clone_optimizer(model.optimizer, optimizer_config),
model.loss,
metrics_module.clone_metrics(model.metrics),
metrics_module.clone_metrics(model._compile_metrics),
model.loss_weights,
model.sample_weight_mode,
metrics_module.clone_metrics(model.weighted_metrics),
metrics_module.clone_metrics(model._compile_weighted_metrics),
)
if model_weights:

2
tensorflow/python/keras/callbacks.py
View File

@ -126,7 +126,7 @@ def configure_callbacks(callbacks,
callback_metrics = []
# When we have deferred build scenario with iterator input, we will compile
# when we standardize first batch of data.
if mode != 'predict' and model._is_compiled: # pylint: disable=protected-access
if mode != 'predict' and hasattr(model, 'metrics_names'):
callback_metrics = copy.copy(model.metrics_names)
if do_validation:
callback_metrics += ['val_' + n for n in model.metrics_names]

93
tensorflow/python/keras/engine/base_layer.py
View File

@ -68,6 +68,14 @@ class CallConvention(enum.Enum):
POSITIONAL_ARGUMENTS_ARE_INPUTS = 3
def _create_mean_metric(value, name=None):
# TODO(psv): Remove this import when b/110718070 is fixed.
from tensorflow.python.keras import metrics as metrics_module # pylint: disable=g-import-not-at-top
metric_obj = metrics_module.Mean(name=name)
result = metric_obj(value)
return metric_obj, result
@tf_export('keras.layers.Layer')
class Layer(checkpointable.CheckpointableBase):
"""Base layer class.
@ -170,6 +178,13 @@ class Layer(checkpointable.CheckpointableBase):
# in eager mode or graph mode alternatively, we need to keep track of
# eager losses and symbolic losses via separate attributes.
self._eager_losses = []
# A list of metric instances corresponding to the symbolic metric tensors
# added using the `add_metric` API.
self._metrics = []
# TODO(psv): Remove this property.
# A dictionary that maps metric names to metric result tensors. The results
# are the running averages of metric values over an epoch.
self._metrics_tensors = {}
self._dtype = None if dtype is None else dtypes.as_dtype(dtype).name
self._call_fn_args = function_utils.fn_args(self.call)
self._compute_previous_mask = ('mask' in self._call_fn_args or
@ -433,6 +448,84 @@ class Layer(checkpointable.CheckpointableBase):
else:
self._losses.append(_tag_unconditional(loss))
@doc_controls.for_subclass_implementers
def add_metric(self, value, aggregation=None, name=None):
"""Adds metric tensor to the layer.
Args:
value: Metric tensor.
aggregation: Sample-wise metric reduction function. If `aggregation=None`,
it indicates that the metric tensor provided has been aggregated
already. eg, `model.add_metric(BinaryAccuracy(name='acc')(y_true,
y_pred))`. If aggregation='mean', the given metric tensor will be
sample-wise reduced using `mean` function. eg, `model.add_metric(
tf.reduce_mean(outputs), name='output_mean', aggregation='mean')`.
name: String metric name.
Raises:
ValueError: If `aggregation` is anything other than None or `mean`.
"""
if aggregation is not None and aggregation != 'mean':
raise ValueError(
'We currently support only `mean` sample-wise metric aggregation. '
'You provided aggregation=`%s`' % aggregation)
if tf_utils.is_symbolic_tensor(value):
self._symbolic_add_metric(value, aggregation, name)
else:
self._eager_add_metric(value, aggregation, name)
def _get_existing_metric(self, name=None):
match = [m for m in self._metrics if m.name == name]
if not match:
return
if len(match) > 1:
raise ValueError(
'Please provide different names for the metrics you have added. '
'We found {} metrics with the name: "{}"'.format(len(match), name))
return match[0]
def _eager_add_metric(self, value, aggregation=None, name=None):
# If the given metric is available in `metrics` list we just update state
# on it, otherwise we create a new metric instance and
# add it to the `metrics` list.
match = self._get_existing_metric(name)
if match:
match(value) # Update the metric state.
return
else:
if aggregation is None:
raise ValueError('We do not support adding an aggregated metric tensor '
'in `call` in eager execution.')
metric_obj, _ = _create_mean_metric(value, name)
self._metrics.append(metric_obj)
def _symbolic_add_metric(self, value, aggregation=None, name=None):
if aggregation is None:
# Iterate over the metrics and check if the given metric exists already.
# This can happen when a metric instance is created in subclassed model
# layer `__init__` and we have tracked that instance already in
# model.__setattr__.
match = self._get_existing_metric(name)
if match:
result_tensor = value
if match.name not in self._metrics_tensors:
self._metrics_tensors[match.name] = result_tensor
return
else:
raise ValueError(
'We currently do not support reusing a metric instance.')
else:
# We track the instance using the metadata on the result tensor.
result_tensor = value
metric_obj = result_tensor._metric_obj
else:
# If a non-aggregated tensor is given as input (ie. `aggregation` is
# explicitly set to `mean`), we wrap the tensor in `Mean` metric.
metric_obj, result_tensor = _create_mean_metric(value, name)
self._metrics.append(metric_obj)
self._metrics_tensors[metric_obj.name] = result_tensor
def get_losses_for(self, inputs):
"""Retrieves losses relevant to a specific set of inputs.

36
tensorflow/python/keras/engine/network.py
View File

@ -134,6 +134,11 @@ class Network(base_layer.Layer):
self._updates = [] # Used in symbolic mode only.
self._losses = []
self._eager_losses = []
# A list of metric instances corresponding to the symbolic metric tensors
# added using the `add_metric` API.
self._metrics = []
# A dictionary that maps metric names to metric result tensors.
self._metrics_tensors = {}
self._scope = None # Never used.
self._reuse = None # Never used.
self._call_is_graph_friendly = True
@ -412,6 +417,13 @@ class Network(base_layer.Layer):
else:
if value not in self._non_trainable_weights:
self._non_trainable_weights.append(value)
# Keeping track of metric instance created in subclassed model/layer.
# We do this so that we can maintain the correct order of metrics by adding
# the instance to the `metrics` list as soon as it is created.
from tensorflow.python.keras import metrics as metrics_module # pylint: disable=g-import-not-at-top
if isinstance(value, metrics_module.Metric):
self._metrics.append(value)
super(Network, self).__setattr__(name, value)
@property
@ -697,6 +709,30 @@ class Network(base_layer.Layer):
sub_layers=self._layers,
extra_variables=self._non_trainable_weights + self._trainable_weights)
@property
def metrics(self):
"""Returns the network's symbolic metrics.
Model overrides this function to include the metrics from `compile` API.
"""
metrics = []
for layer in self.layers:
metrics += layer._metrics # pylint: disable=protected-access
return metrics + self._metrics
@property
def _all_metrics_tensors(self):
"""Returns the network's symbolic metric tensors."""
# TODO(psv): Remove this property.
metrics_tensors = {}
for layer in self.layers:
if isinstance(layer, Network):
metrics_tensors.update(layer._all_metrics_tensors)
else:
metrics_tensors.update(layer._metrics_tensors)
metrics_tensors.update(self._metrics_tensors)
return metrics_tensors
@property
def input_spec(self):
"""Gets the network's input specs.

8
tensorflow/python/keras/engine/saving.py
View File

@ -79,6 +79,10 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
from tensorflow.python.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
# TODO(psv) Add warning when we save models that contain non-serializable
# entities like metrics added using `add_metric` and losses added using
# `add_loss.`
if not isinstance(filepath, h5py.File):
# If file exists and should not be overwritten.
if not overwrite and os.path.isfile(filepath):
@ -126,8 +130,8 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True):
'config': model.optimizer.get_config()
},
'loss': model.loss,
'metrics': model.metrics,
'weighted_metrics': model.weighted_metrics,
'metrics': model._compile_metrics,
'weighted_metrics': model._compile_weighted_metrics,
'sample_weight_mode': model.sample_weight_mode,
'loss_weights': model.loss_weights,
},

171
tensorflow/python/keras/engine/training.py
View File

@ -18,6 +18,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import weakref
import numpy as np
@ -174,25 +175,66 @@ class Model(Network):
metric_name = '%s_%s' % (self.output_names[output_index], metric_name)
j = 1
base_metric_name = metric_name
while metric_name in self.metrics_names:
while metric_name in self._compile_metrics_names:
metric_name = '%s_%d' % (base_metric_name, j)
j += 1
return metric_name
@property
def metrics(self):
"""Returns the model's metrics added using `compile`, `add_metric` APIs."""
metrics = []
if self._is_compiled:
metrics += self._compile_stateful_metric_functions
return metrics + super(Model, self).metrics
@property
def metrics_names(self):
"""Returns the model's display labels for all outputs."""
metrics_names = []
if self._is_compiled:
metrics_names += self._compile_metrics_names # Includes names of losses.
# Add metric names from layers.
for layer in self.layers:
metrics_names += [m.name for m in layer._metrics] # pylint: disable=protected-access
metrics_names += [m.name for m in self._metrics]
return metrics_names
@property
def _all_metrics_tensors(self):
"""Returns the network's symbolic metric tensors."""
metrics_tensors = {}
if self._is_compiled:
metrics_tensors.update(self._compile_metrics_tensors)
metrics_tensors.update(super(Model, self)._all_metrics_tensors)
return metrics_tensors
@property
def _all_stateful_metrics_tensors(self):
"""Returns the network's symbolic metric tensors."""
metrics_tensors = {}
if self._is_compiled:
metrics_tensors.update(self._compile_stateful_metrics_tensors)
metrics_tensors.update(super(Model, self)._all_metrics_tensors)
return metrics_tensors
def _init_metric_attributes(self):
"""Initialized model metric attributes."""
# List of all metric names in the model.
self.metrics_names = ['loss']
# List of all aggregated metric result tensors. This includes aggregated
# loss result tensors.
self._stateful_metrics_tensors = []
# List of all metric result tensors (aggregated or not - based on the
# values given in compile.)
self.metrics_tensors = []
self._compile_metrics_names = ['loss']
# List of stateful metric functions. Used for resetting metric state during
# training/eval. This includes loss functions.
self.stateful_metric_functions = []
# training/eval.
# This includes loss functions when there are multiple outputs.
self._compile_stateful_metric_functions = []
# Dict of all aggregated metric result tensors. This includes aggregated
# loss result tensors when there are multiple outputs.
self._compile_stateful_metrics_tensors = {}
# Dict of all metric result tensors (aggregated or not - based on the
# values given in compile.). This includes aggregated loss result tensors
# when there are multiple outputs.
self._compile_metrics_tensors = {}
def _set_per_output_metric_attributes(self, metrics_dict, output_index):
"""Sets the metric attributes on the model for the given output.
@ -201,24 +243,39 @@ class Model(Network):
metrics_dict: A dict with metric names as keys and metric fns as values.
output_index: The index of the model output for which the metric
attributes are added.
"""
for metric_name, (_, stateful_metric_fn) in metrics_dict.items():
metric_name = self._add_unique_metric_name(metric_name, output_index)
# Keep track of metric name.
self.metrics_names.append(metric_name)
# Keep track of stateful metric function.
self.stateful_metric_functions.append(stateful_metric_fn)
Returns:
Metrics dict updated with unique metric names as keys.
"""
updated_metrics_dict = collections.OrderedDict()
for metric_name, (metric_fn, stateful_metric_fn) in metrics_dict.items():
metric_name = self._add_unique_metric_name(metric_name, output_index)
updated_metrics_dict[metric_name] = (metric_fn, stateful_metric_fn)
# Keep track of metric name, function and stateful function.
self._compile_metrics_names.append(metric_name)
self._compile_stateful_metric_functions.append(stateful_metric_fn)
return updated_metrics_dict
def _set_metric_attributes(self, outputs, skip_target_indices=None):
"""Sets the metric attributes on the model for all the model outputs."""
skip_target_indices = skip_target_indices or []
updated_per_output_metrics = []
updated_per_output_weighted_metrics = []
for i in range(len(outputs)):
if i in skip_target_indices:
updated_per_output_metrics.append(self._per_output_metrics[i])
updated_per_output_weighted_metrics.append(
self._per_output_weighted_metrics[i])
continue
self._set_per_output_metric_attributes(self._per_output_metrics[i], i)
self._set_per_output_metric_attributes(
self._per_output_weighted_metrics[i], i)
updated_per_output_metrics.append(
self._set_per_output_metric_attributes(self._per_output_metrics[i],
i))
updated_per_output_weighted_metrics.append(
self._set_per_output_metric_attributes(
self._per_output_weighted_metrics[i], i))
self._per_output_metrics = updated_per_output_metrics
self._per_output_weighted_metrics = updated_per_output_weighted_metrics
def _handle_per_output_metrics(self,
metrics_dict,
@ -253,16 +310,16 @@ class Model(Network):
weighted_metric_fn = training_utils.weighted_masked_objective(fn)
return weighted_metric_fn(y_true, y_pred, weights=weights, mask=mask)
def _track_metric_tensors(stateless_result, stateful_result):
self.metrics_tensors.append(stateless_result)
self._stateful_metrics_tensors.append(stateful_result)
def _track_metric_tensors(name, stateless_result, stateful_result):
self._compile_metrics_tensors[name] = stateless_result
self._compile_stateful_metrics_tensors[name] = stateful_result
if isinstance(metric_fn, metrics_module.Metric):
# If the given metric fn is stateful, call the fn and return result.
metric_result = _call_stateful_fn(metric_fn)
metric_results.append(metric_result)
if not self.run_eagerly:
_track_metric_tensors(metric_result, metric_result)
_track_metric_tensors(metric_name, metric_result, metric_result)
elif self.run_eagerly:
# In eager mode, if the given metric fn is not stateful, we invoke the
# given fn or its stateful version based on the given flag.
@ -276,7 +333,8 @@ class Model(Network):
# stateless fns.
stateful_metric_result = _call_stateful_fn(stateful_fn)
metric_result = _call_stateless_fn(metric_fn)
_track_metric_tensors(metric_result, stateful_metric_result)
_track_metric_tensors(metric_name, metric_result,
stateful_metric_result)
return metric_results
@ -304,6 +362,7 @@ class Model(Network):
skip_target_indices = skip_target_indices or []
metric_results = []
with K.name_scope('metrics'):
# Invoke all metrics added using `compile`.
for i in range(len(outputs)):
if i in skip_target_indices:
continue
@ -325,6 +384,12 @@ class Model(Network):
output_mask,
weights=sample_weights[i],
return_stateful_result=return_stateful_result))
# Add metric results from the `add_metric` metrics in eager mode.
if context.executing_eagerly():
for m in self.metrics:
if m not in self._compile_stateful_metric_functions:
metric_results.append(m.result())
return metric_results
@property
@ -461,10 +526,10 @@ class Model(Network):
self._track_checkpointable(
self.optimizer, name='optimizer', overwrite=True)
self.loss = loss
self.metrics = metrics or []
self._compile_metrics = metrics or []
self.loss_weights = loss_weights
self.sample_weight_mode = sample_weight_mode
self.weighted_metrics = weighted_metrics
self._compile_weighted_metrics = weighted_metrics
if self.run_eagerly and target_tensors is not None:
raise ValueError(
'target_tensors argument is not supported when '
@ -573,7 +638,7 @@ class Model(Network):
self.total_loss = None
for i in range(len(self.outputs)):
if len(self.outputs) > 1:
self.metrics_names.append(self.output_names[i] + '_loss')
self._compile_metrics_names.append(self.output_names[i] + '_loss')
# Set metric attributes on model.
self._set_metric_attributes(
@ -666,7 +731,8 @@ class Model(Network):
if len(self.outputs) > 1:
# Keep track of the un-aggregated loss result tensor.
self.metrics_tensors.append(output_loss)
self._compile_metrics_tensors[self.output_names[i] +
'_loss'] = output_loss
# Keep track of stateful result tensor and function for the loss.
mean_wrapped_loss = metrics_module.MeanMetricWrapper(
@ -677,10 +743,11 @@ class Model(Network):
y_pred,
weights=sample_weight,
mask=mask)
self._stateful_metrics_tensors.append(result_tensor)
self.stateful_metric_functions.append(mean_wrapped_loss)
self._compile_stateful_metrics_tensors[self.output_names[i] +
'_loss'] = result_tensor
self._compile_stateful_metric_functions.append(mean_wrapped_loss)
self.metrics_names.append(self.output_names[i] + '_loss')
self._compile_metrics_names.append(self.output_names[i] + '_loss')
if total_loss is None:
total_loss = loss_weight * output_loss
else:
@ -782,18 +849,24 @@ class Model(Network):
setattr(self, fn_name, fn)
def _make_train_function(self):
metrics_tensors = [
self._all_metrics_tensors[m] for m in self.metrics_names[1:]
]
self._make_train_function_helper('train_function',
[self.total_loss] + self.metrics_tensors)
[self.total_loss] + metrics_tensors)
def _make_fit_function(self):
# TODO(psv/anjalisridhar): Remove updates after we fix b/118841692
# Stateful metrics updates
metric_updates = []
for m in self.stateful_metric_functions:
for m in self.metrics:
metric_updates += m.updates
metrics_tensors = [
self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:]
]
self._make_train_function_helper(
'_fit_function', [self.total_loss] + self._stateful_metrics_tensors,
metric_updates)
'_fit_function', [self.total_loss] + metrics_tensors, metric_updates)
def _make_test_function_helper(self, fn_name, outputs, metric_updates=None):
if not hasattr(self, fn_name):
@ -819,12 +892,18 @@ class Model(Network):
setattr(self, fn_name, fn)
def _make_test_function(self):
metrics_tensors = [
self._all_metrics_tensors[m] for m in self.metrics_names[1:]
]
self._make_test_function_helper('test_function',
[self.total_loss] + self.metrics_tensors)
[self.total_loss] + metrics_tensors)
def _make_eval_function(self):
self._make_test_function_helper(
'_eval_function', [self.total_loss] + self._stateful_metrics_tensors)
metrics_tensors = [
self._all_stateful_metrics_tensors[m] for m in self.metrics_names[1:]
]
self._make_test_function_helper('_eval_function',
[self.total_loss] + metrics_tensors)
def _make_predict_function(self):
if not hasattr(self, 'predict_function'):
@ -1202,12 +1281,14 @@ class Model(Network):
y = [y]
target_tensors = [v for v in y if tensor_util.is_tensor(v)]
is_compile_called = True
self.compile(optimizer=self.optimizer,
loss=self.loss,
metrics=self.metrics,
loss_weights=self.loss_weights,
target_tensors=target_tensors,
run_eagerly=self.run_eagerly)
self.compile(
optimizer=self.optimizer,
loss=self.loss,
metrics=self._compile_metrics,
weighted_metrics=self._compile_weighted_metrics,
loss_weights=self.loss_weights,
target_tensors=target_tensors,
run_eagerly=self.run_eagerly)
# In graph mode, if we had just set inputs and targets as symbolic tensors
# by invoking build and compile on the model respectively, we do not have to

4
tensorflow/python/keras/engine/training_arrays.py
View File

@ -299,8 +299,8 @@ def model_iteration(model,
# Setup work for each epoch
results = []
epoch_logs = {}
if hasattr(model, 'stateful_metric_functions'):
for m in model.stateful_metric_functions:
if hasattr(model, 'metrics'):
for m in model.metrics:
m.reset_states()
callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode)
progbar.on_epoch_begin(epoch, epoch_logs)

15
tensorflow/python/keras/engine/training_distributed.py
View File

@ -171,7 +171,7 @@ def fit_loop(
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.stateful_metric_functions:
for m in model.metrics:
m.reset_states()
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
@ -311,7 +311,8 @@ def _experimental_fit_loop(
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
if steps_per_epoch is None:
@ -480,7 +481,7 @@ def test_loop(model, iterator, verbose=0, steps=None):
len(model.outputs) * current_strategy.num_replicas_in_sync)]
ins = dataset_inputs + dataset_targets + sample_weights
for m in model.stateful_metric_functions:
for m in model.metrics:
m.reset_states()
outs = []
@ -590,7 +591,8 @@ def _experimental_test_loop(model, iterator, verbose=0, steps=None,
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
with current_strategy.scope():
@ -867,10 +869,11 @@ def _clone_and_build_model(model, inputs=None, targets=None):
cloned_model.compile(
optimizer,
model.loss,
metrics=metrics_module.clone_metrics(model.metrics),
metrics=metrics_module.clone_metrics(model._compile_metrics),
loss_weights=model.loss_weights,
sample_weight_mode=model.sample_weight_mode,
weighted_metrics=metrics_module.clone_metrics(model.weighted_metrics),
weighted_metrics=metrics_module.clone_metrics(
model._compile_weighted_metrics),
target_tensors=targets)
return cloned_model

33
tensorflow/python/keras/engine/training_eager.py
View File

@ -254,16 +254,25 @@ def iterator_fit_loop(model,
if val is not None else None for val in sample_weights
]
# Set stateful_metrics in callbacks. We do not do this before the
# `steps_per_epoch` loop because model will be compiled only in the first
# iteration of this loop in the deferred build scenario.
# Train model.
outs, loss, _, aggregated_loss_metrics, masks = _process_single_batch(
model,
x,
y,
output_loss_metrics=output_loss_metrics,
sample_weights=sample_weights,
training=True)
outs = generic_utils.to_list(outs)
if step_index == 0:
# Set stateful_metrics in callbacks. We do not do this before the
# `steps_per_epoch` loop because model will be compiled only in the first
# iteration of this loop in the deferred build scenario.
for cbk in callbacks:
if (isinstance(cbk, cbks.BaseLogger) or
isinstance(cbk, cbks.ProgbarLogger)):
cbk.stateful_metrics = model.metrics_names[1:] # Exclude `loss`
if step_index == 0 and not callbacks.params['metrics']:
callback_metrics = copy.copy(model.metrics_names)
if do_validation:
callback_metrics += ['val_' + n for n in model.metrics_names]
@ -277,16 +286,6 @@ def iterator_fit_loop(model,
'validation_steps': validation_steps
})
# Train model.
outs, loss, _, aggregated_loss_metrics, masks = _process_single_batch(
model,
x,
y,
output_loss_metrics=output_loss_metrics,
sample_weights=sample_weights,
training=True)
outs = generic_utils.to_list(outs)
# Calculate metrics.
for l, o in zip(model.metrics_names, outs):
batch_logs[l] = o
@ -392,8 +391,8 @@ def iterator_test_loop(model, inputs, steps, verbose=0):
# Get stateful metrics indices. We do not do this before the `steps` loop
# because model will be compiled only in the first iteration of this loop
# in the deferred build scenario.
if hasattr(model, 'metrics'):
for m in model.stateful_metric_functions:
if hasattr(model, '_compile_metrics'):
for m in model.metrics:
m.reset_states()
for m in output_loss_metrics:
m.reset_states()
@ -750,7 +749,7 @@ def fit_loop(model,
for epoch in range(initial_epoch, epochs):
if model._is_compiled: # Model may not be compiled the first time.
# Reset stateful metrics
for m in model.stateful_metric_functions:
for m in model.metrics:
m.reset_states()
for m in output_loss_metrics:

6
tensorflow/python/keras/engine/training_generator.py
View File

@ -137,7 +137,7 @@ def fit_generator(model,
# Construct epoch logs.
epoch_logs = {}
while epoch < epochs:
for m in model.stateful_metric_functions:
for m in model.metrics:
m.reset_states()
callbacks.on_epoch_begin(epoch)
steps_done = 0
@ -240,8 +240,8 @@ def evaluate_generator(model,
if not context.executing_eagerly():
model._make_test_function()
if hasattr(model, 'metrics'):
for m in model.stateful_metric_functions:
if hasattr(model, '_compile_metrics'):
for m in model.metrics:
m.reset_states()
steps_done = 0

361
tensorflow/python/keras/engine/training_test.py
View File

@ -37,6 +37,7 @@ from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.callbacks import Callback
from tensorflow.python.keras.engine.training_utils import weighted_masked_objective
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import test
@ -574,6 +575,31 @@ class TrainingTest(test.TestCase):
# Test with eager execution and iterator
model.fit(iterator, epochs=1, steps_per_epoch=2)
def test_losses_in_defun(self):
with context.eager_mode():
layer = keras.layers.Dense(1, kernel_regularizer='l1')
layer(array_ops.ones([1, 10]))
@function.defun
def get_losses():
return layer.losses
self.assertAllEqual(
self.evaluate(layer.losses), self.evaluate(get_losses()))
@tf_test_util.run_in_graph_and_eager_modes
def test_logging(self):
mock_stdout = io.BytesIO() if six.PY2 else io.StringIO()
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(
RMSPropOptimizer(learning_rate=0.001), loss='binary_crossentropy')
with test.mock.patch.object(sys, 'stdout', mock_stdout):
model.fit(
np.ones((10, 10), 'float32'), np.ones((10, 1), 'float32'), epochs=10)
self.assertTrue('Epoch 5/10' in mock_stdout.getvalue())
class TestExceptionsAndWarnings(test.TestCase):
@ -2248,30 +2274,327 @@ class TestTrainingWithMetrics(test.TestCase):
scores = model.train_on_batch(x, y, sample_weight=w)
self.assertArrayNear(scores, [0.2, 0.8], 0.1)
def test_add_metric_with_tensor_on_model_in_graph_mode(self):
with self.cached_session():
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(1, kernel_initializer='ones')(x)
model = keras.models.Model(x, y)
model.add_metric(
math_ops.reduce_sum(y), name='metric_1', aggregation='mean')
# test with a metric which does not have the standard signature:
# (y_true, y_pred, sample_Weight)
model.add_metric(metrics_module.Mean(name='metric_2')(y))
model.compile('sgd', loss='mse')
inputs = np.ones(shape=(10, 1))
targets = np.ones(shape=(10, 1))
history = model.fit(
inputs,
targets,
epochs=2,
batch_size=5,
validation_data=(inputs, targets))
self.assertEqual(history.history['metric_1'][-1], 5)
self.assertEqual(history.history['metric_2'][-1], 1)
self.assertEqual(history.history['val_metric_1'][-1], 5)
self.assertEqual(history.history['val_metric_2'][-1], 1)
eval_results = model.evaluate(inputs, targets, batch_size=5)
self.assertEqual(eval_results[-1], 1)
self.assertEqual(eval_results[-2], 5)
model.predict(inputs, batch_size=5)
model.train_on_batch(inputs, targets)
model.test_on_batch(inputs, targets)
@tf_test_util.run_in_graph_and_eager_modes
def test_logging(self):
mock_stdout = io.BytesIO() if six.PY2 else io.StringIO()
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(
RMSPropOptimizer(learning_rate=0.001), loss='binary_crossentropy')
with test.mock.patch.object(sys, 'stdout', mock_stdout):
model.fit(
np.ones((10, 10), 'float32'), np.ones((10, 1), 'float32'), epochs=10)
self.assertTrue('Epoch 5/10' in mock_stdout.getvalue())
def test_add_metric_in_model_call(self):
def test_losses_in_defun(self):
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
self.mean = metrics_module.Mean(name='metric_1')
def call(self, x):
self.add_metric(
math_ops.reduce_sum(x), name='metric_2', aggregation='mean')
# Provide same name as in the instance created in __init__
# for eager mode
self.add_metric(self.mean(x), name='metric_1')
return self.dense1(x)
model = TestModel()
model.compile(loss='mse', optimizer=RMSPropOptimizer(0.01))
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
history = model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertAlmostEqual(history.history['metric_1'][-1], 1, 0)
self.assertAlmostEqual(history.history['val_metric_1'][-1], 1, 0)
self.assertAlmostEqual(history.history['metric_2'][-1], 5, 0)
self.assertAlmostEqual(history.history['val_metric_2'][-1], 5, 0)
eval_results = model.evaluate(x, y, batch_size=5)
self.assertAlmostEqual(eval_results[1], 1, 0)
self.assertAlmostEqual(eval_results[2], 5, 0)
model.predict(x, batch_size=5)
model.train_on_batch(x, y)
model.test_on_batch(x, y)
def test_add_metric_in_model_call_run_eagerly(self):
with context.eager_mode():
layer = keras.layers.Dense(1, kernel_regularizer='l1')
layer(array_ops.ones([1, 10]))
@function.defun
def get_losses():
return layer.losses
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
self.mean = metrics_module.Mean(name='metric_1')
def call(self, x):
self.add_metric(
math_ops.reduce_sum(x), name='metric_2', aggregation='mean')
# Provide same name as in the instance created in __init__
# for eager mode
self.add_metric(self.mean(x), name='metric_1')
return self.dense1(x)
model = TestModel()
model.compile(
loss='mse', optimizer=RMSPropOptimizer(0.01), run_eagerly=True)
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
history = model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertAlmostEqual(history.history['metric_1'][-1], 1, 0)
self.assertAlmostEqual(history.history['val_metric_1'][-1], 1, 0)
self.assertAlmostEqual(history.history['metric_2'][-1], 5, 0)
self.assertAlmostEqual(history.history['val_metric_2'][-1], 5, 0)
eval_results = model.evaluate(x, y, batch_size=5)
self.assertAlmostEqual(eval_results[1], 1, 0)
self.assertAlmostEqual(eval_results[2], 5, 0)
model.predict(x, batch_size=5)
model.train_on_batch(x, y)
model.test_on_batch(x, y)
@tf_test_util.run_in_graph_and_eager_modes
def test_add_metric_in_layer_call(self):
class TestLayer(keras.layers.Layer):
def build(self, input_shape):
self.a = self.add_variable(
'a', (1, 1), initializer='ones', trainable=False)
self.built = True
def call(self, inputs):
self.add_metric(
math_ops.reduce_sum(inputs), name='metric_1', aggregation='mean')
return inputs + 1
model = keras.Sequential()
model.add(TestLayer(input_shape=(1,)))
model.add(keras.layers.Dense(2, kernel_initializer='ones'))
model.compile(loss='mse', optimizer=RMSPropOptimizer(0.01))
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
history = model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertEqual(history.history['metric_1'][-1], 5)
self.assertAlmostEqual(history.history['val_metric_1'][-1], 5, 0)
def test_add_metric_in_layer_call_run_eagerly(self):
with context.eager_mode():
class TestLayer(keras.layers.Layer):
def build(self, input_shape):
self.a = self.add_variable(
'a', (1, 1), initializer='ones', trainable=False)
self.built = True
def call(self, inputs):
self.add_metric(
math_ops.reduce_sum(inputs), name='metric_1', aggregation='mean')
return inputs + 1
model = keras.Sequential()
model.add(TestLayer(input_shape=(1,)))
model.add(keras.layers.Dense(2, kernel_initializer='ones'))
model.compile(
loss='mse', optimizer=RMSPropOptimizer(0.01), run_eagerly=True)
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
history = model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertEqual(history.history['metric_1'][-1], 5)
self.assertAlmostEqual(history.history['val_metric_1'][-1], 5, 0)
def test_model_metrics_list(self):
with self.cached_session():
x = keras.layers.Input(shape=(1,))
y = keras.layers.Dense(1, kernel_initializer='ones')(x)
model = keras.models.Model(x, y)
model.add_metric(
math_ops.reduce_sum(y), name='metric_1', aggregation='mean')
model.add_metric(metrics_module.Mean(name='metric_2')(y))
model.compile('sgd', loss='mse', metrics=['acc'])
# Verify that the metrics added using `compile` and `add_metric` API are
# included
self.assertEqual(model._compile_metrics, ['acc'])
names = []
for m in model.metrics:
if isinstance(m, metrics_module.Metric):
names.append(m.name)
else:
names.append(m.__name__)
self.assertEqual(names, ['binary_accuracy', 'metric_1', 'metric_2'])
def test_model_eager_metrics_list(self):
with context.eager_mode():
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
def call(self, x):
self.add_metric(
math_ops.reduce_sum(x), name='metric_1', aggregation='mean')
return self.dense1(x)
model = TestModel()
model.compile(
loss='mse',
optimizer=RMSPropOptimizer(0.01),
metrics=['acc'],
run_eagerly=True)
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertEqual(model._compile_metrics, ['acc'])
names = []
for m in model.metrics:
if isinstance(m, metrics_module.Metric):
names.append(m.name)
else:
names.append(m.__name__)
self.assertEqual(names, ['categorical_accuracy', 'metric_1'])
@tf_test_util.run_in_graph_and_eager_modes
def test_multiple_add_metric_calls(self):
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
self.mean1 = metrics_module.Mean(name='metric_1')
self.mean2 = metrics_module.Mean(name='metric_2')
def call(self, x):
self.add_metric(self.mean2(x), name='metric_2')
self.add_metric(self.mean1(x), name='metric_1')
self.add_metric(
math_ops.reduce_sum(x), name='metric_3', aggregation='mean')
return self.dense1(x)
model = TestModel()
model.compile(loss='mse', optimizer=RMSPropOptimizer(0.01))
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
history = model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertAlmostEqual(history.history['metric_1'][-1], 1, 0)
self.assertAlmostEqual(history.history['metric_2'][-1], 1, 0)
self.assertAlmostEqual(history.history['metric_3'][-1], 5, 0)
eval_results = model.evaluate(x, y, batch_size=5)
self.assertArrayNear(eval_results[1:4], [1, 1, 5], 0.1)
model.predict(x, batch_size=5)
model.train_on_batch(x, y)
model.test_on_batch(x, y)
def test_invalid_metric_tensor_in_call(self):
with context.eager_mode():
class TestLayer(keras.layers.Layer):
def call(self, inputs):
self.add_metric(metrics_module.Mean(name='metric_1')(inputs))
return inputs + 1
model = keras.Sequential()
model.add(TestLayer(input_shape=(1,)))
model.add(keras.layers.Dense(2, kernel_initializer='ones'))
model.compile(
loss='mse', optimizer=RMSPropOptimizer(0.01), run_eagerly=True)
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
with self.assertRaisesRegexp(
ValueError,
'We do not support adding an aggregated metric tensor in `call` in '
'eager execution.'):
model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
@tf_test_util.run_in_graph_and_eager_modes
def test_duplicate_metric_name_in_add_metric(self):
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
self.mean = metrics_module.Mean(name='metric_1')
self.mean2 = metrics_module.Mean(name='metric_1')
def call(self, x):
self.add_metric(self.mean(x), name='metric_1')
return self.dense1(x)
model = TestModel()
model.compile(loss='mse', optimizer=RMSPropOptimizer(0.01))
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
with self.assertRaisesRegexp(
ValueError,
'Please provide different names for the metrics you have added. '
'We found 2 metrics with the name: "metric_1"'):
model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
@tf_test_util.run_in_graph_and_eager_modes
def test_multiple_no_name_input_to_add_metric(self):
class TestModel(keras.Model):
def __init__(self):
super(TestModel, self).__init__(name='test_model')
self.dense1 = keras.layers.Dense(2, kernel_initializer='ones')
def call(self, x):
self.add_metric(math_ops.reduce_sum(x), aggregation='mean')
self.add_metric(math_ops.reduce_sum(x), aggregation='mean')
return self.dense1(x)
model = TestModel()
model.compile(loss='mse', optimizer=RMSPropOptimizer(0.01))
x = np.ones(shape=(10, 1))
y = np.ones(shape=(10, 2))
model.fit(x, y, epochs=2, batch_size=5, validation_data=(x, y))
self.assertEqual([m.name for m in model.metrics], ['mean', 'mean_1'])
self.assertAllEqual(self.evaluate(layer.losses),
self.evaluate(get_losses()))
if __name__ == '__main__':
test.main()

36
tensorflow/python/keras/metrics.py
View File

@ -545,9 +545,20 @@ class Metric(Layer):
Returns:
The metric value tensor.
"""
update_op = self.update_state(*args, **kwargs) # pylint: disable=not-callable
update_op = self.update_state(*args, **kwargs)
with ops.control_dependencies([update_op]):
return self.result() # pylint: disable=not-callable
result_t = self.result()
# We are adding the metric object as metadata on the result tensor.
# This is required when we want to use a metric with `add_metric` API on
# a Model/Layer in graph mode. This metric instance will later be used
# to reset variable state after each epoch of training.
# Example:
# model = Model()
# model.add_metric(Mean()(values), name='mean')
if not context.executing_eagerly():
result_t._metric_obj = self # pylint: disable=protected-access
return result_t
def reset_states(self):
"""Resets all of the metric state variables.
@ -731,7 +742,8 @@ class MeanMetricWrapper(Mean):
matches, sample_weight=sample_weight)
def get_config(self):
config = self._fn_kwargs
config = {'fn': self._fn}
config.update(self._fn_kwargs)
base_config = super(MeanMetricWrapper, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@ -760,6 +772,12 @@ class BinaryAccuracy(MeanMetricWrapper):
super(BinaryAccuracy, self).__init__(
binary_accuracy, name, dtype=dtype, threshold=threshold)
@classmethod
def from_config(cls, config):
if 'fn' in config:
config.pop('fn')
return super(BinaryAccuracy, cls).from_config(config)
class CategoricalAccuracy(MeanMetricWrapper):
"""Calculates how often predictions matches labels.
@ -783,6 +801,12 @@ class CategoricalAccuracy(MeanMetricWrapper):
super(CategoricalAccuracy, self).__init__(
categorical_accuracy, name, dtype=dtype)
@classmethod
def from_config(cls, config):
if 'fn' in config:
config.pop('fn')
return super(CategoricalAccuracy, cls).from_config(config)
class SparseCategoricalAccuracy(MeanMetricWrapper):
"""Calculates how often predictions matches integer labels.
@ -800,6 +824,12 @@ class SparseCategoricalAccuracy(MeanMetricWrapper):
super(SparseCategoricalAccuracy, self).__init__(
sparse_categorical_accuracy, name, dtype=dtype)
@classmethod
def from_config(cls, config):
if 'fn' in config:
config.pop('fn')
return super(SparseCategoricalAccuracy, cls).from_config(config)
class _ConfusionMatrixConditionCount(Metric):
"""Calculates the number of the given confusion matrix condition."""

13
tensorflow/python/keras/models.py
View File

@ -304,8 +304,9 @@ def _in_place_subclassed_model_reset(model):
attributes_cache[name] = value
assert value in model._layers
elif isinstance(
value, (list, tuple)) and name not in ('layers', '_layers',
'stateful_metric_functions'):
value,
(list, tuple)) and name not in ('layers', '_layers', 'metrics',
'_compile_stateful_metric_functions'):
# Handle case: list/tuple of layers (also tracked by the Network API).
if value and all(isinstance(val, Layer) for val in value):
raise ValueError('We do not support the use of list-of-layers '
@ -345,9 +346,6 @@ def _in_place_subclassed_model_reset(model):
'targets',
'_feed_targets',
'sample_weight_modes',
'weighted_metrics',
'metrics_names',
'metrics_tensors',
'total_loss',
'sample_weights',
'_feed_sample_weights',
@ -495,10 +493,11 @@ def clone_and_build_model(
clone.compile(
optimizer,
model.loss,
metrics=metrics_module.clone_metrics(model.metrics),
metrics=metrics_module.clone_metrics(model._compile_metrics),
loss_weights=model.loss_weights,
sample_weight_mode=model.sample_weight_mode,
weighted_metrics=metrics_module.clone_metrics(model.weighted_metrics),
weighted_metrics=metrics_module.clone_metrics(
model._compile_weighted_metrics),
target_tensors=target_tensors)
return clone

3
tensorflow/python/keras/models_test.py
View File

@ -331,7 +331,8 @@ class TestCloneAndBuildModel(test.TestCase):
self.assertEqual('mse', model.loss)
self.assertTrue(
isinstance(model.optimizer, keras.optimizers.RMSprop))
self.assertEqual(['acc', metrics.categorical_accuracy], model.metrics)
self.assertEqual(['acc', metrics.categorical_accuracy],
model._compile_metrics)
def _clone_and_build_test_helper(self, model, is_subclassed=False):
inp = np.random.random((10, 4))

12
tensorflow/tools/api/golden/v1/tensorflow.keras.-model.pbtxt
View File

@ -41,6 +41,14 @@ tf_class {
name: "losses"
mtype: "<type \'property\'>"
}
member {
name: "metrics"
mtype: "<type \'property\'>"
}
member {
name: "metrics_names"
mtype: "<type \'property\'>"
}
member {
name: "name"
mtype: "<type \'property\'>"
@ -109,6 +117,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

12
tensorflow/tools/api/golden/v1/tensorflow.keras.-sequential.pbtxt
View File

@ -42,6 +42,14 @@ tf_class {
name: "losses"
mtype: "<type \'property\'>"
}
member {
name: "metrics"
mtype: "<type \'property\'>"
}
member {
name: "metrics_names"
mtype: "<type \'property\'>"
}
member {
name: "name"
mtype: "<type \'property\'>"
@ -114,6 +122,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.experimental.-peephole-l-s-t-m-cell.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-activation.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-activity-regularization.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-add.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-alpha-dropout.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-average-pooling1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-average-pooling2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-average-pooling3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-average.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-avg-pool1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-avg-pool2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-avg-pool3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-batch-normalization.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-bidirectional.pbtxt
View File

@ -97,6 +97,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-concatenate.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
View File

@ -178,6 +178,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-conv3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-convolution1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-convolution2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-convolution3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-cropping1-d.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-cropping2-d.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-cropping3-d.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt
View File

@ -98,6 +98,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt
View File

@ -98,6 +98,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dense.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-depthwise-conv2-d.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dot.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dropout.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-e-l-u.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-embedding.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-flatten.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u.pbtxt
View File

@ -161,6 +161,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-dropout.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-noise.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-input-layer.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m.pbtxt
View File

@ -161,6 +161,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-lambda.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-layer.pbtxt
View File

@ -87,6 +87,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected1-d.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected2-d.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-masking.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling1-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling2-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling3-d.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-maximum.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-minimum.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-multiply.pbtxt
View File

@ -89,6 +89,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-p-re-l-u.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-permute.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-r-n-n.pbtxt
View File

@ -92,6 +92,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-re-l-u.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-repeat-vector.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-reshape.pbtxt
View File

@ -88,6 +88,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv1-d.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv2-d.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
View File

@ -90,6 +90,10 @@ tf_class {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "

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