def _make_train_function(self):
if ops.executing_eagerly_outside_functions():
return super(WideDeepModel, self)._make_train_function()
# Only needed for graph mode and model_to_estimator.
has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
self._check_trainable_weights_consistency()
# If we have re-compiled the loss/weighted metric sub-graphs then create
# train function even if one exists already. This is because
# `_feed_sample_weights` list has been updated on re-compile.
if getattr(self, 'train_function', None) is None or has_recompiled:
# Restore the compiled trainable state.
current_trainable_state = self._get_trainable_state()
self._set_trainable_state(self._compiled_trainable_state)
inputs = (self._feed_inputs + self._feed_targets +
self._feed_sample_weights)
if not isinstance(K.symbolic_learning_phase(), int):
inputs += [K.symbolic_learning_phase()]
if isinstance(self.optimizer, (list, tuple)):
linear_optimizer = self.optimizer[0]
dnn_optimizer = self.optimizer[1]
else:
linear_optimizer = self.optimizer
dnn_optimizer = self.optimizer
with K.get_graph().as_default():
with K.name_scope('training'):
# Training updates
updates = []
linear_updates = linear_optimizer.get_updates(
params=self.linear_model.trainable_weights, # pylint: disable=protected-access
loss=self.total_loss)
updates += linear_updates
dnn_updates = dnn_optimizer.get_updates(
params=self.dnn_model.trainable_weights, # pylint: disable=protected-access
loss=self.total_loss)
updates += dnn_updates
# Unconditional updates
updates += self.get_updates_for(None)
# Conditional updates relevant to this model
updates += self.get_updates_for(self.inputs)
metrics = self._get_training_eval_metrics()
metrics_tensors = [
m._call_result for m in metrics
if hasattr(m, '_call_result') # pylint: disable=protected-access
]
with K.name_scope('training'):
# Gets loss and metrics. Updates weights at each call.
fn = K.function(inputs, [self.total_loss] + metrics_tensors,
updates=updates,
name='train_function',
**self._function_kwargs)
setattr(self, 'train_function', fn)
# Restore the current trainable state
self._set_trainable_state(current_trainable_state)
def _make_train_function(self):
# TODO(tanzheny): This is a direct copy from super to make it work
# refactor it so that common logic can be shared.
has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
self._check_trainable_weights_consistency()
# If we have re-compiled the loss/weighted metric sub-graphs then create
# train function even if one exists already. This is because
# `_feed_sample_weights` list has been updated on re-compile.
if getattr(self, 'train_function', None) is None or has_recompiled:
# Restore the compiled trainable state.
current_trainable_state = self._get_trainable_state()
self._set_trainable_state(self._compiled_trainable_state)
inputs = (
self._feed_inputs + self._feed_targets + self._feed_sample_weights)
if not isinstance(K.symbolic_learning_phase(), int):
inputs += [K.symbolic_learning_phase()]
linear_optimizer, dnn_optimizer = self._get_optimizers()
with K.get_graph().as_default():
with K.name_scope('training'):
# Training updates
updates = []
linear_updates = linear_optimizer.get_updates(
params=self.linear_model.trainable_weights, # pylint: disable=protected-access
loss=self.total_loss)
updates += linear_updates
dnn_updates = dnn_optimizer.get_updates(
params=self.dnn_model.trainable_weights, # pylint: disable=protected-access
loss=self.total_loss)
updates += dnn_updates
# Unconditional updates
updates += self.get_updates_for(None)
# Conditional updates relevant to this model
updates += self.get_updates_for(self.inputs)
metrics = self._get_training_eval_metrics()
metrics_tensors = [
m._call_result for m in metrics if hasattr(m, '_call_result') # pylint: disable=protected-access
]
with K.name_scope('training'):
# Gets loss and metrics. Updates weights at each call.
fn = K.function(
inputs, [self.total_loss] + metrics_tensors,
updates=updates,
name='train_function',
**self._function_kwargs)
setattr(self, 'train_function', fn)
# Restore the current trainable state
self._set_trainable_state(current_trainable_state)
def __init__(self, model):
# setup a function to predict the digit with dropout (default is to predict without dropout)
self.f = K.function(
[model.layers[0].input,
K.symbolic_learning_phase()],
[model.output],
)
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = flatten_perdevice_values(strategy, inputs)
targets = flatten_perdevice_values(strategy, targets)
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
else:
sample_weights = [
None
for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = distributed_training_utils.flatten_perdevice_values(strategy, inputs)
targets = distributed_training_utils.flatten_perdevice_values(
strategy, targets)
if mode == 'predict':
sample_weights = []
targets = []
else:
sample_weights = [
None for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = flatten_perdevice_values(strategy, inputs)
targets = flatten_perdevice_values(strategy, targets)
# Expand 1-dimensional inputs.
# TODO(b/124535720): Remove once this standarize data logic is shared with
# main flow.
inputs, targets = nest.map_structure(training_utils.standardize_single_array,
(inputs, targets))
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
else:
sample_weights = [
None for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return ins
def _update_sample_weight_mode(model, mode, inputs):
"""Updates the sample_weight_mode of a given model."""
# Add a quick return to prevent us from calling model._feed_targets that
# accesses certain model properties that may not be set in the `PREDICT` mode.
if mode == ModeKeys.PREDICT:
return
sample_weights = None
# `inputs` is the model's inputs + targets + sample_weights +
# learning phase placeholder if specified. To update the sample_weight_mode
# we need to determine if the user has passed sample weights as part of the
# input.
if not callable(inputs):
sample_weights = inputs[len(model._feed_inputs) +
len(model._feed_targets):]
has_learning_phase_pl = (
mode == ModeKeys.TRAIN
and not isinstance(K.symbolic_learning_phase(), int))
if has_learning_phase_pl:
sample_weights = sample_weights[:-1]
model._update_sample_weight_modes(sample_weights=sample_weights)
# Call the DistributionStrategy specific function to update the
# sample_weight_mode on the model.
if model._distribution_strategy:
distributed_training_utils_v1._update_sample_weight_modes(
model, mode, sample_weights)
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = flatten_perdevice_values(strategy, inputs)
targets = flatten_perdevice_values(strategy, targets)
# Expand 1-dimensional inputs.
# TODO(b/124535720): Remove once this standarize data logic is shared with
# main flow.
inputs, targets = nest.map_structure(training_utils.standardize_single_array,
(inputs, targets))
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
else:
sample_weights = [
None for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
# When the inputs are dict, then we want to flatten it in the same order as
# the input layers, such that the data are fed into the input layers in the
# correct order.
if isinstance(inputs, dict):
inputs = [inputs[key] for key in model._feed_input_names]
if is_distributing_by_cloning(model):
inputs = flatten_perdevice_values(strategy, inputs)
targets = flatten_perdevice_values(strategy, targets)
else:
# TODO(b/129653859): Simplify after PerReplica can be the input of
# `def_function.function`.
# Without cloning the `inputs` and `target` are the inputs to
# `values.regroup`. Instead of a flat list of `len(inputs) * num_replicas`
# we need a list of `len(inputs)` lists, where each per-input list has
# `len(num_replicas)` elements. Each element[i] in the per-input
# list is the input to the i-th replica. For example, if inputs are
# `[[1, 2], [3, 4]]` and there are two replicas, then we want
# `[[1, 3], [2, 4]]` (see `values_test.testWrapAListOfTwoTuples`) so that
# we arrive at a `PerReplica(d0: 1, d1: 2)` and a `PerReplica(d0:3, d1:4)`.
inputs = unwrap_perdevice_values(strategy, inputs)
targets = unwrap_perdevice_values(strategy, targets)
# Expand 1-dimensional inputs.
# TODO(b/124535720): Remove once this standarize data logic is shared with
# main flow.
inputs, targets = nest.map_structure(
training_utils.standardize_single_array, (inputs, targets))
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
elif not is_distributing_by_cloning(model):
sample_weights = None # b/129503665
else:
sample_weights = [
None
for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = [inputs, targets, sample_weights]
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return tuple(ins)
def _update_sample_weight_mode(model, mode, inputs):
"""Updates the sample_weight_mode of a given model."""
if not model._distribution_strategy and mode != ModeKeys.PREDICT:
# `inputs` is the model's inputs + targets + sample_weights +
# learning phase placeholder if specified. To update the sample_weight_mode
# we need to determine if the user has passed sample weights as part of the
# input.
sample_weights = inputs[len(model._feed_inputs) +
len(model._feed_targets):]
has_learning_phase_pl = (
mode == ModeKeys.TRAIN
and not isinstance(K.symbolic_learning_phase(), int))
if has_learning_phase_pl:
sample_weights = sample_weights[:-1]
model._update_sample_weight_modes(sample_weights=sample_weights)
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
inputs = distributed_training_utils_v1.get_iterator(
inputs, model._distribution_strategy)
def get_distributed_inputs():
return distributed_training_utils_v1._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# IteratorBase to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
if isinstance(
inputs,
(dataset_ops.DatasetV1, dataset_ops.DatasetV2, iterator_ops.Iterator)):
inputs, targets, sample_weights = model._standardize_user_data(
inputs, extract_tensors_from_dataset=True)
inputs = training_utils_v1.ModelInputs(inputs).as_list()
targets = list(targets or [])
sample_weights = list(sample_weights or [])
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True] # Add learning phase value.
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2)):
inputs = distributed_training_utils.get_iterator(
inputs, model._distribution_strategy)
def get_distributed_inputs():
return distributed_training_utils._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# EagerIterator to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
if isinstance(inputs, (dataset_ops.DatasetV1, dataset_ops.DatasetV2,
iterator_ops.Iterator)):
inputs, targets, sample_weights = model._standardize_user_data(
inputs,
extract_tensors_from_dataset=True)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True] # Add learning phase value.
return ins
def get_layer_evaluation_generator(self, debug_input_tensor=None):
"""
Todo
:param debug_input_tensor:
:return:
"""
''' All outputs we wanna have during the evaluation
1. Input to the layers
2. Layer output without activation
3. Layer output with activation
'''
outputs = [[layer.input, # Input to the layers
layer.output.op.inputs[0].op.inputs[0], # Output without Activation
layer.output # Output with activation
] for layer in self._model.layers if layer.__class__.__name__ != 'InputLayer']
''' Keras function for getting the layer ouputs'''
layer_outputs = K.function([self._model.input,
K.symbolic_learning_phase()],
outputs) # evaluation function
# predictions = K.function([self._model.layers.input,
# K.symbolic_learning_phase()],
# self._model.layers.output)
# Create all the intemediate outputs with dropout disables. If Dropout is enables, we have MC-Dropout
layer_outs = layer_outputs([debug_input_tensor, False])
# preds = predictions([X_test, False])
debug_input_tensor = self._debug_create_tensor(self._model.input.shape, debug_input_tensor)
for i, o in enumerate(zip(layer_outs, self._model.layers[1:])):
i += 1
inputs = o[0][0]
output_b_a = o[0][1] # Output before activation
output = o[0][2] # Output after activation
l = o[1]
model_dict = {'layer_name': l.__class__.__name__,
'layer': l,
'inputs': inputs,
'output_b_a': output_b_a,
'output': output
}
yield model_dict
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
# When the inputs are dict, then we want to flatten it in the same order as
# the input layers, such that the data are fed into the input layers in the
# correct order.
if isinstance(inputs, dict):
inputs = [inputs[key] for key in model._feed_input_names]
if is_distributing_by_cloning(model):
inputs = flatten_per_replica_values(strategy, inputs)
targets = flatten_per_replica_values(strategy, targets)
# Expand 1-dimensional inputs.
# TODO(b/124535720): Remove once this standarize data logic is shared with
# main flow.
inputs, targets = nest.map_structure(
training_utils.standardize_single_array, (inputs, targets))
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
elif not is_distributing_by_cloning(model):
sample_weights = None # b/129503665
else:
sample_weights = [
None
for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = [inputs, targets, sample_weights]
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return tuple(ins)
def get_layer_outputs(model, patches):
"""
Print internal layer outputs to files
:param model: trained keras model
:param patches: input samples
:param layers: relevant layers, if None then all layer are relevant
:return: layer_outs probability dist
"""
inp = model.input # input placeholder
layers, _ = get_relevant_layers(model=model)
if layers is None:
outputs = [layer.output for layer in model.layers[1:]]
else:
outputs = [layer.output for layer in layers]
functors = [K.function([inp, K.symbolic_learning_phase()], [out]) for out in outputs] # evaluation functions
layer_outs = [func([patches, False])[0] for func in functors]
layer_outs_dist = list(map(lambda x: softmax(x, axis=-1), layer_outs))
return layer_outs_dist
def _update_sample_weight_mode(model, mode, adapter):
"""Updates the sample_weight_mode of a given model."""
# Add a quick return to prevent us from calling model._feed_targets that
# accesses certain model properties that may not be set in the `PREDICT` mode.
if mode == ModeKeys.PREDICT:
return
sample_weights = None
# Get some sample inputs from the data_adapter
iterator = _create_dataset_iterator(model._distribution_strategy,
adapter.get_dataset())
inputs = create_batch_inputs(iterator, mode, model,
model._distribution_strategy)
# `inputs` is the model's inputs + targets + sample_weights +
# learning phase placeholder if specified. To update the sample_weight_mode
# we need to determine if the user has passed sample weights as part of the
# input.
if not callable(inputs):
# if not isinstance(inputs, collections.Sequence):
# inputs = (inputs,)
# Note that the batch inputs should be a tuple of 2, 3 or 4 items.
# (input, target, {sample_weights}, {learning_phase})
sample_weights_index = 0
if model._feed_inputs:
sample_weights_index += 1
if model._feed_targets:
sample_weights_index += 1
sample_weights = inputs[sample_weights_index:]
has_learning_phase_pl = (
mode == ModeKeys.TRAIN
and not isinstance(backend.symbolic_learning_phase(), int))
if has_learning_phase_pl:
sample_weights = sample_weights[:-1]
model._update_sample_weight_modes(nest.flatten(sample_weights))
# Call the DistributionStrategy specific function to update the
# sample_weight_mode on the model.
if model._distribution_strategy:
dist_utils._update_sample_weight_modes(model, mode, sample_weights)
# Force delete the iterator.
del iterator
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
# When the inputs are dict, then we want to flatten it in the same order as
# the input layers, such that the data are fed into the input layers in the
# correct order.
if isinstance(inputs, dict):
inputs = [inputs[key] for key in model._feed_input_names]
if is_distributing_by_cloning(model):
inputs = flatten_per_replica_values(strategy, inputs)
targets = flatten_per_replica_values(strategy, targets)
# Expand 1-dimensional inputs.
# TODO(b/124535720): Remove once this standarize data logic is shared with
# main flow.
inputs, targets = nest.map_structure(
training_utils.standardize_single_array, (inputs, targets))
if mode == ModeKeys.PREDICT:
sample_weights = []
targets = []
elif not is_distributing_by_cloning(model):
sample_weights = None # b/129503665
else:
sample_weights = [
None for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = [inputs, targets, sample_weights]
if mode == ModeKeys.TRAIN and not isinstance(K.symbolic_learning_phase(),
int):
ins += [True]
return tuple(ins)
def create_batch_inputs(iterator, mode, model, strategy):
"""Create the input data from the iterator based on the model and strategy."""
if strategy:
# Note that the batch_ins is a function to avoid the tf.function
# retrace.
def distribute_batch_ins():
return dist_utils._prepare_feed_values(model, iterator, None, None,
mode)
batch_ins = distribute_batch_ins
else:
batch_ins = next(iterator)
if (mode == ModeKeys.TRAIN and not model.run_eagerly
and not isinstance(backend.symbolic_learning_phase(), int)):
# Add learning phase value.
if not isinstance(batch_ins, collections.Sequence):
batch_ins = (batch_ins, True)
else:
batch_ins += (True, )
return batch_ins
def _debug_output(self, output):
''' If you want to debug the values Eager Execution needs to be enabled'''
if not tf.executing_eagerly():
logging.warning('If eager execution is disabled not all debug values can be visualized!'
' Enable Eager Execution for proper debugging!')
# For debugging the layer we want a tensor only
if self._debug[self.DEBUG.layer]:
out = self._debug_create_tensor(output.shape)
elif self._debug[self.DEBUG.model]:
functor = K.function([self._model.input,
K.symbolic_learning_phase()],
output) # evaluation function
out = functor([self._debug[self.DEBUG.tensor], False])
out = tf.convert_to_tensor(out, dtype=tf.float32)
return out
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
def get_distributed_inputs():
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# EagerIterator to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
return training_distributed._prepare_feed_values(model, inputs, targets,
sample_weights, mode)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
if model._distribution_strategy:
def get_distributed_inputs():
return training_distributed._prepare_feed_values(
model, inputs, targets, sample_weights, mode)
# In the eager case, we want to call the input method per step, so return
# a lambda from here that can be called. Note that this is applicable only
# in Distribution Strategy case as it follows the same code path for both
# eager and graph modes.
# TODO(priyag,omalleyt): Either we should move the training DS with
# EagerIterator to use training_generator code path, or figure out how to
# set a symbolic Iterator out of a Dataset when in eager mode.
if context.executing_eagerly():
return get_distributed_inputs
else:
return get_distributed_inputs()
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def model_iteration(model,
inputs,
targets=None,
sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
mode='train',
**kwargs):
"""Loop function for arrays of data with modes 'train'/'test'/'predict'.
Arguments:
model: Keras Model instance.
inputs: Either a list of arrays or a dictionary.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
concatenation of list the display names of the outputs of `f` and the
list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training (useful for resuming a
previous training run)
steps_per_epoch: Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. Ignored with
the default value of `None`.
validation_steps: Number of steps to run validation for (only if doing
validation from data tensors). Ignored with the default value of `None`.
mode: One of 'train'/'test'/'predict'.
**kwargs: Additional arguments for backwards compatibility.
Returns:
- In 'train' mode: `History` object.
- In 'test' mode: Evaluation metrics.
- In 'predict' mode: Outputs of the Model called on inputs.
Raises:
ValueError: in case of invalid arguments.
"""
# Backwards compatibility.
if 'steps' in kwargs:
steps_per_epoch = kwargs['steps']
_validate_arguments(steps_per_epoch, validation_steps, kwargs)
if mode == 'train':
_print_train_info(inputs, val_inputs, steps_per_epoch, verbose)
# Get step function and loop type.
f = model._get_execution_function(mode)
use_steps = steps_per_epoch is not None
do_validation = val_inputs is not None
# Prepare input data.
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
learning_phase_input = []
if not isinstance(K.symbolic_learning_phase(), int):
learning_phase_input = [True] if mode == 'train' else [False]
ins = inputs + targets + sample_weights + learning_phase_input
num_samples_or_steps = _get_num_samples_or_steps(ins, batch_size,
steps_per_epoch)
# Configure callbacks.
count_mode = 'steps' if use_steps else 'samples'
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=num_samples_or_steps,
validation_steps=validation_steps,
verbose=0, # Handle ProgBarLogger separately in this loop.
count_mode=count_mode,
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = _get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# Find beforehand arrays that need sparse-to-dense conversion.
if issparse is not None:
indices_for_conversion_to_dense = []
feed = _get_model_feed(model, mode)
for i, (input_data, feed_tensor) in enumerate(zip(ins, feed)):
if issparse(input_data) and not K.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == 'predict':
aggregator = OutputsAggregator(use_steps, num_samples_or_steps)
else:
aggregator = MetricsAggregator(use_steps, num_samples_or_steps)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
results = []
epoch_logs = {}
if hasattr(model, 'stateful_metric_functions'):
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode)
progbar.on_epoch_begin(epoch, epoch_logs)
if use_steps:
# Step-wise loop.
for step in range(steps_per_epoch):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
progbar.on_batch_begin(step, batch_logs)
# Get outputs.
try:
batch_outs = f(ins)
except errors.OutOfRangeError:
logging.warning('Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches). You may need to'
'use the repeat() function when building your '
'dataset.' %
steps_per_epoch * epochs)
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
# Callbacks batch end.
batch_logs.update(_make_logs(model, batch_outs, mode))
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
progbar.on_batch_end(step, batch_logs)
if callbacks.model.stop_training:
break
else:
# Sample-wise loop.
index_array = np.arange(num_samples_or_steps)
if shuffle == 'batch':
index_array = training_utils.batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_samples_or_steps, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
# Slice into a batch.
try:
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
# Sparse to dense conversion.
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
# Callbacks batch_begin.
batch_logs = {'batch': batch_index, 'size': len(batch_ids)}
callbacks._call_batch_hook(mode, 'begin', batch_index, batch_logs)
progbar.on_batch_begin(batch_index, batch_logs)
# Get outputs.
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if batch_index == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs, batch_start, batch_end)
# Callbacks batch end.
batch_logs.update(_make_logs(model, batch_outs, mode))
callbacks._call_batch_hook(mode, 'end', batch_index, batch_logs)
progbar.on_batch_end(batch_index, batch_logs)
if callbacks.model.stop_training:
break
aggregator.finalize()
results = aggregator.results
epoch_logs.update(_make_logs(model, results, mode))
if len(results) == 1:
results = results[0]
# Run the test loop every epoch during training.
if do_validation and not callbacks.model.stop_training:
val_results = model_iteration(
model,
val_inputs,
targets=val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps_per_epoch=validation_steps,
callbacks=callbacks,
verbose=0,
mode='test')
if not isinstance(val_results, list):
val_results = [val_results]
epoch_logs.update(_make_logs(model, val_results, mode, prefix='val_'))
callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
progbar.on_epoch_end(epoch, epoch_logs)
callbacks._call_end_hook(mode)
if mode == 'train':
return model.history
return results
def model_iteration(model,
inputs,
targets=None,
sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
mode='train',
**kwargs):
"""Loop function for arrays of data with modes 'train'/'test'/'predict'.
Arguments:
model: Keras Model instance.
inputs: Either a list of arrays or a dictionary.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
concatenation of list the display names of the outputs of `f` and the
list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training (useful for resuming a
previous training run)
steps_per_epoch: Total number of steps (batches of samples) before
declaring one epoch finished and starting the next epoch. Ignored with
the default value of `None`.
validation_steps: Number of steps to run validation for (only if doing
validation from data tensors). Ignored with the default value of `None`.
mode: One of 'train'/'test'/'predict'.
**kwargs: Additional arguments for backwards compatibility.
Returns:
- In 'train' mode: `History` object.
- In 'test' mode: Evaluation metrics.
- In 'predict' mode: Outputs of the Model called on inputs.
Raises:
ValueError: in case of invalid arguments.
"""
# Backwards compatibility.
if 'steps' in kwargs:
steps_per_epoch = kwargs['steps']
_validate_arguments(steps_per_epoch, validation_steps, kwargs)
if mode == 'train':
_print_train_info(inputs, val_inputs, steps_per_epoch, verbose)
# Get step function and loop type.
f = model._get_execution_function(mode)
use_steps = steps_per_epoch is not None
do_validation = val_inputs is not None
# Prepare input data.
inputs = training_utils.ModelInputs(inputs).as_list()
targets = targets or []
sample_weights = sample_weights or []
learning_phase_input = []
if not isinstance(K.symbolic_learning_phase(), int):
learning_phase_input = [True] if mode == 'train' else [False]
ins = inputs + targets + sample_weights + learning_phase_input
num_samples_or_steps = _get_num_samples_or_steps(ins, batch_size,
steps_per_epoch)
# Configure callbacks.
count_mode = 'steps' if use_steps else 'samples'
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
samples=num_samples_or_steps,
validation_steps=validation_steps,
verbose=0, # Handle ProgBarLogger separately in this loop.
count_mode=count_mode,
mode=mode)
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = _get_progbar(model, count_mode)
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# Find beforehand arrays that need sparse-to-dense conversion.
if issparse is not None:
indices_for_conversion_to_dense = []
feed = _get_model_feed(model, mode)
for i, (input_data, feed_tensor) in enumerate(zip(ins, feed)):
if issparse(input_data) and not K.is_sparse(feed_tensor):
indices_for_conversion_to_dense.append(i)
# Select aggregation method.
if mode == 'predict':
aggregator = OutputsAggregator(use_steps, num_samples_or_steps)
else:
aggregator = MetricsAggregator(use_steps, num_samples_or_steps)
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
for epoch in range(initial_epoch, epochs):
if callbacks.model.stop_training:
break
# Setup work for each epoch
results = []
epoch_logs = {}
if hasattr(model, 'stateful_metric_functions'):
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch, epoch_logs, mode=mode)
progbar.on_epoch_begin(epoch, epoch_logs)
if use_steps:
# Step-wise loop.
for step in range(steps_per_epoch):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
progbar.on_batch_begin(step, batch_logs)
# Get outputs.
try:
batch_outs = f(ins)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches). You may need to'
'use the repeat() function when building your '
'dataset.' % steps_per_epoch * epochs)
break
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if step == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs)
# Callbacks batch end.
batch_logs.update(_make_logs(model, batch_outs, mode))
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
progbar.on_batch_end(step, batch_logs)
if callbacks.model.stop_training:
break
else:
# Sample-wise loop.
index_array = np.arange(num_samples_or_steps)
if shuffle == 'batch':
index_array = training_utils.batch_shuffle(
index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_samples_or_steps, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
# Slice into a batch.
try:
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1],
batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
# Sparse to dense conversion.
if issparse is not None:
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
# Callbacks batch_begin.
batch_logs = {'batch': batch_index, 'size': len(batch_ids)}
callbacks._call_batch_hook(mode, 'begin', batch_index,
batch_logs)
progbar.on_batch_begin(batch_index, batch_logs)
# Get outputs.
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
# Aggregate results.
if batch_index == 0:
aggregator.create(batch_outs)
aggregator.aggregate(batch_outs, batch_start, batch_end)
# Callbacks batch end.
batch_logs.update(_make_logs(model, batch_outs, mode))
callbacks._call_batch_hook(mode, 'end', batch_index,
batch_logs)
progbar.on_batch_end(batch_index, batch_logs)
if callbacks.model.stop_training:
break
aggregator.finalize()
results = aggregator.results
epoch_logs.update(_make_logs(model, results, mode))
if len(results) == 1:
results = results[0]
# Run the test loop every epoch during training.
if do_validation and not callbacks.model.stop_training:
val_results = model_iteration(model,
val_inputs,
targets=val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps_per_epoch=validation_steps,
callbacks=callbacks,
verbose=0,
mode='test')
if not isinstance(val_results, list):
val_results = [val_results]
epoch_logs.update(
_make_logs(model, val_results, mode, prefix='val_'))
callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
progbar.on_epoch_end(epoch, epoch_logs)
callbacks._call_end_hook(mode)
if mode == 'train':
return model.history
return results
#%%
logger.info('Loading predictor')
bert_topic = ktrain.load_predictor('models/bert_20ng/trained_bert_4epochs_2e-2')
#%%
ARTICLE1 = "Google_2_en.yml"
ARTICLE2 = "Google_9_en.yml"
with open(f"articles/{ARTICLE1}") as f:
article1 = yaml.load(f)
with open(f"articles/{ARTICLE2}") as f:
article2 = yaml.load(f)
# %%
inp = bert_topic.model.input
outs = bert_topic.model.layers[-2].output
functor = K.function([inp, K.symbolic_learning_phase()], outs)
#%%
embeddings1 = []
for p in article1['paragraphs']:
preproc = bert_topic.preproc.preprocess(p)
out = functor([preproc, True])
embeddings1.append(out.prod(axis=0))
embeddings2 = []
for p in article2['paragraphs']:
preproc = bert_topic.preproc.preprocess(p)
out = functor([preproc, True])
embeddings2.append(out.prod(axis=0))
