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train | ProjectRegistry.load_module | Load a Python module that should be useable from Jamfiles.
There are generally two types of modules Jamfiles might want to
use:
- Core Boost.Build. Those are imported using plain names, e.g.
'toolset', so this function checks if we have module named
b2.package.module already.
... | deps/src/boost_1_68_0/tools/build/src/build/project.py | def load_module(self, name, extra_path=None):
"""Load a Python module that should be useable from Jamfiles.
There are generally two types of modules Jamfiles might want to
use:
- Core Boost.Build. Those are imported using plain names, e.g.
'toolset', so this function checks if w... | def load_module(self, name, extra_path=None):
"""Load a Python module that should be useable from Jamfiles.
There are generally two types of modules Jamfiles might want to
use:
- Core Boost.Build. Those are imported using plain names, e.g.
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train | ProjectAttributes.set | Set the named attribute from the specification given by the user.
The value actually set may be different. | deps/src/boost_1_68_0/tools/build/src/build/project.py | def set(self, attribute, specification, exact=False):
"""Set the named attribute from the specification given by the user.
The value actually set may be different."""
assert isinstance(attribute, basestring)
assert isinstance(exact, (int, bool))
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"""Set the named attribute from the specification given by the user.
The value actually set may be different."""
assert isinstance(attribute, basestring)
assert isinstance(exact, (int, bool))
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train | ProjectAttributes.dump | Prints the project attributes. | deps/src/boost_1_68_0/tools/build/src/build/project.py | def dump(self):
"""Prints the project attributes."""
id = self.get("id")
if not id:
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"""Prints the project attributes."""
id = self.get("id")
if not id:
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train | ProjectRules.make_wrapper | Given a free-standing function 'callable', return a new
callable that will call 'callable' and report all exceptins,
using 'call_and_report_errors'. | deps/src/boost_1_68_0/tools/build/src/build/project.py | def make_wrapper(self, callable_):
"""Given a free-standing function 'callable', return a new
callable that will call 'callable' and report all exceptins,
using 'call_and_report_errors'."""
assert callable(callable_)
def wrapper(*args, **kw):
return self.call_and_repo... | def make_wrapper(self, callable_):
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assert callable(callable_)
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train | ProjectRules.constant | Declare and set a project global constant.
Project global constants are normal variables but should
not be changed. They are applied to every child Jamfile. | deps/src/boost_1_68_0/tools/build/src/build/project.py | def constant(self, name, value):
"""Declare and set a project global constant.
Project global constants are normal variables but should
not be changed. They are applied to every child Jamfile."""
assert is_iterable_typed(name, basestring)
assert is_iterable_typed(value, basestrin... | def constant(self, name, value):
"""Declare and set a project global constant.
Project global constants are normal variables but should
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assert is_iterable_typed(name, basestring)
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train | ProjectRules.path_constant | Declare and set a project global constant, whose value is a path. The
path is adjusted to be relative to the invocation directory. The given
value path is taken to be either absolute, or relative to this project
root. | deps/src/boost_1_68_0/tools/build/src/build/project.py | def path_constant(self, name, value):
"""Declare and set a project global constant, whose value is a path. The
path is adjusted to be relative to the invocation directory. The given
value path is taken to be either absolute, or relative to this project
root."""
assert is_iterable... | def path_constant(self, name, value):
"""Declare and set a project global constant, whose value is a path. The
path is adjusted to be relative to the invocation directory. The given
value path is taken to be either absolute, or relative to this project
root."""
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train | ProjectRules.conditional | Calculates conditional requirements for multiple requirements
at once. This is a shorthand to be reduce duplication and to
keep an inline declarative syntax. For example:
lib x : x.cpp : [ conditional <toolset>gcc <variant>debug :
<define>DEBUG_EXCEPTION <define>DEBUG_TRACE ... | deps/src/boost_1_68_0/tools/build/src/build/project.py | def conditional(self, condition, requirements):
"""Calculates conditional requirements for multiple requirements
at once. This is a shorthand to be reduce duplication and to
keep an inline declarative syntax. For example:
lib x : x.cpp : [ conditional <toolset>gcc <variant>debug :
... | def conditional(self, condition, requirements):
"""Calculates conditional requirements for multiple requirements
at once. This is a shorthand to be reduce duplication and to
keep an inline declarative syntax. For example:
lib x : x.cpp : [ conditional <toolset>gcc <variant>debug :
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train | create_array_feature_extractor | Creates a feature extractor from an input array feature, return
input_features is a list of one (name, array) tuple.
extract_indices is either an integer or a list. If it's an integer,
the output type is by default a double (but may also be an integer).
If a list, the output type is an array. | src/external/coremltools_wrap/coremltools/coremltools/models/array_feature_extractor.py | def create_array_feature_extractor(input_features, output_name, extract_indices,
output_type = None):
"""
Creates a feature extractor from an input array feature, return
input_features is a list of one (name, array) tuple.
extract_indices is either an integer or a li... | def create_array_feature_extractor(input_features, output_name, extract_indices,
output_type = None):
"""
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train | BuildOutputProcessor.add_input | Add a single build XML output file to our data. | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | def add_input(self, input):
'''
Add a single build XML output file to our data.
'''
events = xml.dom.pulldom.parse(input)
context = []
for (event,node) in events:
if event == xml.dom.pulldom.START_ELEMENT:
context.append(node)
i... | def add_input(self, input):
'''
Add a single build XML output file to our data.
'''
events = xml.dom.pulldom.parse(input)
context = []
for (event,node) in events:
if event == xml.dom.pulldom.START_ELEMENT:
context.append(node)
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train | BuildOutputProcessor.x_build_targets_target | Process the target dependency DAG into an ancestry tree so we can look up
which top-level library and test targets specific build actions correspond to. | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | def x_build_targets_target( self, node ):
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Process the target dependency DAG into an ancestry tree so we can look up
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target_node = node
name = self.get_child_data(target_node,tag='name',... | def x_build_targets_target( self, node ):
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Process the target dependency DAG into an ancestry tree so we can look up
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train | BuildOutputProcessor.x_build_action | Given a build action log, process into the corresponding test log and
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name = self.get_child(action_node,tag='name')
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action_node = node
name = self.get_child(action_node,tag='name')
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train | BuildOutputProcessor.x_build_timestamp | The time-stamp goes to the corresponding attribute in the result. | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | def x_build_timestamp( self, node ):
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The time-stamp goes to the corresponding attribute in the result.
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train | BuildConsoleSummaryReport.print_action | Print the detailed info of failed or always print tests. | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | def print_action(self, test_succeed, action):
'''
Print the detailed info of failed or always print tests.
'''
#self.info_print(">>> {0}",action.keys())
if not test_succeed or action['info']['always_show_run_output']:
output = action['output'].strip()
if o... | def print_action(self, test_succeed, action):
'''
Print the detailed info of failed or always print tests.
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train | _get_weight_param_summary | Get a summary of _NeuralNetwork_pb2.WeightParams
Args:
wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display
Returns:
a str summary for wp | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | def _get_weight_param_summary(wp):
"""Get a summary of _NeuralNetwork_pb2.WeightParams
Args:
wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display
Returns:
a str summary for wp
"""
summary_str = ''
if wp.HasField('quantization'):
nbits = wp... | def _get_weight_param_summary(wp):
"""Get a summary of _NeuralNetwork_pb2.WeightParams
Args:
wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display
Returns:
a str summary for wp
"""
summary_str = ''
if wp.HasField('quantization'):
nbits = wp... | [
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train | _summarize_network_layer_info | Args:
layer - an MLModel NeuralNetwork Layer protobuf message
Returns:
layer_type : str - type of layer
layer_name : str - name of the layer
layer_inputs : list[str] - a list of strings representing input blobs of the layer
layer_outputs : list[str] - a list of strings representing output blobs ... | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | def _summarize_network_layer_info(layer):
"""
Args:
layer - an MLModel NeuralNetwork Layer protobuf message
Returns:
layer_type : str - type of layer
layer_name : str - name of the layer
layer_inputs : list[str] - a list of strings representing input blobs of the layer
layer_outputs : li... | def _summarize_network_layer_info(layer):
"""
Args:
layer - an MLModel NeuralNetwork Layer protobuf message
Returns:
layer_type : str - type of layer
layer_name : str - name of the layer
layer_inputs : list[str] - a list of strings representing input blobs of the layer
layer_outputs : li... | [
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train | summarize_neural_network_spec | Summarize network into the following structure.
Args:
mlmodel_spec : mlmodel spec
Returns:
inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
outputs : list[(str, str)] - a list of two tuple (name, descriptor) for each output blob
layers : list[(str, list[str... | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | def summarize_neural_network_spec(mlmodel_spec):
""" Summarize network into the following structure.
Args:
mlmodel_spec : mlmodel spec
Returns:
inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
outputs : list[(str, str)] - a list of two tuple (name, descript... | def summarize_neural_network_spec(mlmodel_spec):
""" Summarize network into the following structure.
Args:
mlmodel_spec : mlmodel spec
Returns:
inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
outputs : list[(str, str)] - a list of two tuple (name, descript... | [
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train | print_network_spec | Print the network information summary.
Args:
mlmodel_spec : the mlmodel spec
interface_only : Shows only the input and output of the network | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | def print_network_spec(mlmodel_spec, interface_only=False):
""" Print the network information summary.
Args:
mlmodel_spec : the mlmodel spec
interface_only : Shows only the input and output of the network
"""
inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec)
print('... | def print_network_spec(mlmodel_spec, interface_only=False):
""" Print the network information summary.
Args:
mlmodel_spec : the mlmodel spec
interface_only : Shows only the input and output of the network
"""
inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec)
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train | _generate_base_svm_classifier_spec | Takes an SVM classifier produces a starting spec using the parts. that are
shared between all SVMs. | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_SVC.py | def _generate_base_svm_classifier_spec(model):
"""
Takes an SVM classifier produces a starting spec using the parts. that are
shared between all SVMs.
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
check_fitted(model, la... | def _generate_base_svm_classifier_spec(model):
"""
Takes an SVM classifier produces a starting spec using the parts. that are
shared between all SVMs.
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
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train | convert | Convert a Support Vector Classtion (SVC) model to the protobuf spec.
Parameters
----------
model: SVC
A trained SVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output column.
R... | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_SVC.py | def convert(model, feature_names, target):
"""Convert a Support Vector Classtion (SVC) model to the protobuf spec.
Parameters
----------
model: SVC
A trained SVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (defau... | def convert(model, feature_names, target):
"""Convert a Support Vector Classtion (SVC) model to the protobuf spec.
Parameters
----------
model: SVC
A trained SVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (defau... | [
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train | NetGraph.make_input_layers | Extract the ordering of the input layers. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
if hasattr(self.model, 'input_layers'):
input_keras_layers = self.model.input_layers[:]
self.input_layers = [None] * len(input_keras_layers)
f... | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
if hasattr(self.model, 'input_layers'):
input_keras_layers = self.model.input_layers[:]
self.input_layers = [None] * len(input_keras_layers)
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train | NetGraph.make_output_layers | Extract the ordering of output layers. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
# TODO
# use successors == 0 as the criteria for output layer
# will fail when some intermediate layers also generate output.
# However, because the possibility of having inserted layers,... | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
# TODO
# use successors == 0 as the criteria for output layer
# will fail when some intermediate layers also generate output.
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train | NetGraph.generate_blob_names | Generate blob names for each one of the edge. At this time, Keras does not
support "fork" operation (a layer with more than 1 blob output). So we just
use names of the src layer to identify a blob. We also assume all neural
networks are singly-connected graphs - which should be the case. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def generate_blob_names(self):
"""
Generate blob names for each one of the edge. At this time, Keras does not
support "fork" operation (a layer with more than 1 blob output). So we just
use names of the src layer to identify a blob. We also assume all neural
networks are singly... | def generate_blob_names(self):
"""
Generate blob names for each one of the edge. At this time, Keras does not
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train | NetGraph._remove_layer | remove the layer and its input/output edges | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def _remove_layer(self, layer):
"""
remove the layer and its input/output edges
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove all edges
for succ in successors:
self._remove_edge(layer, succ)
fo... | def _remove_layer(self, layer):
"""
remove the layer and its input/output edges
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successors = self.get_successors(layer)
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# remove all edges
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train | NetGraph._insert_layer_after | Insert the new_layer after layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer):
"""
Insert the new_layer after layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
"""
# reminder: new_keras_layer is not part of the original Keras n... | def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer):
"""
Insert the new_layer after layer, whose position is layer_idx. The new layer's
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train | NetGraph._insert_layer_between | Insert the new_layer before layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def _insert_layer_between(self, src, snk, new_layer, new_keras_layer):
"""
Insert the new_layer before layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
"""
if snk is None:
insert_pos = self.layer_list.inde... | def _insert_layer_between(self, src, snk, new_layer, new_keras_layer):
"""
Insert the new_layer before layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
"""
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train | NetGraph.defuse_activation | Defuse the fused activation layers in the network. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def defuse_activation(self):
"""
Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
# unwrap time-distributed... | def defuse_activation(self):
"""
Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
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train | NetGraph._get_1d_interface_edges | Get edges that represents transition from not 1D to 1D, and 1D to not 1D
A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs.
An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def _get_1d_interface_edges(self):
"""
Get edges that represents transition from not 1D to 1D, and 1D to not 1D
A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs.
An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs.
"""
... | def _get_1d_interface_edges(self):
"""
Get edges that represents transition from not 1D to 1D, and 1D to not 1D
A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs.
An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs.
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train | NetGraph.insert_1d_permute_layers | Insert permutation layers before a 1D start point or after 1D end point | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | def insert_1d_permute_layers(self):
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train | replace_nodes | Replace the old node with the new one.
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:param root: ast node that contains an indirect reference to old
:param old: node to replace
:param new: node to replace `old` with | src/unity/python/turicreate/meta/asttools/mutators/replace_mutator.py | def replace_nodes(root, old, new):
'''
Replace the old node with the new one.
Old must be an indirect child of root
:param root: ast node that contains an indirect reference to old
:param old: node to replace
:param new: node to replace `old` with
'''
rep = Replacer(old, new)
... | def replace_nodes(root, old, new):
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Replace the old node with the new one.
Old must be an indirect child of root
:param root: ast node that contains an indirect reference to old
:param old: node to replace
:param new: node to replace `old` with
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train | log_component_configuration | Report something about component configuration that the user should better know. | deps/src/boost_1_68_0/tools/build/src/build/configure.py | def log_component_configuration(component, message):
"""Report something about component configuration that the user should better know."""
assert isinstance(component, basestring)
assert isinstance(message, basestring)
__component_logs.setdefault(component, []).append(message) | def log_component_configuration(component, message):
"""Report something about component configuration that the user should better know."""
assert isinstance(component, basestring)
assert isinstance(message, basestring)
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train | create | Create a Transformer object to transform data for feature engineering.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
transformers: Transformer | list[Transformer]
An Transformer or a list of Transformers.
See Also
--------
turicreate.to... | src/unity/python/turicreate/toolkits/_feature_engineering/__init__.py | def create(dataset, transformers):
"""
Create a Transformer object to transform data for feature engineering.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
transformers: Transformer | list[Transformer]
An Transformer or a list of Transformer... | def create(dataset, transformers):
"""
Create a Transformer object to transform data for feature engineering.
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train | VGGishFeatureExtractor._preprocess_data | Preprocess each example, breaking it up into frames.
Returns two numpy arrays: preprocessed frame and their indexes | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | def _preprocess_data(audio_data, verbose=True):
'''
Preprocess each example, breaking it up into frames.
Returns two numpy arrays: preprocessed frame and their indexes
'''
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progres... | def _preprocess_data(audio_data, verbose=True):
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Preprocess each example, breaking it up into frames.
Returns two numpy arrays: preprocessed frame and their indexes
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train | VGGishFeatureExtractor._extract_features | Parameters
----------
preprocessed_data : SArray
Returns
-------
numpy array containing the deep features | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | def _extract_features(self, preprocessed_data, verbose=True):
"""
Parameters
----------
preprocessed_data : SArray
Returns
-------
numpy array containing the deep features
"""
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"""
Parameters
----------
preprocessed_data : SArray
Returns
-------
numpy array containing the deep features
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train | VGGishFeatureExtractor.get_deep_features | Performs both audio preprocessing and VGGish deep feature extraction. | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | def get_deep_features(self, audio_data, verbose):
'''
Performs both audio preprocessing and VGGish deep feature extraction.
'''
preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
deep_features = self._extract_features(preprocessed_data, verbose)
outp... | def get_deep_features(self, audio_data, verbose):
'''
Performs both audio preprocessing and VGGish deep feature extraction.
'''
preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
deep_features = self._extract_features(preprocessed_data, verbose)
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train | VGGishFeatureExtractor.get_spec | Return the Core ML spec | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | def get_spec(self):
"""
Return the Core ML spec
"""
if _mac_ver() >= (10, 14):
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else:
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"""
Return the Core ML spec
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if _mac_ver() >= (10, 14):
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train | remove_trivial | Remove redundant statements.
The statement `a = 1` will be removed::
a = 1
a = 2
The statement `a = 1` will not be removed because `b` depends on it::
a = 1
b = a + 2
a = 2
:param root: ast node | src/unity/python/turicreate/meta/asttools/mutators/remove_trivial.py | def remove_trivial(root):
'''
Remove redundant statements.
The statement `a = 1` will be removed::
a = 1
a = 2
The statement `a = 1` will not be removed because `b` depends on it::
a = 1
b = a + 2
a = 2
:param root: ast node
... | def remove_trivial(root):
'''
Remove redundant statements.
The statement `a = 1` will be removed::
a = 1
a = 2
The statement `a = 1` will not be removed because `b` depends on it::
a = 1
b = a + 2
a = 2
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train | safe_isinstance | To prevent circular imports, this extends isinstance()
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raised in case `value` doesn't have a __class__ attribute. | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | def safe_isinstance(value, types=None, class_names=None):
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train | value_to_jam | Makes a token to refer to a Python value inside Jam language code.
The token is merely a string that can be passed around in Jam code and
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train | abbreviate_dashed | Abbreviates each part of string that is delimited by a '-'. | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | def abbreviate_dashed(s):
"""Abbreviates each part of string that is delimited by a '-'."""
r = []
for part in s.split('-'):
r.append(abbreviate(part))
return '-'.join(r) | def abbreviate_dashed(s):
"""Abbreviates each part of string that is delimited by a '-'."""
r = []
for part in s.split('-'):
r.append(abbreviate(part))
return '-'.join(r) | [
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train | abbreviate | Apply a set of standard transformations to string to produce an
abbreviation no more than 4 characters long. | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | def abbreviate(s):
"""Apply a set of standard transformations to string to produce an
abbreviation no more than 4 characters long.
"""
if not s:
return ''
# check the cache
if s in abbreviate.abbreviations:
return abbreviate.abbreviations[s]
# anything less than 4 characters ... | def abbreviate(s):
"""Apply a set of standard transformations to string to produce an
abbreviation no more than 4 characters long.
"""
if not s:
return ''
# check the cache
if s in abbreviate.abbreviations:
return abbreviate.abbreviations[s]
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train | Node.get_decision | Get the decision from this node to a child node.
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A child node of this node.
Returns
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dict: A dictionary that describes how to get from this node to the
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Get the decision from this node to a child node.
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train | Node.to_dict | Return the node as a dictionary.
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root_id: Root id of the sub-tree
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Recursive function to dump this tree as a json blob.
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root_id: Root id of the sub-tree
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Recursive function to dump this tree as a json blob.
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node_id: id of the node to get the prediction value.
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Return the prediction score (if leaf node) or None if its an
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Return the prediction score (if leaf node) or None if its an
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train | DecisionTree.get_prediction_path | Return the prediction path from this node to the parent node.
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node_id : id of the node to get the prediction path.
missing_id : Additional info that contains nodes with missing features.
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Return the prediction path from this node to the parent node.
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train | create | Given a weighted graph with observed class labels of a subset of vertices,
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train | _is_not_pickle_safe_gl_model_class | Check if a Turi create model is pickle safe.
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obj_class : Class to be checked.
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True if the GLC class is a model and is pickle safe. | src/unity/python/turicreate/_gl_pickle.py | def _is_not_pickle_safe_gl_model_class(obj_class):
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Check if a Turi create model is pickle safe.
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----------
obj_class : Class to be checked.
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Check if a Turi create model is pickle safe.
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train | _is_not_pickle_safe_gl_class | Check if class is a Turi create model.
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----------
obj_class : Class to be checked.
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True if the class is a GLC Model. | src/unity/python/turicreate/_gl_pickle.py | def _is_not_pickle_safe_gl_class(obj_class):
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Check if class is a Turi create model.
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obj_class : Class to be checked.
Returns
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Check if class is a Turi create model.
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train | _get_gl_class_type | Internal util to get the type of the GLC class. The pickle file stores
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obj_class : Class which has to be categorized.
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train | _get_gl_object_from_persistent_id | Internal util to get a GLC object from a persistent ID in the pickle file.
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type_tag : The name of the glc class as saved in the GLC pickler.
gl_archive_abs_path: An absolute path to the GLC archive where the
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... | src/unity/python/turicreate/_gl_pickle.py | def _get_gl_object_from_persistent_id(type_tag, gl_archive_abs_path):
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Internal util to get a GLC object from a persistent ID in the pickle file.
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type_tag : The name of the glc class as saved in the GLC pickler.
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obj: Name of the object whose persistent ID is extracted.
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train | GLPickler.close | Close the pickle file, and the zip archive file. The single zip archive
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train | GLUnpickler.persistent_load | Reconstruct a GLC object using the persistent ID.
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pid : The persistent ID used in pickle file to save the GLC object.
Returns
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Reconstruct a GLC object using the persistent ID.
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Parameters
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pid : The persistent ID used in pickle file to save the GLC object.
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Reconstruct a GLC object using the persistent ID.
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pid : The persistent ID used in pickle file to save the GLC object.
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train | GLUnpickler.close | Clean up files that were created. | src/unity/python/turicreate/_gl_pickle.py | def close(self):
"""
Clean up files that were created.
"""
if self.file:
self.file.close()
self.file = None
# If temp_file is a folder, we do not remove it because we may
# still need it after the unpickler is disposed
if self.tmp_file and... | def close(self):
"""
Clean up files that were created.
"""
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self.file.close()
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train | convert | Convert scikit-learn pipeline, classifier, or regressor to Core ML format.
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sk_obj: model | [model] of scikit-learn format.
Scikit learn model(s) to convert to a Core ML format.
The input model may be a single scikit learn model, a scikit learn
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"""
Convert scikit-learn pipeline, classifier, or regressor to Core ML format.
Parameters
----------
sk_obj: model | [model] of scikit-learn format.
Scikit learn model(s) to convert to a Core ML format.
... | def convert(sk_obj, input_features = None,
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"""
Convert scikit-learn pipeline, classifier, or regressor to Core ML format.
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sk_obj: model | [model] of scikit-learn format.
Scikit learn model(s) to convert to a Core ML format.
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train | ParseMessage | Generate a new Message instance from this Descriptor and a byte string.
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descriptor: Protobuf Descriptor object
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descriptor: Protobuf Descriptor object
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Newly created protobuf Message object.
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train | load_images | Loads images from a directory. JPEG and PNG images are supported.
Parameters
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url : str
The string of the path where all the images are stored.
format : {'PNG' | 'JPG' | 'auto'}, optional
The format of the images in the directory. The default 'auto' parameter
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Loads images from a directory. JPEG and PNG images are supported.
Parameters
----------
url : str
The string of the path where all the images are stored.
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Loads images from a directory. JPEG and PNG images are supported.
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url : str
The string of the path where all the images are stored.
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train | _decode | Internal helper function for decoding a single Image or an SArray of Images | src/unity/python/turicreate/toolkits/image_analysis/image_analysis.py | def _decode(image_data):
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train | resize | Resizes the image or SArray of Images to a specific width, height, and
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image : turicreate.Image | SArray
The image or SArray of images to be resized.
width : int
The width the image is resized to.
height : int
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Resizes the image or SArray of Images to a specific width, height, and
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Parameters
----------
image : turicreate.Image | SArray
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Resizes the image or SArray of Images to a specific width, height, and
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train | _convert_1bit_array_to_byte_array | Convert bit array to byte array.
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Convert bit array to byte array.
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numpy.array
1D numpy array of type uint8
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train | _decompose_bytes_to_bit_arr | Unpack bytes to bits
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Byte Stream, as a list of uint8 values
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-------
bit_arr: list
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Unpack bytes to bits
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Byte Stream, as a list of uint8 values
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Decomposed bit stream as a list of 0/1s of length (len(arr) * 8)
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Unpack bytes to bits
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Weight blob to be quantized
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Generate a linear lookup table.
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Number of bits to represent a quantized weight value
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Weight blob to be quantized
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-------
lookup_table: numpy.array
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train | _get_kmeans_lookup_table_and_weight | Generate K-Means lookup table given a weight parameter field
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Generate K-Means lookup table given a weight parameter field
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Weight as numpy array
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Generate K-Means lookup table given a weight parameter field
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train | _quantize_channelwise_linear | Linearly quantize weight blob.
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Weight to be quantized.
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Number of bits per weight element
:param axis: int
Axis of the weight blob to compute channel-wise quantization, can be 0 or 1
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Linearly quantize weight blob.
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Number of bits per weight element
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train | _quantize_wp | Quantize the weight blob
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Quantization mode
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Weight parameters
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train | _quantize_wp_field | Quantize WeightParam field in Neural Network Protobuf
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WeightParam field
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Number of bits to be quantized
:param qm: str
Quantization mode
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Quantize WeightParam field in Neural Network Protobuf
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WeightParam field
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Number of bits to be quantized
:param qm: str
Quantization mode
:pa... | def _quantize_wp_field(wp, nbits, qm, shape, axis=0, **kwargs):
"""
Quantize WeightParam field in Neural Network Protobuf
:param wp: MLModel.NeuralNetwork.WeightParam
WeightParam field
:param nbits: int
Number of bits to be quantized
:param qm: str
Quantization mode
:pa... | [
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train | compare_models | Utility function to compare the performance of a full precision vs quantized model
:param full_precision_model: MLModel
The full precision model with float32 weights
:param quantized_model: MLModel
Quantized version of the model with quantized weights
:param sample_data: str | [dict]
... | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | def compare_models(full_precision_model, quantized_model,
sample_data):
"""
Utility function to compare the performance of a full precision vs quantized model
:param full_precision_model: MLModel
The full precision model with float32 weights
:param quantized_model... | def compare_models(full_precision_model, quantized_model,
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"""
Utility function to compare the performance of a full precision vs quantized model
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train | quantize_weights | Utility function to convert a full precision (float) MLModel to a
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:param full_precision_model: MLModel
Model which will be converted to half precision. Currently conversion
for only neural network models is supported. If a pipeline model is
passed in th... | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | def quantize_weights(full_precision_model,
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Utility function to convert a full precision (float) MLModel to a
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Utility function to convert a full precision (float) MLModel to a
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train | create | Create a recommender that uses item-item similarities based on
users in common.
Parameters
----------
observation_data : SFrame
The dataset to use for training the model. It must contain a column of
user ids and a column of item ids. Each row represents an observed
interaction b... | src/unity/python/turicreate/toolkits/recommender/item_similarity_recommender.py | def create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
nearest_items=None,
similarity_type='jaccard',
threshold=0.001,
only_top_k=64,
verbose=True,
target_memory_usage = 8*102... | def create(observation_data,
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nearest_items=None,
similarity_type='jaccard',
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verbose=True,
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train | _get_elementwise_name_from_keras_layer | Get the keras layer name from the activation name. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def _get_elementwise_name_from_keras_layer(keras_layer):
"""
Get the keras layer name from the activation name.
"""
if isinstance(keras_layer, _keras.layers.Add):
return 'ADD'
elif isinstance(keras_layer, _keras.layers.Multiply):
return 'MULTIPLY'
elif isinstance(keras_layer, _ke... | def _get_elementwise_name_from_keras_layer(keras_layer):
"""
Get the keras layer name from the activation name.
"""
if isinstance(keras_layer, _keras.layers.Add):
return 'ADD'
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train | convert_dense | Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_dense(builder, layer, input_names, output_names, keras_layer):
"""
Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and o... | def convert_dense(builder, layer, input_names, output_names, keras_layer):
"""
Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_embedding | Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_embedding(builder, layer, input_names, output_names, keras_layer):
"""Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and ou... | def convert_embedding(builder, layer, input_names, output_names, keras_layer):
"""Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_activation | Convert an activation layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_activation(builder, layer, input_names, output_names, keras_layer):
"""
Convert an activation layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get ... | def convert_activation(builder, layer, input_names, output_names, keras_layer):
"""
Convert an activation layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_advanced_relu | Convert an ReLU layer with maximum value from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer):
"""
Convert an ReLU layer with maximum value from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
... | def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer):
"""
Convert an ReLU layer with maximum value from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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train | convert_convolution | Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
_check... | def convert_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_convolution1d | Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_convolution1d(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get... | def convert_convolution1d(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_separable_convolution | Convert separable convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_separable_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert separable convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.... | def convert_separable_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert separable convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
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train | convert_batchnorm | Convert a Batch Normalization layer.
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Convert a Batch Normalization layer.
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
i... | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Convert a Batch Normalization layer.
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
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train | convert_flatten | Convert a flatten layer from keras to coreml.
----------
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_flatten(builder, layer, input_names, output_names, keras_layer):
"""
Convert a flatten layer from keras to coreml.
----------
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, ou... | def convert_flatten(builder, layer, input_names, output_names, keras_layer):
"""
Convert a flatten layer from keras to coreml.
----------
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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train | convert_merge | Convert concat layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_merge(builder, layer, input_names, output_names, keras_layer):
"""
Convert concat layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and ou... | def convert_merge(builder, layer, input_names, output_names, keras_layer):
"""
Convert concat layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_pooling | Convert pooling layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_pooling(builder, layer, input_names, output_names, keras_layer):
"""
Convert pooling layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
_check_data_for... | def convert_pooling(builder, layer, input_names, output_names, keras_layer):
"""
Convert pooling layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_padding | Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_padding(builder, layer, input_names, output_names, keras_layer):
"""
Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
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Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
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train | convert_cropping | Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_cropping(builder, layer, input_names, output_names, keras_layer):
"""
Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural ... | def convert_cropping(builder, layer, input_names, output_names, keras_layer):
"""
Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
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----------
keras_layer: layer
A keras layer object.
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train | convert_upsample | Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_upsample(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
_check_dat... | def convert_upsample(builder, layer, input_names, output_names, keras_layer):
"""
Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_permute | Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
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A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_permute(builder, layer, input_names, output_names, keras_layer):
"""
Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
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A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, o... | def convert_permute(builder, layer, input_names, output_names, keras_layer):
"""
Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
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A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_simple_rnn | Convert an SimpleRNN layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_simple_rnn(builder, layer, input_names, output_names, keras_layer):
"""
Convert an SimpleRNN layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get i... | def convert_simple_rnn(builder, layer, input_names, output_names, keras_layer):
"""
Convert an SimpleRNN layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_lstm | Convert an LSTM layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_lstm(builder, layer, input_names, output_names, keras_layer):
"""
Convert an LSTM layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = ker... | def convert_lstm(builder, layer, input_names, output_names, keras_layer):
"""
Convert an LSTM layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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train | convert_gru | Convert a GRU layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_gru(builder, layer, input_names, output_names, keras_layer):
"""
Convert a GRU layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = keras_... | def convert_gru(builder, layer, input_names, output_names, keras_layer):
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Convert a GRU layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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train | convert_bidirectional | Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | def convert_bidirectional(builder, layer, input_names, output_names, keras_layer):
"""
Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
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"""
Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
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train | SimpleInstructions.SLICE_0 | obj[:] | src/unity/python/turicreate/meta/decompiler/simple_instructions.py | def SLICE_0(self, instr):
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train | SimpleInstructions.STORE_SLICE_1 | obj[lower:] = expr | src/unity/python/turicreate/meta/decompiler/simple_instructions.py | def STORE_SLICE_1(self, instr):
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train | SimpleInstructions.STORE_SLICE_3 | obj[lower:upper] = expr | src/unity/python/turicreate/meta/decompiler/simple_instructions.py | def STORE_SLICE_3(self, instr):
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train | SimpleInstructions.DELETE_SLICE_0 | obj[:] = expr | src/unity/python/turicreate/meta/decompiler/simple_instructions.py | def DELETE_SLICE_0(self, instr):
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train | create | Create a content-based recommender model in which the similarity
between the items recommended is determined by the content of
those items rather than learned from user interaction data.
The similarity score between two items is calculated by first
computing the similarity between the item data for eac... | src/unity/python/turicreate/toolkits/recommender/item_content_recommender.py | def create(item_data, item_id,
observation_data = None,
user_id = None, target = None,
weights = 'auto',
similarity_metrics = 'auto',
item_data_transform = 'auto',
max_item_neighborhood_size = 64, verbose=True):
"""Create a content-based recommender... | def create(item_data, item_id,
observation_data = None,
user_id = None, target = None,
weights = 'auto',
similarity_metrics = 'auto',
item_data_transform = 'auto',
max_item_neighborhood_size = 64, verbose=True):
"""Create a content-based recommender... | [
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train | lhs | Return a set of symbols in `node` that are assigned.
:param node: ast node
:returns: set of strings. | src/unity/python/turicreate/meta/asttools/visitors/cond_symbol_visitor.py | def lhs(node):
'''
Return a set of symbols in `node` that are assigned.
:param node: ast node
:returns: set of strings.
'''
gen = ConditionalSymbolVisitor()
if isinstance(node, (list, tuple)):
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return gen.lhs | def lhs(node):
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Return a set of symbols in `node` that are assigned.
:param node: ast node
:returns: set of strings.
'''
gen = ConditionalSymbolVisitor()
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gen.visit(node)
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train | conditional_lhs | Group outputs into conditional and stable
:param node: ast node
:returns: tuple of (conditional, stable) | src/unity/python/turicreate/meta/asttools/visitors/cond_symbol_visitor.py | def conditional_lhs(node):
'''
Group outputs into conditional and stable
:param node: ast node
:returns: tuple of (conditional, stable)
'''
gen = ConditionalSymbolVisitor()
gen.visit(node)
return gen.cond_lhs, gen.stable_lhs | def conditional_lhs(node):
'''
Group outputs into conditional and stable
:param node: ast node
:returns: tuple of (conditional, stable)
'''
gen = ConditionalSymbolVisitor()
gen.visit(node)
return gen.cond_lhs, gen.stable_lhs | [
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train | conditional_symbols | Group lhs and rhs into conditional, stable and undefined
:param node: ast node
:returns: tuple of (conditional_lhs, stable_lhs),(conditional_rhs, stable_rhs), undefined | src/unity/python/turicreate/meta/asttools/visitors/cond_symbol_visitor.py | def conditional_symbols(node):
'''
Group lhs and rhs into conditional, stable and undefined
:param node: ast node
:returns: tuple of (conditional_lhs, stable_lhs),(conditional_rhs, stable_rhs), undefined
'''
gen = ConditionalSymbolVisitor()
gen.visit(node)
lhs = gen.cond_lhs,... | def conditional_symbols(node):
'''
Group lhs and rhs into conditional, stable and undefined
:param node: ast node
:returns: tuple of (conditional_lhs, stable_lhs),(conditional_rhs, stable_rhs), undefined
'''
gen = ConditionalSymbolVisitor()
gen.visit(node)
lhs = gen.cond_lhs,... | [
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train | _loadlib | Load rabit library. | src/external/xgboost/subtree/rabit/wrapper/rabit.py | def _loadlib(lib='standard'):
"""Load rabit library."""
global _LIB
if _LIB is not None:
warnings.warn('rabit.int call was ignored because it has'\
' already been initialized', level=2)
return
if lib == 'standard':
_LIB = ctypes.cdll.LoadLibrary(WRAPPER_... | def _loadlib(lib='standard'):
"""Load rabit library."""
global _LIB
if _LIB is not None:
warnings.warn('rabit.int call was ignored because it has'\
' already been initialized', level=2)
return
if lib == 'standard':
_LIB = ctypes.cdll.LoadLibrary(WRAPPER_... | [
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train | init | Intialize the rabit module, call this once before using anything.
Parameters
----------
args: list of str, optional
The list of arguments used to initialized the rabit
usually you need to pass in sys.argv.
Defaults to sys.argv when it is None.
lib: {'standard', 'mock', 'mpi'}
... | src/external/xgboost/subtree/rabit/wrapper/rabit.py | def init(args=None, lib='standard'):
"""Intialize the rabit module, call this once before using anything.
Parameters
----------
args: list of str, optional
The list of arguments used to initialized the rabit
usually you need to pass in sys.argv.
Defaults to sys.argv when it is N... | def init(args=None, lib='standard'):
"""Intialize the rabit module, call this once before using anything.
Parameters
----------
args: list of str, optional
The list of arguments used to initialized the rabit
usually you need to pass in sys.argv.
Defaults to sys.argv when it is N... | [
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... | 74514c3f99e25b46f22c6e02977fe3da69221c2e |
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