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train | glob | Returns the list of files matching the given pattern in the
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supplied as portable paths. Each pattern should be non-absolute
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""" Returns the list of files matching the given pattern in the
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supplied as portable paths. Each pattern should be non-absolute
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train | glob_tree | Recursive version of GLOB. Builds the glob of files while
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train | glob_in_parents | Recursive version of GLOB which glob sall parent directories
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assert(isinstance(dir, str))
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train | _wrap_function_return | Recursively walks each thing in val, opening lists and dictionaries,
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train | _setattr_wrapper | A setattr wrapper call used only by _publish(). This ensures that anything
published into this module is also published into tc.extensions | src/unity/python/turicreate/extensions.py | def _setattr_wrapper(mod, key, value):
"""
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setattr(mod, key, value)
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setattr(mod, key, value)
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train | _run_toolkit_function | Dispatches arguments to a toolkit function.
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fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
The arguments that were passed
kwargs : dictionary
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"""
Dispatches arguments to a toolkit function.
Parameters
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fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
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Dispatches arguments to a toolkit function.
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fnname : string
The toolkit function to run
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train | _class_instance_from_name | class_name is of the form modA.modB.modC.class module_path splits on "."
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train | _create_class_instance | Look for the class in .extensions in case it has already been
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train | _publish | Publishes all functions and classes registered in unity_server.
The functions and classes will appear in the module turicreate.extensions | src/unity/python/turicreate/extensions.py | def _publish():
import copy
"""
Publishes all functions and classes registered in unity_server.
The functions and classes will appear in the module turicreate.extensions
"""
unity = _get_unity()
fnlist = unity.list_toolkit_functions()
# Loop through all the functions and inject it into
... | def _publish():
import copy
"""
Publishes all functions and classes registered in unity_server.
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unity = _get_unity()
fnlist = unity.list_toolkit_functions()
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train | ext_import | Loads a turicreate toolkit module (a shared library) into the
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Toolkit module created via SDK can either be directly imported,
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Loads a turicreate toolkit module (a shared library) into the
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Toolkit module created via SDK can either be directly imported,
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Loads a turicreate toolkit module (a shared library) into the
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train | _get_argument_list_from_toolkit_function_name | Given a toolkit function name, return the argument list | src/unity/python/turicreate/extensions.py | def _get_argument_list_from_toolkit_function_name(fn):
"""
Given a toolkit function name, return the argument list
"""
unity = _get_unity()
fnprops = unity.describe_toolkit_function(fn)
argnames = fnprops['arguments']
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Given a toolkit function name, return the argument list
"""
unity = _get_unity()
fnprops = unity.describe_toolkit_function(fn)
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train | _descend_namespace | Given a globals dictionary, and a name of the form "a.b.c.d", recursively
walk the globals expanding caller_globals['a']['b']['c']['d'] returning
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"""
names = name.split('.')
cur = call... | def _descend_namespace(caller_globals, name):
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train | _ExtMetaPath.find_module | We have to see if fullname refers to a module we can import.
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train | main | Print lines of input along with output. | src/unity/python/doc/source/sphinx_turicreate_ext/pycon.py | def main():
"""
Print lines of input along with output.
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console = InteractiveInterpreter()
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source = ''
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train | GetTypeChecker | Returns a type checker for a message field of the specified types.
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Returns:
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train | TypeChecker.CheckValue | Type check the provided value and return it.
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train | python_source | Generate executable python source code from an ast node.
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:param file: file to write output to. | src/unity/python/turicreate/meta/asttools/visitors/pysourcegen.py | def python_source(ast, file=sys.stdout):
'''
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:param file: file to write output to.
'''
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gen.visit(ast)
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'''
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:param file: file to write output to.
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train | CEscape | Escape a bytes string for use in an ascii protocol buffer.
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text.encode('string_escape') does not seem to satisfy our needs as it
encodes unprintable characters using two-digit hex escapes whereas our
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train | CUnescape | Unescape a text string with C-style escape sequences to UTF-8 bytes. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/text_encoding.py | def CUnescape(text):
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train | reset | Clear the module state. This is mainly for testing purposes.
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type: a string
suffixes: None or a seque... | deps/src/boost_1_68_0/tools/build/src/build/type.py | def register (type, suffixes = [], base_type = None):
""" Registers a target type, possibly derived from a 'base-type'.
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""" Registers a target type, possibly derived from a 'base-type'.
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train | register_suffixes | Specifies that targets with suffix from 'suffixes' have the type 'type'.
If a different type is already specified for any of syffixes, issues an error. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def register_suffixes (suffixes, type):
""" Specifies that targets with suffix from 'suffixes' have the type 'type'.
If a different type is already specified for any of syffixes, issues an error.
"""
assert is_iterable_typed(suffixes, basestring)
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""" Specifies that targets with suffix from 'suffixes' have the type 'type'.
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assert is_iterable_typed(suffixes, basestring)
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train | set_scanner | Sets a scanner class that will be used for this 'type'. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def set_scanner (type, scanner):
""" Sets a scanner class that will be used for this 'type'.
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""" Sets a scanner class that will be used for this 'type'.
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from .scanner import Scanner
assert isinstance(type, basestring)
assert issubclass(scanner, Scanner)
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train | get_scanner | Returns a scanner instance appropriate to 'type' and 'property_set'. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def get_scanner (type, prop_set):
""" Returns a scanner instance appropriate to 'type' and 'property_set'.
"""
if __debug__:
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scanner_ty... | def get_scanner (type, prop_set):
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train | all_bases | Returns type and all of its bases, in the order of their distance from type. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def all_bases (type):
""" Returns type and all of its bases, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = []
while type:
result.append (type)
type = __types [type]['base']
return result | def all_bases (type):
""" Returns type and all of its bases, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = []
while type:
result.append (type)
type = __types [type]['base']
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train | all_derived | Returns type and all classes that derive from it, in the order of their distance from type. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def all_derived (type):
""" Returns type and all classes that derive from it, in the order of their distance from type.
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""" Returns type and all classes that derive from it, in the order of their distance from type.
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assert isinstance(type, basestring)
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train | is_derived | Returns true if 'type' is 'base' or has 'base' as its direct or indirect base. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def is_derived (type, base):
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train | is_subtype | Same as is_derived. Should be removed. | deps/src/boost_1_68_0/tools/build/src/build/type.py | def is_subtype (type, base):
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assert isinstance(type, basestring)
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train | set_generated_target_suffix | Sets a target suffix that should be used when generating target
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""" Sets a target suffix that should be used when generating target
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train | change_generated_target_suffix | Change the suffix previously registered for this type/properties
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""" Change the suffix previously registered for this type/properties
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"""
assert isinstance(type, basestring)
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train | generated_target_ps | Returns suffix that should be used when generating target of 'type',
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train | type | Returns file type given it's name. If there are several dots in filename,
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train | register_type | Register the given type on the specified OSes, or on remaining OSes
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train | ProgressTablePrinter.print_row | keys of kwargs must be the names passed to __init__(...) as `column_names` | src/unity/python/turicreate/util/_progress_table_printer.py | def print_row(self, **kwargs):
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train | process_features | Parameters
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train | pretty_print_list | Pretty print a list to be readable. | src/unity/python/turicreate/toolkits/_feature_engineering/_internal_utils.py | def pretty_print_list(lst, name = 'features', repr_format=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/_layers.py | def _get_elementwise_name_from_keras_layer(keras_layer):
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"""
Get the keras layer name from the activation name.
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mode = keras_layer.mode
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train | _same_elements_per_channel | Test if a 3D (H,W,C) matrix x has the same element in each (H,W) matrix for each channel | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | def _same_elements_per_channel(x):
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return False
... | def _same_elements_per_channel(x):
"""
Test if a 3D (H,W,C) matrix x has the same element in each (H,W) matrix for each channel
"""
eps = 1e-5
dims = x.shape
for c in range(dims[-1]):
xc = x[:,:,c].flatten()
if not np.all(np.absolute(xc - xc[0]) < eps):
return False
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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/_layers.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 output... | 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_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/_layers.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 input... | 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_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/_layers.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/_layers.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 netwo... | 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.
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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/_layers.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.
"""
# Get input and... | 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_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/_layers.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.
"""
# Get input ... | 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/_layers.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 inpu... | 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_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/_layers.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 = keras_la... | 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_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/_layers.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 input ... | 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_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/_layers.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_layer... | 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.
"""
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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/_layers.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
A ne... | 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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train | convert_batchnorm | Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], ... | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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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/_layers.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, output... | 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_softmax | Convert a softmax layer from keras to coreml.
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----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | def convert_softmax(builder, layer, input_names, output_names, keras_layer):
"""Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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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.
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A keras layer object.
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train | sge_submit | customized submit script, that submit nslave jobs, each must contain args as parameter
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this usually includes th... | src/external/xgboost/subtree/rabit/tracker/rabit_sge.py | def sge_submit(nslave, worker_args, worker_envs):
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nslave number of slave process to start up
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train | dump_ast | Returns a string representing the ast.
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train | ctypes2numpy | Convert a ctypes pointer array to a numpy array. | src/external/xgboost/python-package/xgboost/core.py | def ctypes2numpy(cptr, length, dtype):
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train | _maybe_from_pandas | Extract internal data from pd.DataFrame | src/external/xgboost/python-package/xgboost/core.py | def _maybe_from_pandas(data, feature_names, feature_types):
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train | DMatrix._init_from_csr | Initialize data from a CSR matrix. | src/external/xgboost/python-package/xgboost/core.py | def _init_from_csr(self, csr):
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train | DMatrix._init_from_csc | Initialize data from a CSC matrix. | src/external/xgboost/python-package/xgboost/core.py | def _init_from_csc(self, csc):
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train | DMatrix._init_from_npy2d | Initialize data from a 2-D numpy matrix. | src/external/xgboost/python-package/xgboost/core.py | def _init_from_npy2d(self, mat, missing):
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train | DMatrix.get_float_info | Get float property from the DMatrix.
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The field name of the information
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a numpy array of float information of the data
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train | DMatrix.get_uint_info | Get unsigned integer property from the DMatrix.
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train | DMatrix.set_float_info | Set float type property into the DMatrix.
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field: str
The field name of the information
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train | DMatrix.set_uint_info | Set uint type property into the DMatrix.
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field: str
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train | DMatrix.save_binary | Save DMatrix to an XGBoost buffer.
Parameters
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fname : string
Name of the output buffer file.
silent : bool (optional; default: True)
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Parameters
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fname : string
Name of the output buffer file.
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fname : string
Name of the output buffer file.
silent : bool (optional; default: True)
If set, the output is suppressed.
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train | DMatrix.num_row | Get the number of rows in the DMatrix.
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"""Get the number of rows in the DMatrix.
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number of rows : int
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Returns
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number of rows : int
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train | DMatrix.num_col | Get the number of columns (features) in the DMatrix.
Returns
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train | DMatrix.slice | Slice the DMatrix and return a new DMatrix that only contains `rindex`.
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rindex : list
List of indices to be selected.
Returns
-------
res : DMatrix
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rindex : list
List of indices to be selected.
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List of indices to be selected.
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train | DMatrix.feature_names | Set feature names (column labels).
Parameters
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feature_names : list or None
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Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if not feature_names is None:
# validate feature name
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"""Set feature names (column labels).
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feature_names : list or None
Labels for features. None will reset existing feature names
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if not feature_names is None:
# validate feature name
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train | DMatrix.feature_types | Set feature types (column types).
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train | Booster.update | Update for one iteration, with objective function calculated internally.
Parameters
----------
dtrain : DMatrix
Training data.
iteration : int
Current iteration number.
fobj : function
Customized objective function. | src/external/xgboost/python-package/xgboost/core.py | def update(self, dtrain, iteration, fobj=None):
"""
Update for one iteration, with objective function calculated internally.
Parameters
----------
dtrain : DMatrix
Training data.
iteration : int
Current iteration number.
fobj : function
... | def update(self, dtrain, iteration, fobj=None):
"""
Update for one iteration, with objective function calculated internally.
Parameters
----------
dtrain : DMatrix
Training data.
iteration : int
Current iteration number.
fobj : function
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train | Booster.boost | Boost the booster for one iteration, with customized gradient statistics.
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dtrain : DMatrix
The training DMatrix.
grad : list
The first order of gradient.
hess : list
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Boost the booster for one iteration, with customized gradient statistics.
Parameters
----------
dtrain : DMatrix
The training DMatrix.
grad : list
The first order of gradient.
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... | def boost(self, dtrain, grad, hess):
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Boost the booster for one iteration, with customized gradient statistics.
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----------
dtrain : DMatrix
The training DMatrix.
grad : list
The first order of gradient.
hess : list
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train | Booster.eval_set | Evaluate a set of data.
Parameters
----------
evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
feval : function
Custom evaluation function.
Returns
-------
res... | src/external/xgboost/python-package/xgboost/core.py | def eval_set(self, evals, iteration=0, feval=None):
# pylint: disable=invalid-name
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Parameters
----------
evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
... | def eval_set(self, evals, iteration=0, feval=None):
# pylint: disable=invalid-name
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Parameters
----------
evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
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train | Booster.predict | Predict with data.
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For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call bst.copy() to make copies
of model object and then call predict
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Predict with data.
NOTE: This function is not thread safe.
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train | Booster.save_raw | Save the model to a in memory buffer represetation
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-------
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a in memory buffer represetation of the model
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train | Booster.load_model | Load the model from a file.
Parameters
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fname : string or a memory buffer
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"""
Load the model from a file.
Parameters
----------
fname : string or a memory buffer
Input file name or memory buffer(see also save_raw)
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Load the model from a file.
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fname : string or a memory buffer
Input file name or memory buffer(see also save_raw)
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train | Booster.dump_model | Dump model into a text file.
Parameters
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foout : string
Output file name.
fmap : string, optional
Name of the file containing feature map names.
with_stats : bool (optional)
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Dump model into a text file.
Parameters
----------
foout : string
Output file name.
fmap : string, optional
Name of the file containing feature map names.
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Dump model into a text file.
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----------
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Output file name.
fmap : string, optional
Name of the file containing feature map names.
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train | Booster.get_dump | Returns the dump the model as a list of strings. | src/external/xgboost/python-package/xgboost/core.py | def get_dump(self, fmap='', with_stats=False):
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Returns the dump the model as a list of strings.
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train | Booster.get_fscore | Get feature importance of each feature.
Parameters
----------
fmap: str (optional)
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fmap: str (optional)
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train | Booster._validate_features | Validate Booster and data's feature_names are identical.
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train | disassembler | Disassemble a code object.
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train | transform | Matches all elements of 'list' agains the 'pattern'
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train | replace | Replaces occurrences of a match string in a given
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s (str): the string to modify
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s (str): the string to modify
pattern (str): the search expression
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items (list): the list of strings to modify.
match (str): the search expression.
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Args:
items (list): the list of strings to modify.
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train | create | Create a topic model from the given data set. A topic model assumes each
document is a mixture of a set of topics, where for each topic some words
are more likely than others. One statistical approach to do this is called a
"topic model". This method learns a topic model for the given document
collectio... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def create(dataset,
num_topics=10,
initial_topics=None,
alpha=None,
beta=.1,
num_iterations=10,
num_burnin=5,
associations=None,
verbose=False,
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method='auto'):... | def create(dataset,
num_topics=10,
initial_topics=None,
alpha=None,
beta=.1,
num_iterations=10,
num_burnin=5,
associations=None,
verbose=False,
print_interval=10,
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train | perplexity | Compute the perplexity of a set of test documents given a set
of predicted topics.
Let theta be the matrix of document-topic probabilities, where
theta_ik = p(topic k | document i). Let Phi be the matrix of term-topic
probabilities, where phi_jk = p(word j | topic k).
Then for each word in each do... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def perplexity(test_data, predictions, topics, vocabulary):
"""
Compute the perplexity of a set of test documents given a set
of predicted topics.
Let theta be the matrix of document-topic probabilities, where
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"""
Compute the perplexity of a set of test documents given a set
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train | TopicModel._get_summary_struct | Returns a structured description of the model, including (where relevant)
the schema of the training data, description of the training data,
training statistics, and model hyperparameters.
Returns
-------
sections : list (of list of tuples)
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train | TopicModel._get | Return the value of a given field. The list of all queryable fields is
detailed below, and can be obtained with the
:py:func:`~TopicModel._list_fields` method.
+-----------------------+----------------------------------------------+
| Field | Description ... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def _get(self, field):
"""
Return the value of a given field. The list of all queryable fields is
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+-----------------------+----------------------------------------------+
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train | TopicModel._training_stats | Return a dictionary of statistics collected during creation of the
model. These statistics are also available with the ``get`` method and
are described in more detail in that method's documentation.
Returns
-------
out : dict
Dictionary of statistics compiled during ... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def _training_stats(self):
"""
Return a dictionary of statistics collected during creation of the
model. These statistics are also available with the ``get`` method and
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Returns
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out : dict
... | def _training_stats(self):
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Return a dictionary of statistics collected during creation of the
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train | TopicModel.get_topics | Get the words associated with a given topic. The score column is the
probability of choosing that word given that you have chosen a
particular topic.
Parameters
----------
topic_ids : list of int, optional
The topics to retrieve words. Topic ids are zero-based.
... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def get_topics(self, topic_ids=None, num_words=5, cdf_cutoff=1.0,
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"""
Get the words associated with a given topic. The score column is the
probability of choosing that word given that you have chosen a
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Get the words associated with a given topic. The score column is the
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train | TopicModel.predict | Use the model to predict topics for each document. The provided
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representing a single document in bag-of-words format, where keys
are words and values are their corresponding counts. If `dataset` is
an SFrame, then it must... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def predict(self, dataset, output_type='assignment', num_burnin=None):
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Use the model to predict topics for each document. The provided
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representing a single document in bag-of-words format, where keys
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train | TopicModel.evaluate | Estimate the model's ability to predict new data. Imagine you have a
corpus of books. One common approach to evaluating topic models is to
train on the first half of all of the books and see how well the model
predicts the second half of each book.
This method returns a metric called pe... | src/unity/python/turicreate/toolkits/topic_model/topic_model.py | def evaluate(self, train_data, test_data=None, metric='perplexity'):
"""
Estimate the model's ability to predict new data. Imagine you have a
corpus of books. One common approach to evaluating topic models is to
train on the first half of all of the books and see how well the model
... | def evaluate(self, train_data, test_data=None, metric='perplexity'):
"""
Estimate the model's ability to predict new data. Imagine you have a
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train | bbox_to_ybox | Convert from corner bounding box to center/shape | src/unity/python/turicreate/toolkits/object_detector/_detection.py | def bbox_to_ybox(bbox):
"""Convert from corner bounding box to center/shape"""
return [
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(bbox[0] + bbox[2]) / 2,
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train | _raise_error_if_not_drawing_classifier_input_sframe | Performs some sanity checks on the SFrame provided as input to
`turicreate.drawing_classifier.create` and raises a ToolkitError
if something in the dataset is missing or wrong. | src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py | def _raise_error_if_not_drawing_classifier_input_sframe(
dataset, feature, target):
"""
Performs some sanity checks on the SFrame provided as input to
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"""
from turicreate.toolki... | def _raise_error_if_not_drawing_classifier_input_sframe(
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Performs some sanity checks on the SFrame provided as input to
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train | create | Create a :class:`DrawingClassifier` model.
Parameters
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dataset : SFrame
Input data. The columns named by the ``feature`` and ``target``
parameters will be extracted for training the drawing classifier.
target : string
Name of the column containing the target variable.... | src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py | def create(input_dataset, target, feature=None, validation_set='auto',
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max_iterations=100, verbose=True):
"""
Create a :class:`DrawingClassifier` model.
Parameters
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dataset : SFrame
Input data. The columns named by the ``... | def create(input_dataset, target, feature=None, validation_set='auto',
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"""
Create a :class:`DrawingClassifier` model.
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train | DrawingClassifier.export_coreml | Save the model in Core ML format. The Core ML model takes a grayscale
drawing of fixed size as input and produces two outputs:
`classLabel` and `labelProbabilities`.
The first one, `classLabel` is an integer or string (depending on the
classes the model was trained on) to store the la... | src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py | def export_coreml(self, filename, verbose=False):
"""
Save the model in Core ML format. The Core ML model takes a grayscale
drawing of fixed size as input and produces two outputs:
`classLabel` and `labelProbabilities`.
The first one, `classLabel` is an integer or string (depe... | def export_coreml(self, filename, verbose=False):
"""
Save the model in Core ML format. The Core ML model takes a grayscale
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train | DrawingClassifier._predict_with_probabilities | Predict with probabilities. The core prediction part that both
`evaluate` and `predict` share.
Returns an SFrame with two columns, self.target, and "probabilities".
The column with column name, self.target, contains the predictions made
by the model for the provided dataset.
... | src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py | def _predict_with_probabilities(self, input_dataset, batch_size=None,
verbose=True):
"""
Predict with probabilities. The core prediction part that both
`evaluate` and `predict` share.
Returns an SFrame with two columns, self.target, and "probabilities".
The column wit... | def _predict_with_probabilities(self, input_dataset, batch_size=None,
verbose=True):
"""
Predict with probabilities. The core prediction part that both
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