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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.dtype | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int... | python | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int... | [
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>>> x = mx.nd.zeros((2,3))
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray._fresh_grad | def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
... | python | def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.asnumpy | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., ... | python | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., ... | [
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>>> type(y)
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>>> y
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.astype | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
... | python | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.copyto | def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray``... | python | def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray``... | [
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.as_in_context | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
... | python | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
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Returns
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.attach_grad | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'wr... | python | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.grad | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | python | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | [
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.detach | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | python | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | [
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apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.backward | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to re... | python | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
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apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.build | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
... | python | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
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apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.sentence | def sentence(self, padding=75):
"""
Get sentence
"""
vec = word_to_vector(self.sentence_str)
vec += [-1] * (padding - self.sentence_length)
return np.array(vec, dtype=np.int32) | python | def sentence(self, padding=75):
"""
Get sentence
"""
vec = word_to_vector(self.sentence_str)
vec += [-1] * (padding - self.sentence_length)
return np.array(vec, dtype=np.int32) | [
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apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.word | def word(self, _id, padding=75):
"""
Get words
"""
word = self.words[_id][2]
vec = word_to_vector(word)
vec += [-1] * (padding - len(vec))
return np.array(vec, dtype=np.int32) | python | def word(self, _id, padding=75):
"""
Get words
"""
word = self.words[_id][2]
vec = word_to_vector(word)
vec += [-1] * (padding - len(vec))
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apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.word_frame_pos | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) | python | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) | [
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apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.prepare_sparse_params | def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
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'''Prepares the module for processing a data batch by pulling row_sparse
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Parameters
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param_rowids : dict of str to NDArray of list of NDArrays
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apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.save_params | def save_params(self, fname):
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Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
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"""Saves model parameters to file.
Parameters
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fname : str
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>>> # An example of saving module parameters.
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apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.get_params_from_kv | def get_params_from_kv(self, arg_params, aux_params):
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arg_params : list of NDArray
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aux_params : list of NDArray
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arg_params : list of NDArray
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apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.clip_by_global_norm_per_ctx | def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
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apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.rescale_grad | def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
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""" Rescale the gradient of provided parameters by a certain scale """
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apache/incubator-mxnet | example/sparse/factorization_machine/model.py | factorization_machine_model | def factorization_machine_model(factor_size, num_features,
lr_mult_config, wd_mult_config, init_config):
""" builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
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x = mx.symbol.Variable("... | python | def factorization_machine_model(factor_size, num_features,
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""" builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
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apache/incubator-mxnet | example/rnn/word_lm/data.py | batchify | def batchify(data, batch_size):
"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
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"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
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apache/incubator-mxnet | example/rnn/word_lm/data.py | Corpus.tokenize | def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
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with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
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"""Tokenizes a text file."""
assert os.path.exists(path)
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with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
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apache/incubator-mxnet | python/mxnet/symbol_doc.py | _build_doc | def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_v... | python | def _build_doc(func_name,
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param_str = _build_param_doc(arg_names, arg_types, arg_desc)
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apache/incubator-mxnet | python/mxnet/symbol_doc.py | SymbolDoc.get_output_shape | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
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"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | [
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apache/incubator-mxnet | python/mxnet/context.py | num_gpus | def num_gpus():
"""Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
"""
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value | python | def num_gpus():
"""Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
"""
count = ctypes.c_int()
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apache/incubator-mxnet | python/mxnet/context.py | gpu_memory_info | def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
(free, total) : ... | python | def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
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-------
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apache/incubator-mxnet | python/mxnet/context.py | current_context | def current_context():
"""Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
Examples
-------
>>> mx.current_context()
cpu(0)
>>> with... | python | def current_context():
"""Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
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apache/incubator-mxnet | example/gluon/audio/urban_sounds/datasets.py | AudioFolderDataset._list_audio_files | def _list_audio_files(self, root, skip_rows=0):
"""Populates synsets - a map of index to label for the data items.
Populates the data in the dataset, making tuples of (data, label)
"""
self.synsets = []
self.items = []
if not self._train_csv:
# The audio files... | python | def _list_audio_files(self, root, skip_rows=0):
"""Populates synsets - a map of index to label for the data items.
Populates the data in the dataset, making tuples of (data, label)
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self.synsets = []
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apache/incubator-mxnet | example/gluon/audio/urban_sounds/datasets.py | AudioFolderDataset.transform_first | def transform_first(self, fn, lazy=False):
"""Returns a new dataset with the first element of each sample
transformed by the transformer function `fn`.
This is useful, for example, when you only want to transform data
while keeping label as is.
lazy=False is passed to transform_... | python | def transform_first(self, fn, lazy=False):
"""Returns a new dataset with the first element of each sample
transformed by the transformer function `fn`.
This is useful, for example, when you only want to transform data
while keeping label as is.
lazy=False is passed to transform_... | [
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apache/incubator-mxnet | python/setup.py | config_cython | def config_cython():
"""Try to configure cython and return cython configuration"""
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
try:
... | python | def config_cython():
"""Try to configure cython and return cython configuration"""
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
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apache/incubator-mxnet | python/mxnet/_ctypes/symbol.py | SymbolBase._compose | def _compose(self, *args, **kwargs):
"""Compose symbol on inputs.
This call mutates the current symbol.
Parameters
----------
args:
provide positional arguments
kwargs:
provide keyword arguments
Returns
-------
the resul... | python | def _compose(self, *args, **kwargs):
"""Compose symbol on inputs.
This call mutates the current symbol.
Parameters
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args:
provide positional arguments
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provide keyword arguments
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apache/incubator-mxnet | python/mxnet/_ctypes/symbol.py | SymbolBase._set_attr | def _set_attr(self, **kwargs):
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Parameters
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**kwargs
The attributes to set
"""
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))... | python | def _set_attr(self, **kwargs):
"""Set the attribute of the symbol.
Parameters
----------
**kwargs
The attributes to set
"""
keys = c_str_array(kwargs.keys())
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apache/incubator-mxnet | example/ssd/symbol/symbol_factory.py | get_config | def get_config(network, data_shape, **kwargs):
"""Configuration factory for various networks
Parameters
----------
network : str
base network name, such as vgg_reduced, inceptionv3, resnet...
data_shape : int
input data dimension
kwargs : dict
extra arguments
"""
... | python | def get_config(network, data_shape, **kwargs):
"""Configuration factory for various networks
Parameters
----------
network : str
base network name, such as vgg_reduced, inceptionv3, resnet...
data_shape : int
input data dimension
kwargs : dict
extra arguments
"""
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apache/incubator-mxnet | example/ssd/symbol/symbol_factory.py | get_symbol_train | def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
"""
if ne... | python | def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._set_trainer | def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set th... | python | def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._get_row_sparse | def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id... | python | def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._load_init | def _load_init(self, data, ctx):
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assert self_dim in (0, data_dim), \
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... | python | def _load_init(self, data, ctx):
"""(Re)initializes by loading from data."""
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._finish_deferred_init | def _finish_deferred_init(self):
"""Finishes deferred initialization."""
if not self._deferred_init:
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self._deferred_init = ()
assert self.shape is not None and np.prod(self.shape) > 0, \
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"""Finishes deferred initialization."""
if not self._deferred_init:
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._init_impl | def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
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while len(dev_list) <= ctx.device_id:
de... | python | def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._init_grad | def _init_grad(self):
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self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._reduce | def _reduce(self):
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ctx = context.cpu()
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block = self.list_data()
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# fetch all rows for 'row_sparse' para... | python | def _reduce(self):
"""Reduce data from multiple context to cpu."""
ctx = context.cpu()
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.initialize | def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(),
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Parameters
----------
init : Initializer
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.reset_ctx | def reset_ctx(self, ctx):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.set_data | def set_data(self, data):
"""Sets this parameter's value on all contexts."""
self.shape = data.shape
if self._data is None:
assert self._deferred_init, \
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self._deferred_init = self._deferred_init[:3] + (... | python | def set_data(self, data):
"""Sets this parameter's value on all contexts."""
self.shape = data.shape
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.row_sparse_data | def row_sparse_data(self, row_id):
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The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_row_sparse_data | def list_row_sparse_data(self, row_id):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.data | def data(self, ctx=None):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_data | def list_data(self):
"""Returns copies of this parameter on all contexts, in the same order
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instead.
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.grad | def grad(self, ctx=None):
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ctx : Context
Desired context.
"""
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gr... | python | def grad(self, ctx=None):
"""Returns a gradient buffer for this parameter on one context.
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ctx : Context
Desired context.
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_grad | def list_grad(self):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_ctx | def list_ctx(self):
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return self._ctx_lis... | python | def list_ctx(self):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.zero_grad | def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
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if self._grad is None:
return
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ndarray.zeros_like(i, out=i) | python | def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
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if self._grad is None:
return
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.var | def var(self):
"""Returns a symbol representing this parameter."""
if self._var is None:
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"""Returns a symbol representing this parameter."""
if self._var is None:
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.cast | def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self.dtype = dtype
if self._data is None:
return
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... | python | def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self.dtype = dtype
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.get | def get(self, name, **kwargs):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.get_constant | def get_constant(self, name, value=None):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.update | def update(self, other):
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.initialize | def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`
API. It has no effect when using :py:class:`Symbol` API.
Parameters
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... | python | def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.setattr | def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters.
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model's Parameters::
model.collect_params().setattr('grad_req', 'null')
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"""Set an attribute to a new value for all Parameters.
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apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.save | def save(self, filename, strip_prefix=''):
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filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving.
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filename : str
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apache/incubator-mxnet | python/mxnet/torch.py | _make_torch_function | def _make_torch_function(handle):
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"""Create a Torch function from the FunctionHandle."""
# Get the property of function
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apache/incubator-mxnet | python/mxnet/torch.py | _init_torch_module | def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module."""
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module_obj = sys.modul... | python | def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module."""
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apache/incubator-mxnet | python/mxnet/gluon/model_zoo/vision/inception.py | inception_v3 | def inception_v3(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567>`_ paper.
Parameters
----------
pretrained : bool, de... | python | def inception_v3(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567>`_ paper.
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apache/incubator-mxnet | python/mxnet/recordio.py | pack | def pack(header, s):
"""Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
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s ... | python | def pack(header, s):
"""Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
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apache/incubator-mxnet | python/mxnet/recordio.py | unpack | def unpack(s):
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s : str
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Header of the image record.
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"""Unpack a MXImageRecord to string.
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s : str
String buffer from ``MXRecordIO.read``.
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apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.open | def open(self):
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"""Opens the record file."""
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check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle)))
self.writable = True
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apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO._check_pid | def _check_pid(self, allow_reset=False):
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if not self.pid == current_process().pid:
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self.reset()
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apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.close | def close(self):
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self.pid = Non... | python | def close(self):
"""Closes the record file."""
if not self.is_open:
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apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.write | def write(self, buf):
"""Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : ... | python | def write(self, buf):
"""Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : ... | [
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apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.read | def read(self):
"""Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
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... item = record.read()
... print(item)
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"""Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
... print(item)
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apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.close | def close(self):
"""Closes the record file."""
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return
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self.fidx.close() | python | def close(self):
"""Closes the record file."""
if not self.is_open:
return
super(MXIndexedRecordIO, self).close()
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apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.seek | def seek(self, idx):
"""Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
reader pointer position. It doesn't return anything."""
assert not self.writable
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"""Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
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apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.tell | def tell(self):
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Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
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>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
..... | python | def tell(self):
"""Returns the current position of write head.
Examples
---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
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apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.write_idx | def write_idx(self, idx, buf):
"""Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
Parameters
----------
idx : int
Index of a file.
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"""Inserts input record at given index.
Examples
---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
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idx : int
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | _add_new_columns | def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added.
"""
#TODO(leodirac): we ... | python | def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe.
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
metrics : metric.EvalMetric
New metrics to be added.
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | args_wrapper | def args_wrapper(*args):
"""Generates callback arguments for model.fit()
for a set of callback objects.
Callback objects like PandasLogger(), LiveLearningCurve()
get passed in. This assembles all their callback arguments.
"""
out = defaultdict(list)
for callback in args:
callback_ar... | python | def args_wrapper(*args):
"""Generates callback arguments for model.fit()
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Callback objects like PandasLogger(), LiveLearningCurve()
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"""
out = defaultdict(list)
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.append_metrics | def append_metrics(self, metrics, df_name):
"""Append new metrics to selected dataframes.
Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified.
"""
dataframe = self._... | python | def append_metrics(self, metrics, df_name):
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Parameters
----------
metrics : metric.EvalMetric
New metrics to be added.
df_name : str
Name of the dataframe to be modified.
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.train_cb | def train_cb(self, param):
"""Callback funtion for training.
"""
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') | python | def train_cb(self, param):
"""Callback funtion for training.
"""
if param.nbatch % self.frequent == 0:
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger._process_batch | def _process_batch(self, param, dataframe):
"""Update parameters for selected dataframe after a completed batch
Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
"""
now = time.time()
if param.eval_metric is no... | python | def _process_batch(self, param, dataframe):
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Parameters
----------
dataframe : pandas.DataFrame
Selected dataframe needs to be modified.
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | PandasLogger.epoch_cb | def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time
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"""
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
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... | python | def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time
and append it to epoch dataframe.
"""
metrics = {}
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | LiveBokehChart._push_render | def _push_render(self):
"""Render the plot with bokeh.io and push to notebook.
"""
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() | python | def _push_render(self):
"""Render the plot with bokeh.io and push to notebook.
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bokeh.io.push_notebook(handle=self.handle)
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apache/incubator-mxnet | python/mxnet/notebook/callback.py | LiveLearningCurve._process_batch | def _process_batch(self, param, df_name):
"""Update selected dataframe after a completed batch
Parameters
----------
df_name : str
Selected dataframe name needs to be modified.
"""
if param.eval_metric is not None:
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df_name : str
Selected dataframe name needs to be modified.
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apache/incubator-mxnet | example/named_entity_recognition/src/ner.py | build_vocab | def build_vocab(nested_list):
"""
:param nested_list: list of list of string
:return: dictionary mapping from string to int, inverse of that dictionary
"""
# Build vocabulary
word_counts = Counter(itertools.chain(*nested_list))
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vocabulary_inv = [x[0] for x ... | python | def build_vocab(nested_list):
"""
:param nested_list: list of list of string
:return: dictionary mapping from string to int, inverse of that dictionary
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# Build vocabulary
word_counts = Counter(itertools.chain(*nested_list))
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apache/incubator-mxnet | example/named_entity_recognition/src/ner.py | sym_gen | def sym_gen(seq_len):
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apache/incubator-mxnet | python/mxnet/contrib/text/vocab.py | Vocabulary._index_unknown_and_reserved_tokens | def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens):
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self._unknown_token = unknown_token
# Thus, constants.UNKNOWN_IDX must be 0.
self._idx_to_token = [unknown_token]
if reserved_tokens is None:
sel... | python | def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens):
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apache/incubator-mxnet | python/mxnet/contrib/text/vocab.py | Vocabulary._index_counter_keys | def _index_counter_keys(self, counter, unknown_token, reserved_tokens, most_freq_count,
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Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and
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tokens : str or list of strs
A source token or tokens to be converted.
Returns
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int or list of ints
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indices : int or list of ints
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apache/incubator-mxnet | python/mxnet/io/io.py | _make_io_iterator | def _make_io_iterator(handle):
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arg_descs = ctypes.POINTER(ctypes.c_char_p)()
check_ca... | python | def _make_io_iterator(handle):
"""Create an io iterator by handle."""
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")",
",",
"label",
"=",
"self",
".",
"getlabel",
"(",
")",
",",
"pad",
"=",
"self",
".",
... | Get next data batch from iterator.
Returns
-------
DataBatch
The data of next batch.
Raises
------
StopIteration
If the end of the data is reached. | [
"Get",
"next",
"data",
"batch",
"from",
"iterator",
"."
] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/io/io.py#L208-L225 | train |
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