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train | reduce_mean | Reduce the array along a given axis by mean value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | [
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train | reduce_min | Reduce the array along a given axis by minimum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | [
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train | reduce_sum | Reduce the array along a given axis by sum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | [
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train | reduce_prod | Reduce the array along a given axis by product value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | [
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train | reduce_log_sum | Reduce the array along a given axis by log sum value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
... | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
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log_sym = symbol.log(sum_op)
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train | reduce_log_sum_exp | Reduce the array along a given axis by log sum exp value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep... | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
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train | reduce_sum_square | Reduce the array along a given axis by sum square value | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | [
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train | reduce_l1 | Reduce input tensor by l1 normalization. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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return 'n... | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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train | reduce_l2 | Reduce input tensor by l2 normalization. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | [
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train | avg_pooling | Average pooling | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def avg_pooling(attrs, inputs, proto_obj):
""" Average pooling"""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
... | def avg_pooling(attrs, inputs, proto_obj):
""" Average pooling"""
new_attrs = translation_utils._fix_attribute_names(attrs,
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'strides': 'stride',
... | [
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train | lp_pooling | LP Pooling | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def lp_pooling(attrs, inputs, proto_obj):
"""LP Pooling"""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
... | def lp_pooling(attrs, inputs, proto_obj):
"""LP Pooling"""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._fix_attribute_names(attrs,
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'strides': 'stride',
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train | max_roi_pooling | Max ROI Pooling. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
... | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
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'spatial_scale': 'spatial_scale'
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train | depthtospace | Rearranges data from depth into blocks of spatial data. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | [
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train | spacetodepth | Rearranges blocks of spatial data into depth. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | [
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train | hardmax | Returns batched one-hot vectors. | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_... | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
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train | lpnormalization | ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330 | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | def lpnormalization(attrs, inputs, proto_obj):
"""ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
... | def lpnormalization(attrs, inputs, proto_obj):
"""ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
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train | download_mp4 | download mp4s | example/gluon/lipnet/utils/download_data.py | def download_mp4(from_idx, to_idx, _params):
"""
download mp4s
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
save_folder = '{src_path}/{nm}'.format(src_path=_params['src_path'], nm=name)
if idx == 0 or os.path.isdir(save_folder):
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"""
download mp4s
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
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train | download_align | download aligns | example/gluon/lipnet/utils/download_data.py | def download_align(from_idx, to_idx, _params):
"""
download aligns
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succ = set()
fail = set()
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name = 's' + str(idx)
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train | run_ut_py3_qemu | Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet | ci/docker/qemu/runtime_functions.py | def run_ut_py3_qemu():
"""Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet"""
from vmcontrol import VM
with VM() as vm:
qemu_provision(vm.ssh_port)
logging.info("execute tests")
qemu_ssh(vm.ssh_port, "./runtime_functions.py... | def run_ut_py3_qemu():
"""Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet"""
from vmcontrol import VM
with VM() as vm:
qemu_provision(vm.ssh_port)
logging.info("execute tests")
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train | run_ut_python3_qemu_internal | this runs inside the vm | ci/docker/qemu/runtime_functions.py | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt") ... | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt") ... | [
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train | _get_subword_units | Return subword-units presentation, given a word/token. | example/nce-loss/text8_data.py | def _get_subword_units(token, gram):
"""Return subword-units presentation, given a word/token.
"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
return [t[i:i + gram] for i in range(0, len(t) - gram + 1)] | def _get_subword_units(token, gram):
"""Return subword-units presentation, given a word/token.
"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
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train | fit | Train the model using Caffe operator in MXNet | example/caffe/train_model.py | def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
"""Train the model using Caffe operator in MXNet"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
if 'log_file' in args and args.log_file i... | def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
"""Train the model using Caffe operator in MXNet"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
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train | Atari8080Preprocessor.preprocess | Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector. | example/reinforcement-learning/parallel_actor_critic/envs.py | def preprocess(self, img):
"""
Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
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"""
# Crop, down-sample, erase background and set foreground to 1.
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Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector.
"""
# Crop, down-sample, erase background and set foreground to 1.
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train | _new_empty_handle | Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle. | python/mxnet/ndarray/ndarray.py | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | [
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train | _new_alloc_handle | Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle. | python/mxnet/ndarray/ndarray.py | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
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"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
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train | _get_indexing_dispatch_code | Returns a dispatch code for calling basic or advanced indexing functions. | python/mxnet/ndarray/ndarray.py | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
... | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
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train | _get_index_range | Given start, stop, step and array length, return
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are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily... | python/mxnet/ndarray/ndarray.py | def _get_index_range(start, stop, length, step=1):
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absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
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"""Given start, stop, step and array length, return
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The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
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train | _get_oshape_of_gather_nd_op | Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd. | python/mxnet/ndarray/ndarray.py | def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
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This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
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train | _get_dim_size | Given start, stop, and stop, calculate the number of elements
of this slice. | python/mxnet/ndarray/ndarray.py | def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1)... | def _get_dim_size(start, stop, step):
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train | _get_broadcast_shape | Given two shapes that are not identical, find the shape
that both input shapes can broadcast to. | python/mxnet/ndarray/ndarray.py | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
... | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
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train | ones | Returns a new array filled with all ones, with the given shape and type.
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shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.cu... | python/mxnet/ndarray/ndarray.py | def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
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shape : int or tuple of int or list of int
The shape of the empty array.
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shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
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Device context (default is the current default context).
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Parameters
--------
shape : int or tuple of int
The shape of the new array.
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Fill value.
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shape : int or tuple of int
The shape of the new array.
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train | array | Creates an array from any object exposing the array interface.
Parameters
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source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
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"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
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"""Creates an array from any object exposing the array interface.
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An object exposing the array interface, an object whose `__array__`
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train | moveaxis | Moves the `source` axis into the `destination` position
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Parameters
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tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but... | python/mxnet/ndarray/ndarray.py | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original... | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
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Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
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train | arange | Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns an `NDArray`.... | python/mxnet/ndarray/ndarray.py | def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
... | def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t):
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Values are generated within the half-open interval [`start`, `stop`). In other
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train | _ufunc_helper | Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : functi... | python/mxnet/ndarray/ndarray.py | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.... | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
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train | add | Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are br... | python/mxnet/ndarray/ndarray.py | def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
... | def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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train | subtract | Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the array... | python/mxnet/ndarray/ndarray.py | def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them ha... | def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
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train | multiply | Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
... | python/mxnet/ndarray/ndarray.py | def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadca... | def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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train | divide | Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
... | python/mxnet/ndarray/ndarray.py | def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcas... | def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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train | modulo | Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
P... | python/mxnet/ndarray/ndarray.py | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcasta... | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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train | power | Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the ar... | python/mxnet/ndarray/ndarray.py | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them h... | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
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train | maximum | Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
... | python/mxnet/ndarray/ndarray.py | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a com... | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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Parameters
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"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
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otherwise return 0(false).
Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.
.. note::
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For each element in input arrays, return 1(true) if corresponding elements are same,
otherwise return 0(false).
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train | not_equal | Returns the result of element-wise **not equal to** (!=) comparison operation
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For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::... | python/mxnet/ndarray/ndarray.py | def not_equal(lhs, rhs):
"""Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_e... | def not_equal(lhs, rhs):
"""Returns the result of element-wise **not equal to** (!=) comparison operation
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train | greater | Returns the result of element-wise **greater than** (>) comparison operation
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For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
... | python/mxnet/ndarray/ndarray.py | def greater(lhs, rhs):
"""Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs,... | def greater(lhs, rhs):
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train | greater_equal | Returns the result of element-wise **greater than or equal to** (>=) comparison
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otherwise return 0(false).
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train | lesser | Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If ... | python/mxnet/ndarray/ndarray.py | def lesser(lhs, rhs):
"""Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)`... | def lesser(lhs, rhs):
"""Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
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train | lesser_equal | Returns the result of element-wise **lesser than or equal to** (<=) comparison
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For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs... | python/mxnet/ndarray/ndarray.py | def lesser_equal(lhs, rhs):
"""Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx... | def lesser_equal(lhs, rhs):
"""Returns the result of element-wise **lesser than or equal to** (<=) comparison
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For each element in input arrays, return 1(true) if lhs elements are
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train | logical_and | Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
... | python/mxnet/ndarray/ndarray.py | def logical_and(lhs, rhs):
"""Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logica... | def logical_and(lhs, rhs):
"""Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
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train | logical_or | Returns the result of element-wise **logical or** comparison
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For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
... | python/mxnet/ndarray/ndarray.py | def logical_or(lhs, rhs):
"""Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or... | def logical_or(lhs, rhs):
"""Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
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train | logical_xor | Returns the result of element-wise **logical xor** comparison
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For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.... | python/mxnet/ndarray/ndarray.py | def logical_xor(lhs, rhs):
"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadca... | def logical_xor(lhs, rhs):
"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadca... | [
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train | concatenate | DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool... | python/mxnet/ndarray/ndarray.py | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
T... | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
T... | [
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train | imdecode | DEPRECATED, use mx.img instead
Parameters
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str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
... | python/mxnet/ndarray/ndarray.py | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
... | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
... | [
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The shape of the empty array.
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train | split_v2 | Split an array into multiple sub-arrays.
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input data.
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train | from_dlpack | Returns a NDArray backed by a dlpack tensor.
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train | NDArray._get_index_nd | Returns an index array for use in scatter_nd and gather_nd. | python/mxnet/ndarray/ndarray.py | def _get_index_nd(self, key):
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train | NDArray._prepare_value_nd | Given value and vshape, create an `NDArray` from value with the same
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train | NDArray._set_nd_advanced_indexing | This function is called by __setitem__ when key is an advanced index. | python/mxnet/ndarray/ndarray.py | def _set_nd_advanced_indexing(self, key, value):
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train | NDArray._get_nd_basic_indexing | This function is called when key is a slice, or an integer,
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"""This function is called when key is a slice, or an integer,
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if isinstance(key, integer_types):
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train | NDArray._sync_copyfrom | Performs a synchronized copy from the `source_array` to the current array.
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is a `numpy.ndarray` or array-like object.
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This function blocks until all the pending read/write ... | def _sync_copyfrom(self, source_array):
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train | NDArray._slice | Returns a sliced NDArray that shares memory with the current one.
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start : int
Starting inclusive index of slice in the first dim.
stop : int
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start : int
Starting inclusive index of slice in the first dim.
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train | NDArray._at | Returns a view of the array sliced at `idx` in the first dim.
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index for slicing the `NDArray` in the first dim.
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idx : int
index for slicing the `NDArray` in the first dim.
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idx : int
index for slicing the `NDArray` in the first dim.
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train | NDArray.reshape | Returns a **view** of this array with a new shape without altering any data.
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shape : tuple of int, or n ints
The new shape should not change the array size, namely
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"""Returns a **view** of this array with a new shape without altering any data.
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----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
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"""Returns a **view** of this array with a new shape without altering any data.
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train | NDArray.broadcast_to | Broadcasts the input array to a new shape.
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For example, you could broadcast from shape (2, 1) to (2, 3), but not from
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"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, ... | def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
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train | NDArray.shape | Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L) | python/mxnet/ndarray/ndarray.py | def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
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>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
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train | NDArray.context | Device context of the array.
Examples
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>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0) | python/mxnet/ndarray/ndarray.py | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu... | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
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train | NDArray.dtype | Data-type of the array's elements.
Returns
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This NDArray's data type.
Examples
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>>> x = mx.nd.zeros((2,3))
>>> x.dtype
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>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
... | python/mxnet/ndarray/ndarray.py | def dtype(self):
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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... | def dtype(self):
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Returns
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numpy.dtype
This NDArray's data type.
Examples
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>>> x = mx.nd.zeros((2,3))
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<type 'numpy.float32'>
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train | NDArray._fresh_grad | Whether this array's corresponding gradient array
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`_fresh_grad` need to be manually set to False
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array). | python/mxnet/ndarray/ndarray.py | def _fresh_grad(self):
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train | NDArray.asnumpy | Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
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>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
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... | python/mxnet/ndarray/ndarray.py | 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., ... | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
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--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
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train | NDArray.astype | Returns a copy of the array after casting to a specified type.
Parameters
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dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context... | python/mxnet/ndarray/ndarray.py | 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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"""Returns a copy of the array after casting to a specified type.
Parameters
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dtype : numpy.dtype or str
The type of the returned array.
copy : bool
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train | NDArray.copyto | Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
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``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
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"""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
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If ``other`` is a context, a new ``NDArray``... | def copyto(self, other):
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train | NDArray.as_in_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
The target context.
Returns
... | python/mxnet/ndarray/ndarray.py | 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
... | 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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train | NDArray.attach_grad | Attach a gradient buffer to this NDArray, so that `backward`
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Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
... | python/mxnet/ndarray/ndarray.py | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
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Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
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Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
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train | NDArray.grad | Returns gradient buffer attached to this NDArray. | python/mxnet/ndarray/ndarray.py | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
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"""Returns gradient buffer attached to this NDArray."""
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train | NDArray.detach | Returns a new NDArray, detached from the current graph. | python/mxnet/ndarray/ndarray.py | 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)))
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"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
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train | NDArray.backward | 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 retain the computaion graph for another backward
pass on the same graph. ... | python/mxnet/ndarray/ndarray.py | 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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out_grad : NDArray, optional
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train | Align.build | Build the align array | example/gluon/lipnet/utils/align.py | 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(' ')
... | 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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train | Align.sentence | Get sentence | example/gluon/lipnet/utils/align.py | 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) | def sentence(self, padding=75):
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Get sentence
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train | Align.word | Get words | example/gluon/lipnet/utils/align.py | 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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train | Align.word_frame_pos | Get the position of words | example/gluon/lipnet/utils/align.py | 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) | 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))
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train | CustomModule.prepare_sparse_params | 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 | example/rnn/large_word_lm/custom_module.py | 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
'''
if ... | 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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train | CustomModule.save_params | Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile') | example/rnn/large_word_lm/custom_module.py | def save_params(self, fname):
"""Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
arg_... | def save_params(self, fname):
"""Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
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train | CustomModule.get_params_from_kv | Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArray... | example/rnn/large_word_lm/custom_module.py | def get_params_from_kv(self, arg_params, aux_params):
""" Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
... | def get_params_from_kv(self, arg_params, aux_params):
""" Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
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train | CustomModule.clip_by_global_norm_per_ctx | Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients... | example/rnn/large_word_lm/custom_module.py | def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the ... | def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
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train | CustomModule.rescale_grad | Rescale the gradient of provided parameters by a certain scale | example/rnn/large_word_lm/custom_module.py | 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)
grad_vals = self._exec_group.grad_arrays[param... | 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
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train | factorization_machine_model | 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) | example/sparse/factorization_machine/model.py | 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)
"""
x = mx.symbol.Variable("... | 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)
"""
x = mx.symbol.Variable("... | [
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train | batchify | Reshape data into (num_example, batch_size) | example/rnn/word_lm/data.py | 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
return data | 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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train | Corpus.tokenize | Tokenizes a text file. | example/rnn/word_lm/data.py | def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
... | def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
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train | _build_doc | Build docstring for symbolic functions. | python/mxnet/symbol_doc.py | 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... | def _build_doc(func_name,
desc,
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arg_desc,
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"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
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train | SymbolDoc.get_output_shape | Get user friendly information of the output shapes. | python/mxnet/symbol_doc.py | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | def get_output_shape(sym, **input_shapes):
"""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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"... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
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