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fixing cross-reference issues
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@Roshrini
Roshrini committed Nov 12, 2018
1 parent 4e74b3d commit dabb87b
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Showing 4 changed files with 83 additions and 72 deletions.
68 changes: 34 additions & 34 deletions python/mxnet/ndarray/ndarray.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -399,7 +399,7 @@ def __setitem__(self, key, value):
Parameters
----------
key : int, slice, list, np.ndarray, NDArray, or tuple of all previous types
key : int, mxnet.ndarray.slice, list, np.ndarray, NDArray, or tuple of all previous types
The indexing key.
value : scalar or array-like object that can be broadcast to the shape of self[key]
The value to set.
Expand Down Expand Up @@ -467,7 +467,7 @@ def __getitem__(self, key):
Parameters
----------
key : int, slice, list, np.ndarray, NDArray, or tuple of all previous types
key : int, mxnet.ndarray.slice, list, np.ndarray, NDArray, or tuple of all previous types
Indexing key.
Examples
Expand Down Expand Up @@ -2642,9 +2642,9 @@ def add(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -2704,9 +2704,9 @@ def subtract(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -2765,9 +2765,9 @@ def multiply(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -2826,9 +2826,9 @@ def divide(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -2883,9 +2883,9 @@ def modulo(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3002,9 +3002,9 @@ def maximum(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3059,9 +3059,9 @@ def minimum(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3120,9 +3120,9 @@ def equal(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3184,9 +3184,9 @@ def not_equal(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3251,9 +3251,9 @@ def greater(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3315,9 +3315,9 @@ def greater_equal(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3379,9 +3379,9 @@ def lesser(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3443,9 +3443,9 @@ def lesser_equal(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3506,9 +3506,9 @@ def logical_and(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3566,9 +3566,9 @@ def logical_or(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -3626,9 +3626,9 @@ def logical_xor(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down
16 changes: 8 additions & 8 deletions python/mxnet/ndarray/sparse.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1205,9 +1205,9 @@ def add(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.sparse.array
First array to be added.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.sparse.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -1277,9 +1277,9 @@ def subtract(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.sparse.array
First array to be subtracted.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.sparse.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.__spec__
Expand Down Expand Up @@ -1348,9 +1348,9 @@ def multiply(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.sparse.array
First array to be multiplied.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.sparse.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down Expand Up @@ -1432,9 +1432,9 @@ def divide(lhs, rhs):
Parameters
----------
lhs : scalar or array
lhs : scalar or mxnet.ndarray.sparse.array
First array in division.
rhs : scalar or array
rhs : scalar or mxnet.ndarray.sparse.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Expand Down
59 changes: 33 additions & 26 deletions python/mxnet/optimizer/optimizer.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -70,11 +70,12 @@ class Optimizer(object):
The initial number of updates.
multi_precision : bool, optional
Flag to control the internal precision of the optimizer.
``False`` results in using the same precision as the weights (default),
``True`` makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
Flag to control the internal precision of the optimizer.::
False: results in using the same precision as the weights (default),
True: makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
Properties
----------
Expand Down Expand Up @@ -481,16 +482,17 @@ class SGD(Optimizer):
Parameters
----------
momentum : float, optional
The momentum value.
The momentum value.
lazy_update : bool, optional
Default is True. If True, lazy updates are applied \
if the storage types of weight and grad are both ``row_sparse``.
Default is True. If True, lazy updates are applied \
if the storage types of weight and grad are both ``row_sparse``.
multi_precision: bool, optional
Flag to control the internal precision of the optimizer.
``False`` results in using the same precision as the weights (default),
``True`` makes internal 32-bit copy of the weights and applies gradients \
in 32-bit precision even if actual weights used in the model have lower precision.\
Turning this on can improve convergence and accuracy when training with float16.
Flag to control the internal precision of the optimizer.::
False: results in using the same precision as the weights (default),
True: makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
"""
def __init__(self, momentum=0.0, lazy_update=True, **kwargs):
super(SGD, self).__init__(**kwargs)
Expand Down Expand Up @@ -694,11 +696,13 @@ class LBSGD(Optimizer):
momentum : float, optional
The momentum value.
multi_precision: bool, optional
Flag to control the internal precision of the optimizer.
``False`` results in using the same precision as the weights (default),
``True`` makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
Flag to control the internal precision of the optimizer.::
False: results in using the same precision as the weights (default),
True: makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
warmup_strategy: string ('linear', 'power2', 'sqrt'. , 'lars' default : 'linear')
warmup_epochs: unsigned, default: 5
batch_scale: unsigned, default: 1 (same as batch size*numworkers)
Expand Down Expand Up @@ -933,11 +937,12 @@ class NAG(Optimizer):
momentum : float, optional
The momentum value.
multi_precision: bool, optional
Flag to control the internal precision of the optimizer.
``False`` results in using the same precision as the weights (default),
``True`` makes internal 32-bit copy of the weights and applies gradients \
in 32-bit precision even if actual weights used in the model have lower precision.\
Turning this on can improve convergence and accuracy when training with float16.
Flag to control the internal precision of the optimizer.::
False: results in using the same precision as the weights (default),
True: makes internal 32-bit copy of the weights and applies gradients
in 32-bit precision even if actual weights used in the model have lower precision.
Turning this on can improve convergence and accuracy when training with float16.
"""
def __init__(self, momentum=0.0, **kwargs):
super(NAG, self).__init__(**kwargs)
Expand Down Expand Up @@ -1175,9 +1180,11 @@ class RMSProp(Optimizer):
epsilon : float, optional
Small value to avoid division by 0.
centered : bool, optional
Flag to control which version of RMSProp to use.
``True`` will use Graves's version of `RMSProp`,
``False`` will use Tieleman & Hinton's version of `RMSProp`.
Flag to control which version of RMSProp to use.::
True: will use Graves's version of `RMSProp`,
False: will use Tieleman & Hinton's version of `RMSProp`.
clip_weights : float, optional
Clips weights into range ``[-clip_weights, clip_weights]``.
"""
Expand Down
12 changes: 8 additions & 4 deletions python/mxnet/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -261,10 +261,14 @@ def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=Non
Parameters
----------
shape: list or tuple
stype: str, valid values: "csr" or "row_sparse"
density, optional: float, should be between 0 and 1
distribution, optional: str, valid values: "uniform" or "powerlaw"
dtype, optional: numpy.dtype, default value is None
stype: str
valid values: "csr" or "row_sparse"
density: float, optional
should be between 0 and 1
distribution: str, optional
valid values: "uniform" or "powerlaw"
dtype: numpy.dtype, optional
default value is None
Returns
-------
Expand Down

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