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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/16 20:49:59 UTC
[GitHub] gigasquid closed pull request #13275: Sphinx errors in Gluon
gigasquid closed pull request #13275: Sphinx errors in Gluon
URL: https://github.com/apache/incubator-mxnet/pull/13275
This is a PR merged from a forked repository.
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diff --git a/python/mxnet/autograd.py b/python/mxnet/autograd.py
index b3acee27caa..afc67e2569b 100644
--- a/python/mxnet/autograd.py
+++ b/python/mxnet/autograd.py
@@ -273,8 +273,10 @@ def grad(heads, variables, head_grads=None, retain_graph=None, create_graph=Fals
returned as new NDArrays instead of stored into `variable.grad`.
Supports recording gradient graph for computing higher order gradients.
- .. Note: Currently only a very limited set of operators support higher order
- gradients.
+ .. note::
+
+ Currently only a very limited set of operators support higher order \
+ gradients.
Parameters
----------
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 6cb9fc690b5..7047364966a 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -326,8 +326,7 @@ def save_parameters(self, filename):
References
----------
- `Saving and Loading Gluon Models
-
+ `Saving and Loading Gluon Models \
<https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
"""
params = self._collect_params_with_prefix()
@@ -372,8 +371,7 @@ def load_parameters(self, filename, ctx=None, allow_missing=False,
References
----------
- `Saving and Loading Gluon Models
-
+ `Saving and Loading Gluon Models \
<https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>`_
"""
loaded = ndarray.load(filename)
diff --git a/python/mxnet/gluon/contrib/nn/basic_layers.py b/python/mxnet/gluon/contrib/nn/basic_layers.py
index c656cd2d4e1..28fea1592da 100644
--- a/python/mxnet/gluon/contrib/nn/basic_layers.py
+++ b/python/mxnet/gluon/contrib/nn/basic_layers.py
@@ -27,7 +27,7 @@
from ...nn import Sequential, HybridSequential, BatchNorm
class Concurrent(Sequential):
- """Lays `Block`s concurrently.
+ """Lays `Block` s concurrently.
This block feeds its input to all children blocks, and
produce the output by concatenating all the children blocks' outputs
@@ -60,7 +60,7 @@ def forward(self, x):
class HybridConcurrent(HybridSequential):
- """Lays `HybridBlock`s concurrently.
+ """Lays `HybridBlock` s concurrently.
This block feeds its input to all children blocks, and
produce the output by concatenating all the children blocks' outputs
diff --git a/python/mxnet/gluon/contrib/rnn/rnn_cell.py b/python/mxnet/gluon/contrib/rnn/rnn_cell.py
index 1b9afee14bf..0cbc9eaac37 100644
--- a/python/mxnet/gluon/contrib/rnn/rnn_cell.py
+++ b/python/mxnet/gluon/contrib/rnn/rnn_cell.py
@@ -26,7 +26,7 @@
class VariationalDropoutCell(ModifierCell):
"""
Applies Variational Dropout on base cell.
- (https://arxiv.org/pdf/1512.05287.pdf,
+ (https://arxiv.org/pdf/1512.05287.pdf, \
https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf).
Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN
@@ -197,24 +197,29 @@ def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=N
class LSTMPCell(HybridRecurrentCell):
r"""Long-Short Term Memory Projected (LSTMP) network cell.
(https://arxiv.org/abs/1402.1128)
+
Each call computes the following function:
+
.. math::
\begin{array}{ll}
i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{ri} r_{(t-1)} + b_{ri}) \\
f_t = sigmoid(W_{if} x_t + b_{if} + W_{rf} r_{(t-1)} + b_{rf}) \\
- g_t = \tanh(W_{ig} x_t + b_{ig} + W_{rc} r_{(t-1)} + b_{rg}}) \\
+ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{rc} r_{(t-1)} + b_{rg}) \\
o_t = sigmoid(W_{io} x_t + b_{io} + W_{ro} r_{(t-1)} + b_{ro}) \\
c_t = f_t * c_{(t-1)} + i_t * g_t \\
h_t = o_t * \tanh(c_t) \\
r_t = W_{hr} h_t
\end{array}
+
where :math:`r_t` is the projected recurrent activation at time `t`,
- math:`h_t` is the hidden state at time `t`, :math:`c_t` is the
+ :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the
cell state at time `t`, :math:`x_t` is the input at time `t`, and :math:`i_t`,
:math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and
out gates, respectively.
+
Parameters
----------
+
hidden_size : int
Number of units in cell state symbol.
projection_size : int
@@ -234,7 +239,7 @@ class LSTMPCell(HybridRecurrentCell):
to zero.
h2h_bias_initializer : str or Initializer
Initializer for the bias vector.
- prefix : str, default 'lstmp_'
+ prefix : str, default ``'lstmp_``'
Prefix for name of `Block`s
(and name of weight if params is `None`).
params : Parameter or None
diff --git a/python/mxnet/gluon/data/vision/datasets.py b/python/mxnet/gluon/data/vision/datasets.py
index 2c98000389a..0e380c06efa 100644
--- a/python/mxnet/gluon/data/vision/datasets.py
+++ b/python/mxnet/gluon/data/vision/datasets.py
@@ -45,8 +45,7 @@ class MNIST(dataset._DownloadedDataset):
train : bool, default True
Whether to load the training or testing set.
transform : function, default None
- A user defined callback that transforms each sample. For example:
- ::
+ A user defined callback that transforms each sample. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -106,8 +105,7 @@ class FashionMNIST(MNIST):
train : bool, default True
Whether to load the training or testing set.
transform : function, default None
- A user defined callback that transforms each sample. For example:
- ::
+ A user defined callback that transforms each sample. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -139,8 +137,7 @@ class CIFAR10(dataset._DownloadedDataset):
train : bool, default True
Whether to load the training or testing set.
transform : function, default None
- A user defined callback that transforms each sample. For example:
- ::
+ A user defined callback that transforms each sample. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -204,8 +201,7 @@ class CIFAR100(CIFAR10):
train : bool, default True
Whether to load the training or testing set.
transform : function, default None
- A user defined callback that transforms each sample. For example:
- ::
+ A user defined callback that transforms each sample. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -242,8 +238,7 @@ class ImageRecordDataset(dataset.RecordFileDataset):
If 1, always convert images to colored (RGB).
transform : function, default None
- A user defined callback that transforms each sample. For example:
- ::
+ A user defined callback that transforms each sample. For example::
transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -279,8 +274,7 @@ class ImageFolderDataset(dataset.Dataset):
If 0, always convert loaded images to greyscale (1 channel).
If 1, always convert loaded images to colored (3 channels).
transform : callable, default None
- A function that takes data and label and transforms them:
- ::
+ A function that takes data and label and transforms them::
transform = lambda data, label: (data.astype(np.float32)/255, label)
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index da43b62a1c3..7b5832e1ace 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -621,7 +621,7 @@ class LogisticLoss(Loss):
where `pred` is the classifier prediction and `label` is the target tensor
containing values -1 or 1 (0 or 1 if `label_format` is binary).
- `pred` and `label` can have arbitrary shape as long as they have the same number of elements.
+ `pred` and `label` can have arbitrary shape as long as they have the same number of elements.
Parameters
----------
diff --git a/python/mxnet/gluon/nn/basic_layers.py b/python/mxnet/gluon/nn/basic_layers.py
index c95601c6f79..0aa60263a9f 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -75,7 +75,7 @@ def __len__(self):
return len(self._children)
def hybridize(self, active=True, **kwargs):
- """Activates or deactivates `HybridBlock`s recursively. Has no effect on
+ """Activates or deactivates `HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
diff --git a/python/mxnet/gluon/parameter.py b/python/mxnet/gluon/parameter.py
index b57defa5498..b3d8f80318b 100644
--- a/python/mxnet/gluon/parameter.py
+++ b/python/mxnet/gluon/parameter.py
@@ -580,7 +580,7 @@ class Constant(Parameter):
will not change during training. But you can still update their values
manually with the `set_data` method.
- `Constant`s can be created with either::
+ `Constant` s can be created with either::
const = mx.gluon.Constant('const', [[1,2],[3,4]])
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index d23b563add9..bac7b2267db 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -805,10 +805,12 @@ def check_numeric_gradient(sym, location, aux_states=None, numeric_eps=1e-3, rto
location : list or tuple or dict
Argument values used as location to compute gradient
- - if type is list of numpy.ndarray
+ - if type is list of numpy.ndarray, \
inner elements should have the same order as mxnet.sym.list_arguments().
- - if type is dict of str -> numpy.ndarray
+
+ - if type is dict of str -> numpy.ndarray, \
maps the name of arguments to the corresponding numpy.ndarray.
+
*In either case, value of all the arguments must be provided.*
aux_states : list or tuple or dict, optional
The auxiliary states required when generating the executor for the symbol.
@@ -829,7 +831,7 @@ def check_numeric_gradient(sym, location, aux_states=None, numeric_eps=1e-3, rto
References
---------
- ..[1] https://github.com/Theano/Theano/blob/master/theano/gradient.py
+ [1] https://github.com/Theano/Theano/blob/master/theano/gradient.py
"""
assert dtype in (np.float16, np.float32, np.float64)
# cannot use finite differences with small eps without high precision
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