Align the tensor abstraction

Summary:
This commit aligns the properties and methods of tensor class.

caffe/layers/common.py

@@ -368,7 +368,7 @@ class Input(Layer):
                 namespace='Tensor'),
                 namespace='Tensor'),
             shape=self.blob_shapes[i],
             shape=self.blob_shapes[i],
             dtype='float32',
             dtype='float32',
-        ).placeholder() for i in range(len(self.blob_shapes))]
+        ).constant() for i in range(len(self.blob_shapes))]
 
 
 
 
 class Normalize(Layer):
 class Normalize(Layer):

dali/core/ops/array.py

@@ -110,7 +110,7 @@ class Reshape(object):
     reshape1 = dali.ops.Reshape(shape=(2, 3))
     reshape1 = dali.ops.Reshape(shape=(2, 3))
     y = reshape1(inputs['x'])
     y = reshape1(inputs['x'])
 
 
-    # Reshape to the shape given by another tensor
+    # Reshape to the shape of other tensor
     reshape2 = dali.ops.Reshape()
     reshape2 = dali.ops.Reshape()
     z = reshape2(inputs['x'], inputs['x_shape'])
     z = reshape2(inputs['x'], inputs['x_shape'])
     ```
     ```

docs/api/python/dragon.rst

@@ -7,13 +7,13 @@ dragon
   -------
   -------
 
 
   `class EagerTensor <dragon/EagerTensor.html>`_
   `class EagerTensor <dragon/EagerTensor.html>`_
-  : Tensor abstraction under the eager execution.
+  : Tensor abstraction for eager executing.
 
 
   `class GradientTape <dragon/GradientTape.html>`_
   `class GradientTape <dragon/GradientTape.html>`_
   : Record the operations for auto differentiation.
   : Record the operations for auto differentiation.
 
 
   `class Tensor <dragon/Tensor.html>`_
   `class Tensor <dragon/Tensor.html>`_
-  : Tensor abstraction under the graph execution.
+  : Tensor abstraction for graph executing.
 
 
   `class TensorSpec <dragon/TensorSpec.html>`_
   `class TensorSpec <dragon/TensorSpec.html>`_
   : Spec to describe properties of a tensor.
   : Spec to describe properties of a tensor.
@@ -25,10 +25,10 @@ dragon
   ---------
   ---------
 
 
   `arange(...) <dragon/arange.html>`_
   `arange(...) <dragon/arange.html>`_
-  : Return a tensor with evenly spaced values within a interval.
+  : Return a tensor of evenly spaced values within a interval.
 
 
   `assign(...) <dragon/assign.html>`_
   `assign(...) <dragon/assign.html>`_
-  : Assign the value to ref.
+  : Assign the value to input.
 
 
   `broadcast_to(...) <dragon/broadcast_to.html>`_
   `broadcast_to(...) <dragon/broadcast_to.html>`_
   : Broadcast input according to a given shape.
   : Broadcast input according to a given shape.
@@ -46,10 +46,10 @@ dragon
   : Concatenate the inputs along the given axis.
   : Concatenate the inputs along the given axis.
 
 
   `constant(...) <dragon/constant.html>`_
   `constant(...) <dragon/constant.html>`_
-  : Return a tensor taking the value content.
+  : Return a tensor initialized from the value.
 
 
   `copy(...) <dragon/copy.html>`_
   `copy(...) <dragon/copy.html>`_
-  : Copy the value to ref.
+  : Copy the input.
 
 
   `create_function(...) <dragon/create_function.html>`_
   `create_function(...) <dragon/create_function.html>`_
   : Create a callable graph from the specified outputs.
   : Create a callable graph from the specified outputs.
@@ -70,10 +70,10 @@ dragon
   : Return a tensor constructed as the identity matrix.
   : Return a tensor constructed as the identity matrix.
 
 
   `eye_like(...) <dragon/eye_like.html>`_
   `eye_like(...) <dragon/eye_like.html>`_
-  :Return a tensor shaping like another constructed as the identity matrix.
+  :Return a tensor of identity matrix with shape as the other.
 
 
   `fill(...) <dragon/fill.html>`_
   `fill(...) <dragon/fill.html>`_
-  : Return a tensor filled with the specific value.
+  : Return a tensor filled with the scalar value.
 
 
   `flatten(...) <dragon/flatten.html>`_
   `flatten(...) <dragon/flatten.html>`_
   : Flatten the input along the given axes.
   : Flatten the input along the given axes.
@@ -97,22 +97,22 @@ dragon
   : Load a shared library.
   : Load a shared library.
 
 
   `masked_assign(...) <dragon/masked_assign.html>`_
   `masked_assign(...) <dragon/masked_assign.html>`_
-  : Assign the value to ref where mask is **1**.
+  : Assign the value to input where mask is 1.
 
 
   `masked_select(...) <dragon/masked_select.html>`_
   `masked_select(...) <dragon/masked_select.html>`_
-  : Select the elements where the given mask is **1**.
+  : Select the elements of input where mask is 1.
 
 
   `name_scope(...) <dragon/name_scope.html>`_
   `name_scope(...) <dragon/name_scope.html>`_
   : Context-manager to nest the name as prefix for operations.
   : Context-manager to nest the name as prefix for operations.
 
 
   `nonzero(...) <dragon/nonzero.html>`_
   `nonzero(...) <dragon/nonzero.html>`_
-  : Return the indices of non-zero elements.
+  : Return the index of non-zero elements.
 
 
   `ones(...) <dragon/ones.html>`_
   `ones(...) <dragon/ones.html>`_
   : Return a tensor filled with ones.
   : Return a tensor filled with ones.
 
 
   `ones_like(...) <dragon/ones_like.html>`_
   `ones_like(...) <dragon/ones_like.html>`_
-  : Return a tensor shaping like another filled with ones.
+  : Return a tensor of ones with shape as the other.
 
 
   `one_hot(...) <dragon/one_hot.html>`_
   `one_hot(...) <dragon/one_hot.html>`_
   : Return the one-hot representation for input.
   : Return the one-hot representation for input.
@@ -163,7 +163,7 @@ dragon
   : Return a tensor filled with zeros.
   : Return a tensor filled with zeros.
 
 
   `zeros_like(...) <dragon/zeros_like.html>`_
   `zeros_like(...) <dragon/zeros_like.html>`_
-  : Return a tensor shaping like another filled with zeros.
+  : Return a tensor of zeros with shape as the other.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/dragon/EagerTensor.rst

@@ -92,10 +92,6 @@ __add__
 #######
 #######
 .. automethod:: dragon.EagerTensor.__add__
 .. automethod:: dragon.EagerTensor.__add__
 
 
-__div__
-#######
-.. automethod:: dragon.EagerTensor.__div__
-
 __float__
 __float__
 #########
 #########
 .. automethod:: dragon.EagerTensor.__float__
 .. automethod:: dragon.EagerTensor.__float__
@@ -116,10 +112,6 @@ __iadd__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__iadd__
 .. automethod:: dragon.EagerTensor.__iadd__
 
 
-__idiv__
-########
-.. automethod:: dragon.EagerTensor.__idiv__
-
 __imul__
 __imul__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__imul__
 .. automethod:: dragon.EagerTensor.__imul__
@@ -132,6 +124,10 @@ __isub__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__isub__
 .. automethod:: dragon.EagerTensor.__isub__
 
 
+__itruediv__
+############
+.. automethod:: dragon.EagerTensor.__itruediv__
+
 __le__
 __le__
 ######
 ######
 .. automethod:: dragon.EagerTensor.__le__
 .. automethod:: dragon.EagerTensor.__le__
@@ -152,10 +148,6 @@ __radd__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__radd__
 .. automethod:: dragon.EagerTensor.__radd__
 
 
-__rdiv__
-########
-.. automethod:: dragon.EagerTensor.__rdiv__
-
 __rmul__
 __rmul__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__rmul__
 .. automethod:: dragon.EagerTensor.__rmul__
@@ -164,6 +156,10 @@ __rsub__
 ########
 ########
 .. automethod:: dragon.EagerTensor.__rsub__
 .. automethod:: dragon.EagerTensor.__rsub__
 
 
+__rtruediv__
+############
+.. automethod:: dragon.EagerTensor.__rtruediv__
+
 __setitem__
 __setitem__
 ###########
 ###########
 .. automethod:: dragon.EagerTensor.__setitem__
 .. automethod:: dragon.EagerTensor.__setitem__
@@ -172,18 +168,13 @@ __sub__
 #######
 #######
 .. automethod:: dragon.EagerTensor.__sub__
 .. automethod:: dragon.EagerTensor.__sub__
 
 
+__truediv__
+###########
+.. automethod:: dragon.EagerTensor.__truediv__
+
 .. _dragon.assign(...): assign.html
 .. _dragon.assign(...): assign.html
 .. _dragon.cast(...): cast.html
 .. _dragon.cast(...): cast.html
 .. _dragon.copy(...): copy.html
 .. _dragon.copy(...): copy.html
-.. _dragon.math.add(...): math/add.html
-.. _dragon.math.div(...): math/div.html
-.. _dragon.math.greater(...): math/greater.html
-.. _dragon.math.greater_equal(...): math/greater_equal.html
-.. _dragon.math.less(...): math/less.html
-.. _dragon.math.less_equal(...): math/less_equal.html
-.. _dragon.math.mul(...): math/mul.html
-.. _dragon.math.negative(...): math/negative.html
-.. _dragon.math.sub(...): math/sub.html
 .. _dragon.masked_assign(...): masked_assign.html
 .. _dragon.masked_assign(...): masked_assign.html
 .. _dragon.masked_select(...): masked_select.html
 .. _dragon.masked_select(...): masked_select.html
 .. _dragon.reshape(...): reshape.html
 .. _dragon.reshape(...): reshape.html

docs/api/python/dragon/Tensor.rst

@@ -65,10 +65,6 @@ normal
 ######
 ######
 .. automethod:: dragon.Tensor.normal
 .. automethod:: dragon.Tensor.normal
 
 
-placeholder
-###########
-.. automethod:: dragon.Tensor.placeholder
-
 reshape
 reshape
 #######
 #######
 .. automethod:: dragon.Tensor.reshape
 .. automethod:: dragon.Tensor.reshape
@@ -85,10 +81,6 @@ uniform
 #######
 #######
 .. automethod:: dragon.Tensor.uniform
 .. automethod:: dragon.Tensor.uniform
 
 
-variable
-########
-.. automethod:: dragon.Tensor.variable
-
 Overrides
 Overrides
 ---------
 ---------
 
 
@@ -96,10 +88,6 @@ __add__
 #######
 #######
 .. automethod:: dragon.Tensor.__add__
 .. automethod:: dragon.Tensor.__add__
 
 
-__div__
-#######
-.. automethod:: dragon.Tensor.__div__
-
 __float__
 __float__
 #########
 #########
 .. automethod:: dragon.Tensor.__float__
 .. automethod:: dragon.Tensor.__float__
@@ -140,10 +128,6 @@ __radd__
 ########
 ########
 .. automethod:: dragon.Tensor.__radd__
 .. automethod:: dragon.Tensor.__radd__
 
 
-__rdiv__
-########
-.. automethod:: dragon.Tensor.__rdiv__
-
 __rmul__
 __rmul__
 ########
 ########
 .. automethod:: dragon.Tensor.__rmul__
 .. automethod:: dragon.Tensor.__rmul__
@@ -160,24 +144,21 @@ __sub__
 #######
 #######
 .. automethod:: dragon.Tensor.__sub__
 .. automethod:: dragon.Tensor.__sub__
 
 
+__rtruediv__
+############
+.. automethod:: dragon.Tensor.__rtruediv__
+
+__truediv__
+############
+.. automethod:: dragon.Tensor.__truediv__
+
 .. _dragon.assign(...): assign.html
 .. _dragon.assign(...): assign.html
 .. _dragon.cast(...): cast.html
 .. _dragon.cast(...): cast.html
 .. _dragon.copy(...): copy.html
 .. _dragon.copy(...): copy.html
-.. _dragon.math.add(...): math/add.html
-.. _dragon.math.div(...): math/div.html
-.. _dragon.math.greater(...): math/greater.html
-.. _dragon.math.greater_equal(...): math/greater_equal.html
-.. _dragon.math.less(...): math/less.html
-.. _dragon.math.less_equal(...): math/less_equal.html
-.. _dragon.math.mul(...): math/mul.html
-.. _dragon.math.negative(...): math/negative.html
-.. _dragon.math.sub(...): math/sub.html
 .. _dragon.masked_assign(...): masked_assign.html
 .. _dragon.masked_assign(...): masked_assign.html
 .. _dragon.masked_select(...): masked_select.html
 .. _dragon.masked_select(...): masked_select.html
 .. _dragon.reshape(...): reshape.html
 .. _dragon.reshape(...): reshape.html
 .. _dragon.slice(...): slice.html
 .. _dragon.slice(...): slice.html
-.. _dragon.workspace.feed_tensor(...): workspace/feed_tensor.html
-.. _dragon.workspace.fetch_tensor(...): workspace/fetch_tensor.html
 
 
 .. raw:: html
 .. raw:: html
 
 

docs/api/python/dragon/random.rst

@@ -13,13 +13,13 @@ dragon.random
   : Return a tensor initialized from the glorot uniform distribution.
   : Return a tensor initialized from the glorot uniform distribution.
 
 
   `multinomial(...) <random/multinomial.html>`_
   `multinomial(...) <random/multinomial.html>`_
-  : Return a tensor with indices sampled from multinomial distribution.
+  : Return a tensor with indices sampled from the multinomial distribution.
 
 
   `normal(...) <random/normal.html>`_
   `normal(...) <random/normal.html>`_
   : Return a tensor initialized from the normal distribution.
   : Return a tensor initialized from the normal distribution.
 
 
   `normal_like(...) <random/normal_like.html>`_
   `normal_like(...) <random/normal_like.html>`_
-  : Return a tensor shaping like another initialized from the normal distribution.
+  : Return a tensor initialized from the normal distribution with shape as the other.
 
 
   `set_seed(...) <random/set_seed.html>`_
   `set_seed(...) <random/set_seed.html>`_
   : Set the global random seed.
   : Set the global random seed.
@@ -31,7 +31,7 @@ dragon.random
   : Return a tensor initialized from the uniform distribution.
   : Return a tensor initialized from the uniform distribution.
 
 
   `uniform_like(...) <random/uniform_like.html>`_
   `uniform_like(...) <random/uniform_like.html>`_
-  : Return a tensor shaping like another initialized from the uniform distribution.
+  : Return a tensor initialized from the uniform distribution with shape as the other.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/index.rst

@@ -71,13 +71,10 @@ TensorFlow
   * `tensorflow <tensorflow.html>`_
   * `tensorflow <tensorflow.html>`_
   * `tensorflow.bitwise <tensorflow/bitwise.html>`_
   * `tensorflow.bitwise <tensorflow/bitwise.html>`_
   * `tensorflow.dtypes <tensorflow/dtypes.html>`_
   * `tensorflow.dtypes <tensorflow/dtypes.html>`_
-  * `tensorflow.initializers <tensorflow/initializers.html>`_
   * `tensorflow.keras <tensorflow/keras.html>`_
   * `tensorflow.keras <tensorflow/keras.html>`_
   * `tensorflow.linalg <tensorflow/linalg.html>`_
   * `tensorflow.linalg <tensorflow/linalg.html>`_
-  * `tensorflow.losses <tensorflow/losses.html>`_
   * `tensorflow.math <tensorflow/math.html>`_
   * `tensorflow.math <tensorflow/math.html>`_
   * `tensorflow.nn <tensorflow/nn.html>`_
   * `tensorflow.nn <tensorflow/nn.html>`_
-  * `tensorflow.optimizers <tensorflow/optimizers.html>`_
   * `tensorflow.random <tensorflow/random.html>`_
   * `tensorflow.random <tensorflow/random.html>`_
 
 
 TensorLayer
 TensorLayer
@@ -233,27 +230,18 @@ Modules
   `Module vm.tensorflow.dtypes <tensorflow/dtypes.html>`_
   `Module vm.tensorflow.dtypes <tensorflow/dtypes.html>`_
   : Virtual API for ``tensorflow.dtypes`` namespace.
   : Virtual API for ``tensorflow.dtypes`` namespace.
 
 
-  `Module vm.tensorflow.initializers <tensorflow/initializers.html>`_
-  : Virtual API for ``tensorflow.initializers`` namespace.
-
   `Module vm.tensorflow.keras <tensorflow/keras.html>`_
   `Module vm.tensorflow.keras <tensorflow/keras.html>`_
   : Virtual API for ``tensorflow.keras`` namespace.
   : Virtual API for ``tensorflow.keras`` namespace.
 
 
   `Module vm.tensorflow.linalg <tensorflow/linalg.html>`_
   `Module vm.tensorflow.linalg <tensorflow/linalg.html>`_
   : Virtual API for ``tensorflow.linalg`` namespace.
   : Virtual API for ``tensorflow.linalg`` namespace.
 
 
-  `Module vm.tensorflow.losses <tensorflow/losses.html>`_
-  : Virtual API for ``tensorflow.losses`` namespace.
-
   `Module vm.tensorflow.math <tensorflow/math.html>`_
   `Module vm.tensorflow.math <tensorflow/math.html>`_
   : Virtual API for ``tensorflow.math`` namespace.
   : Virtual API for ``tensorflow.math`` namespace.
 
 
   `Module vm.tensorflow.nn <tensorflow/nn.html>`_
   `Module vm.tensorflow.nn <tensorflow/nn.html>`_
   : Virtual API for ``tensorflow.nn`` namespace.
   : Virtual API for ``tensorflow.nn`` namespace.
 
 
-  `Module vm.tensorflow.optimizers <tensorflow/optimizers.html>`_
-  : Virtual API for ``tensorflow.optimizers`` namespace.
-
   `Module vm.tensorflow.random <tensorflow/random.html>`_
   `Module vm.tensorflow.random <tensorflow/random.html>`_
   : Virtual API for ``tensorflow.random`` namespace.
   : Virtual API for ``tensorflow.random`` namespace.
 
 
@@ -325,13 +313,10 @@ Modules
   tensorflow
   tensorflow
   tensorflow/bitwise
   tensorflow/bitwise
   tensorflow/dtypes
   tensorflow/dtypes
-  tensorflow/initializers
   tensorflow/keras
   tensorflow/keras
   tensorflow/linalg
   tensorflow/linalg
-  tensorflow/losses
   tensorflow/math
   tensorflow/math
   tensorflow/nn
   tensorflow/nn
-  tensorflow/optimizers
   tensorflow/random
   tensorflow/random
   tensorlayer/initializers
   tensorlayer/initializers
   tensorlayer/layers
   tensorlayer/layers

docs/api/python/tensorflow.rst

@@ -28,7 +28,7 @@ vm.tensorflow
   : Concatenate the values along the given axis.
   : Concatenate the values along the given axis.
 
 
   `constant(...) <tensorflow/constant.html>`_
   `constant(...) <tensorflow/constant.html>`_
-  : Return a tensor taking the value content.
+  : Return a tensor initialized from the value.
 
 
   `device(...) <tensorflow/device.html>`_
   `device(...) <tensorflow/device.html>`_
   : Context-manager to nest the the device spec.
   : Context-manager to nest the the device spec.
@@ -40,7 +40,7 @@ vm.tensorflow
   : Return a tensor constructed as the identity matrix.
   : Return a tensor constructed as the identity matrix.
 
 
   `fill(...) <tensorflow/fill.html>`_
   `fill(...) <tensorflow/fill.html>`_
-  : Return a tensor filled with the specific value.
+  : Return a tensor filled with the scalar value.
 
 
   `gather(...) <tensorflow/gather.html>`_
   `gather(...) <tensorflow/gather.html>`_
   : Select the elements according to the indices along the given axis.
   : Select the elements according to the indices along the given axis.
@@ -61,16 +61,16 @@ vm.tensorflow
   : Return a tensor filled with ones.
   : Return a tensor filled with ones.
 
 
   `ones_like(...) <tensorflow/ones_like.html>`_
   `ones_like(...) <tensorflow/ones_like.html>`_
-  : Return a tensor shaping like another filled with ones.
+  : Return a tensor of ones with shape as the other.
 
 
   `one_hot(...) <tensorflow/one_hot.html>`_
   `one_hot(...) <tensorflow/one_hot.html>`_
-  : Return the one-hot representation from indices.
+  : Return the one-hot representation for input.
 
 
   `pad(...) <tensorflow/pad.html>`_
   `pad(...) <tensorflow/pad.html>`_
   : Pad the input according to the given sizes.
   : Pad the input according to the given sizes.
 
 
   `range(...) <tensorflow/range.html>`_
   `range(...) <tensorflow/range.html>`_
-  : Return a tensor with evenly spaced values within a interval.
+  : Return a tensor of evenly spaced values within a interval.
 
 
   `reshape(...) <tensorflow/reshape.html>`_
   `reshape(...) <tensorflow/reshape.html>`_
   : Change the dimensions of input.
   : Change the dimensions of input.
@@ -94,7 +94,7 @@ vm.tensorflow
   : Return a tensor filled with zeros.
   : Return a tensor filled with zeros.
 
 
   `zeros_like(...) <tensorflow/zeros_like.html>`_
   `zeros_like(...) <tensorflow/zeros_like.html>`_
-  : Return a tensor shaping like another filled with zeros.
+  : Return a tensor of zeros with shape as the other.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/tensorflow/initializers.rst

-vm.tensorflow.initializers
-==========================
-
-.. only:: html
-
-  Classes
-  -------
-
-  `class Constant <keras/initializers/Constant.html>`_
-  : Fill tensors with a scalar.
-
-  `class GlorotNormal <keras/initializers/GlorotNormal.html>`_
-  : Fill tensors according to a glorot normal distribution.
-
-  `class GlorotUniform <keras/initializers/GlorotUniform.html>`_
-  : Fill tensors according to a glorot uniform distribution.
-
-  `class Initializer <keras/initializers/GlorotUniform.html>`_
-  : The basic Initializer.
-
-  `class Ones <keras/initializers/Ones.html>`_
-  : Fill tensors with ones.
-
-  `class RandomNormal <keras/initializers/RandomNormal.html>`_
-  : Fill tensors according to a random normal distribution.
-
-  `class RandomUniform <keras/initializers/RandomUniform.html>`_
-  : Fill tensors according to a random uniform distribution.
-
-  `class TruncatedNormal <keras/initializers/TruncatedNormal.html>`_
-  : Fill tensors according to a truncated normal distribution.
-
-  `class VarianceScaling <keras/initializers/VarianceScaling.html>`_
-  : Fill tensors with the random values adapting to shape.
-
-  `class Zeros <keras/initializers/Zeros.html>`_
-  : Fill tensors with zeros.
-
-.. raw:: html
-
-  <style>
-  h1:before {
-    content: "Module: dragon.";
-    color: #103d3e;
-  }
-  </style>

docs/api/python/tensorflow/keras/initializers.rst

@@ -7,31 +7,31 @@ initializers
   -------
   -------
 
 
   `class Constant <initializers/Constant.html>`_
   `class Constant <initializers/Constant.html>`_
-  : Fill tensors with a scalar.
+  : Fill tensor with a scalar value.
 
 
   `class GlorotNormal <initializers/GlorotNormal.html>`_
   `class GlorotNormal <initializers/GlorotNormal.html>`_
-  : Fill tensors according to a glorot normal distribution.
+  : Fill tensor from a glorot normal distribution.
 
 
   `class GlorotUniform <initializers/GlorotUniform.html>`_
   `class GlorotUniform <initializers/GlorotUniform.html>`_
-  : Fill tensors according to a glorot uniform distribution.
+  : Fill tensor from a glorot uniform distribution.
 
 
   `class Initializer <initializers/GlorotUniform.html>`_
   `class Initializer <initializers/GlorotUniform.html>`_
   : The basic Initializer.
   : The basic Initializer.
 
 
   `class Ones <initializers/Ones.html>`_
   `class Ones <initializers/Ones.html>`_
-  : Fill tensors with ones.
+  : Fill tensor with ones.
 
 
   `class RandomNormal <initializers/RandomNormal.html>`_
   `class RandomNormal <initializers/RandomNormal.html>`_
-  : Fill tensors according to a random normal distribution.
+  : Fill tensor from a normal distribution.
 
 
   `class RandomUniform <initializers/RandomUniform.html>`_
   `class RandomUniform <initializers/RandomUniform.html>`_
-  : Fill tensors according to a random uniform distribution.
+  : Fill tensor from an uniform distribution.
 
 
   `class TruncatedNormal <initializers/TruncatedNormal.html>`_
   `class TruncatedNormal <initializers/TruncatedNormal.html>`_
-  : Fill tensors according to a truncated normal distribution.
+  : Fill tensor from a truncated normal distribution.
 
 
   `class VarianceScaling <initializers/VarianceScaling.html>`_
   `class VarianceScaling <initializers/VarianceScaling.html>`_
-  : Fill tensors with the random values adapting to shape.
+  : Fill tensor from a scaled random distribution.
 
 
   `class Zeros <initializers/Zeros.html>`_
   `class Zeros <initializers/Zeros.html>`_
   : Fill tensors with zeros.
   : Fill tensors with zeros.

docs/api/python/tensorflow/keras/optimizers.rst

@@ -7,18 +7,18 @@ optimizers
   -------
   -------
 
 
   `class Adam <optimizers/Adam.html>`_
   `class Adam <optimizers/Adam.html>`_
-  : The optimizer which implements Adam algorithm.
+  : The optimizer to apply Adam algorithm.
   `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
   `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
 
 
   `class Optimizer <optimizers/Optimizer.html>`_
   `class Optimizer <optimizers/Optimizer.html>`_
   : The base class for optimizers.
   : The base class for optimizers.
 
 
   `class RMSprop <optimizers/RMSprop.html>`_
   `class RMSprop <optimizers/RMSprop.html>`_
-  : The optimizer which implements RMSprop algorithm.
+  : The optimizer to apply RMSprop algorithm.
   `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
   `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
 
 
   `class SGD <optimizers/SGD.html>`_
   `class SGD <optimizers/SGD.html>`_
-  : The optimizer which implements SGD algorithm.
+  : The optimizer to apply SGD algorithm.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/tensorflow/losses.rst

-vm.tensorflow.losses
-====================
-
-.. only:: html
-
-  Classes
-  -------
-
-  `class BinaryCrossentropy <keras/losses/BinaryCrossentropy.html>`_
-  : A criterion to compute the binary cross entropy with contiguous targets.
-
-  `class CategoricalCrossentropy <keras/losses/CategoricalCrossentropy.html>`_
-  : A criterion to compute the categorical cross entropy with contiguous targets.
-
-  `class Loss <keras/losses/Loss.html>`_
-  : The base class for loss criterion.
-
-  `class MeanAbsoluteError <keras/losses/MeanAbsoluteError.html>`_
-  : A criterion to compute the reduced element-wise absolute value difference.
-
-  `class MeanSquaredError <keras/losses/MeanSquaredError.html>`_
-  : A criterion to compute the reduced element-wise squared error.
-
-  `class SparseCategoricalCrossentropy <keras/losses/SparseCategoricalCrossentropy.html>`_
-  : A criterion to compute the categorical cross entropy with sparse labels.
-
-  Functions
-  ---------
-
-  `binary_crossentropy(...) <keras/losses/binary_crossentropy.html>`_
-  : Compute the binary cross entropy with contiguous targets.
-
-  `categorical_crossentropy(...) <keras/losses/categorical_crossentropy.html>`_
-  : Compute the categorical cross entropy with contiguous targets.
-
-  `mean_absolute_error(...) <keras/losses/mean_absolute_error.html>`_
-  : Compute the reduced element-wise absolute value difference.
-
-  `mean_squared_error(...) <keras/losses/mean_squared_error.html>`_
-  : Compute the reduced element-wise squared error.
-
-  `sparse_categorical_crossentropy(...) <keras/losses/sparse_categorical_crossentropy.html>`_
-  : Compute the categorical cross entropy with sparse labels.
-
-.. raw:: html
-
-  <style>
-  h1:before {
-    content: "Module: dragon.";
-    color: #103d3e;
-  }
-  </style>

docs/api/python/tensorflow/optimizers.rst

-vm.tensorflow.optimizers
-========================
-
-.. only:: html
-
-  Classes
-  -------
-
-  `class Adam <keras/optimizers/Adam.html>`_
-  : The optimizer which implements Adam algorithm.
-  `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
-
-  `class Optimizer <keras/optimizers/Optimizer.html>`_
-  : The base class for optimizers.
-
-  `class RMSprop <keras/optimizers/RMSprop.html>`_
-  : The optimizer which implements RMSprop algorithm.
-  `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
-
-  `class SGD <keras/optimizers/SGD.html>`_
-  : The optimizer which implements SGD algorithm.
-
-.. raw:: html
-
-  <style>
-  h1:before {
-    content: "Module: dragon.";
-    color: #103d3e;
-  }
-  </style>

docs/api/python/tensorlayer/initializers.rst

@@ -7,31 +7,31 @@ vm.tensorlayer.initializers
   -------
   -------
 
 
   `class Constant <initializers/Constant.html>`_
   `class Constant <initializers/Constant.html>`_
-  : Fill tensors with a scalar.
+  : Fill tensor with a scalar value.
 
 
   `class GlorotNormal <initializers/GlorotNormal.html>`_
   `class GlorotNormal <initializers/GlorotNormal.html>`_
-  : Fill tensors according to a glorot normal distribution.
+  : Fill tensor from a glorot normal distribution.
 
 
   `class GlorotUniform <initializers/GlorotUniform.html>`_
   `class GlorotUniform <initializers/GlorotUniform.html>`_
-  : Fill tensors according to a glorot uniform distribution.
+  : Fill tensor from a glorot uniform distribution.
 
 
   `class Initializer <initializers/GlorotUniform.html>`_
   `class Initializer <initializers/GlorotUniform.html>`_
   : The basic Initializer.
   : The basic Initializer.
 
 
   `class Ones <initializers/Ones.html>`_
   `class Ones <initializers/Ones.html>`_
-  : Fill tensors with ones.
+  : Fill tensor with ones.
 
 
   `class RandomNormal <initializers/RandomNormal.html>`_
   `class RandomNormal <initializers/RandomNormal.html>`_
-  : Fill tensors according to a random normal distribution.
+  : Fill tensor from a normal distribution.
 
 
   `class RandomUniform <initializers/RandomUniform.html>`_
   `class RandomUniform <initializers/RandomUniform.html>`_
-  : Fill tensors according to a random uniform distribution.
+  : Fill tensor from an uniform distribution.
 
 
   `class TruncatedNormal <initializers/TruncatedNormal.html>`_
   `class TruncatedNormal <initializers/TruncatedNormal.html>`_
-  : Fill tensors according to a truncated normal distribution.
+  : Fill tensor from a truncated normal distribution.
 
 
   `class Zeros <initializers/Zeros.html>`_
   `class Zeros <initializers/Zeros.html>`_
-  : Fill tensors with zeros.
+  : Fill tensor with zeros.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/torch.rst

@@ -34,7 +34,7 @@ vm.torch
   : Compute the element-wise addition.
   : Compute the element-wise addition.
 
 
   `arange(...) <torch/arange.html>`_
   `arange(...) <torch/arange.html>`_
-  : Return a tensor with evenly spaced values within a interval.
+  : Return a tensor of evenly spaced values within a interval.
 
 
   `argmax(...) <torch/argmax.html>`_
   `argmax(...) <torch/argmax.html>`_
   : Return the indices of maximum elements along the given axis.
   : Return the indices of maximum elements along the given axis.
@@ -163,14 +163,11 @@ vm.torch
   `nonzero(...) <torch/nonzero.html>`_
   `nonzero(...) <torch/nonzero.html>`_
   : Return the indices of non-zero elements.
   : Return the indices of non-zero elements.
 
 
-  `normal(...) <torch/normal.html>`_
-  : Return a tensor with a normal distribution.
-
   `ones(...) <torch/ones.html>`_
   `ones(...) <torch/ones.html>`_
-  : Return a tensor with value 1 filled.
+  : Return a tensor filled with ones.
 
 
   `ones_like(...) <torch/ones_like.html>`_
   `ones_like(...) <torch/ones_like.html>`_
-  : Return a tensor with value 1 filled, shape as input.
+  : Return a tensor of ones with shape as the other.
 
 
   `one_hot(...) <torch/one_hot.html>`_
   `one_hot(...) <torch/one_hot.html>`_
   : Return the one-hot representation for input.
   : Return the one-hot representation for input.
@@ -182,10 +179,10 @@ vm.torch
   : Compute the power of input.
   : Compute the power of input.
 
 
   `rand(...) <torch/rand.html>`_
   `rand(...) <torch/rand.html>`_
-  : Return a float tensor with a uniform distribution of U(0, 1).
+  : Return a tensor from the uniform distribution of U(0, 1).
 
 
   `randn(...) <torch/randn.html>`_
   `randn(...) <torch/randn.html>`_
-  : Return a float tensor with a normal distribution of N(0, 1).
+  : Return a tensor from the normal distribution of N(0, 1).
 
 
   `reciprocal(...) <torch/reciprocal.html>`_
   `reciprocal(...) <torch/reciprocal.html>`_
   : Compute the reciprocal of input.
   : Compute the reciprocal of input.
@@ -235,9 +232,6 @@ vm.torch
   `topk_acc(...) <torch/topk_acc.html>`_
   `topk_acc(...) <torch/topk_acc.html>`_
   : Compute the top-k accuracy according to the label.
   : Compute the top-k accuracy according to the label.
 
 
-  `uniform(...) <torch/uniform.html>`_
-  : Return a tensor with a normal distribution.
-
   `unsqueeze(...) <torch/unsqueeze.html>`_
   `unsqueeze(...) <torch/unsqueeze.html>`_
   : Expand the dimensions of input with size 1.
   : Expand the dimensions of input with size 1.
 
 
@@ -245,10 +239,10 @@ vm.torch
   : Select the elements from two branches under the condition.
   : Select the elements from two branches under the condition.
 
 
   `zeros(...) <torch/zeros.html>`_
   `zeros(...) <torch/zeros.html>`_
-  : Return a tensor with value 0 filled.
+  : Return a tensor filled with zeros.
 
 
   `zeros_like(...) <torch/zeros_like.html>`_
   `zeros_like(...) <torch/zeros_like.html>`_
-  : Return a tensor with value 0s filled, shape as input.
+  : Return a tensor of zeros with shape as the other.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:
@@ -301,7 +295,6 @@ vm.torch
   torch/ne
   torch/ne
   torch/no_grad
   torch/no_grad
   torch/nonzero
   torch/nonzero
-  torch/normal
   torch/ones
   torch/ones
   torch/ones_like
   torch/ones_like
   torch/one_hot
   torch/one_hot
@@ -328,7 +321,6 @@ vm.torch
   torch/tensor
   torch/tensor
   torch/topk
   torch/topk
   torch/topk_acc
   torch/topk_acc
-  torch/uniform
   torch/unsqueeze
   torch/unsqueeze
   torch/where
   torch/where
   torch/zeros_like
   torch/zeros_like

docs/api/python/torch/Tensor_.rst

@@ -26,6 +26,10 @@ id
 ###
 ###
 .. autoattribute:: dragon.vm.torch.Tensor.id
 .. autoattribute:: dragon.vm.torch.Tensor.id
 
 
+is_leaf
+#######
+.. autoattribute:: dragon.vm.torch.Tensor.is_leaf
+
 requires_grad
 requires_grad
 #############
 #############
 .. autoattribute:: dragon.vm.torch.Tensor.requires_grad
 .. autoattribute:: dragon.vm.torch.Tensor.requires_grad
@@ -341,6 +345,10 @@ reshape\_
 #########
 #########
 .. automethod:: dragon.vm.torch.Tensor.reshape_
 .. automethod:: dragon.vm.torch.Tensor.reshape_
 
 
+retain_grad
+###########
+.. automethod:: dragon.vm.torch.Tensor.retain_grad
+
 round
 round
 #####
 #####
 .. automethod:: dragon.vm.torch.Tensor.round
 .. automethod:: dragon.vm.torch.Tensor.round

docs/api/python/torch/normal.rst

-normal
-======
-
-.. autofunction:: dragon.vm.torch.normal
-
-
-.. raw:: html
-
-  <style>
-    h1:before {
-      content: "torch.";
-      color: #103d3e;
-    }
-  </style>

docs/api/python/torch/optim.rst

@@ -7,17 +7,17 @@ vm.torch.optim
   -------
   -------
 
 
   `class Adam <optim/Adam.html>`_
   `class Adam <optim/Adam.html>`_
-  : The optimizer which implements Adam algorithm.
+  : The optimizer to apply Adam algorithm.
 
 
   `class Optimizer <optim/Optimizer.html>`_
   `class Optimizer <optim/Optimizer.html>`_
   : The base class of optimizers.
   : The base class of optimizers.
 
 
   `class RMSprop <optim/RMSprop.html>`_
   `class RMSprop <optim/RMSprop.html>`_
-  : The optimizer which implements RMSprop algorithm.
+  : The optimizer to apply RMSprop algorithm.
   `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
   `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
 
 
   `class SGD <optim/SGD.html>`_
   `class SGD <optim/SGD.html>`_
-  : The optimizer which implements SGD algorithm.
+  : The optimizer to apply SGD algorithm.
 
 
 .. toctree::
 .. toctree::
   :hidden:
   :hidden:

docs/api/python/torch/uniform.rst

-uniform
-=======
-
-.. autofunction:: dragon.vm.torch.uniform
-
-.. raw:: html
-
-  <style>
-    h1:before {
-      content: "torch.";
-      color: #103d3e;
-    }
-  </style>

dragon/python/core/autograph/def_function.py

@@ -255,7 +255,7 @@ class FunctionGuard(object):
                             )
                             )
                         shape = input_signature[i].shape
                         shape = input_signature[i].shape
                         dtype = input_signature[i].dtype
                         dtype = input_signature[i].dtype
-                    inputs.append(Tensor(name, shape, dtype).variable())
+                    inputs.append(Tensor(name, shape, dtype).constant())
             with context.name_scope('${%d}' % id(self)), eager_context.graph_mode():
             with context.name_scope('${%d}' % id(self)), eager_context.graph_mode():
                 returns = nest.flatten(self._python_function(*inputs))
                 returns = nest.flatten(self._python_function(*inputs))
             outputs, dummies = [], []
             outputs, dummies = [], []

dragon/python/core/autograph/function_lib.py

@@ -259,7 +259,7 @@ class Function(object):
 
 
         """
         """
         self.outputs = [Tensor(name) for name in graph_def.output]
         self.outputs = [Tensor(name) for name in graph_def.output]
-        self.inputs = [Tensor(name).variable() for name in graph_def.input]
+        self.inputs = [Tensor(name).constant() for name in graph_def.input]
 
 
         # Fill with all known graph elements.
         # Fill with all known graph elements.
         add_device_option(graph_def)
         add_device_option(graph_def)
@@ -294,7 +294,7 @@ def create_function(inputs=None, outputs=None, givens=None, updater=None):
     Tensors that catch any operators can be used to create a graph:
     Tensors that catch any operators can be used to create a graph:
 
 
     ```python
     ```python
-    x = dragon.Tensor('x', dtype='float32').variable()
+    x = dragon.Tensor('x', dtype='float32').constant()
     y = x * 2
     y = x * 2
     f = dragon.create_function(outputs=y)
     f = dragon.create_function(outputs=y)
     ```
     ```
@@ -316,12 +316,12 @@ def create_function(inputs=None, outputs=None, givens=None, updater=None):
     Specify ``givens`` to substitute tensors before creating:
     Specify ``givens`` to substitute tensors before creating:
 
 
     ```python
     ```python
-    x = dragon.Tensor('x', dtype='float32').variable()
+    x = dragon.Tensor('x', dtype='float32').constant()
     y = x * 2
     y = x * 2
     foo = dragon.create_function(outputs=y)
     foo = dragon.create_function(outputs=y)
 
 
     # "bar" takes "x2" as input, and also writes to "y"
     # "bar" takes "x2" as input, and also writes to "y"
-    x2 = dragon.Tensor('x2', dtype='float32').variable()
+    x2 = dragon.Tensor('x2', dtype='float32').constant()
     bar = dragon.create_function(outputs=y, givens={x: x2})
     bar = dragon.create_function(outputs=y, givens={x: x2})
     ```
     ```
 
 

dragon/python/core/autograph/tensor.py

@@ -9,7 +9,7 @@
 #
 #
 # ------------------------------------------------------------
 # ------------------------------------------------------------
 
 
-"""Define the symbolic tensor abstraction."""
+"""The graph executing tensor."""
 
 
 from __future__ import absolute_import
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import division
@@ -26,12 +26,7 @@ from dragon.core.util import nest
 
 
 
 
 class Tensor(types.TensorMetaclass):
 class Tensor(types.TensorMetaclass):
-    """Tensor abstraction under the graph execution.
-
-    It is provided to construct operators symbolically,
-    while can also be a navigation to the storage.
-
-    """
+    """Tensor abstraction for graph executing."""
 
 
     def __init__(self, name=None, shape=None, dtype=None):
     def __init__(self, name=None, shape=None, dtype=None):
         """Create a ``Tensor``.
         """Create a ``Tensor``.
@@ -180,12 +175,12 @@ class Tensor(types.TensorMetaclass):
         return math_util.prod(self._shape)
         return math_util.prod(self._shape)
 
 
     def astype(self, dtype, inplace=False):
     def astype(self, dtype, inplace=False):
-        """Cast the data type to a specific one.
+        """Cast to the specified data type.
 
 
         Parameters
         Parameters
         ----------
         ----------
         dtype : str
         dtype : str
-            The specific data type.
+            The data type to cast to.
         inplace : bool, optional, default=False
         inplace : bool, optional, default=False
             Whether to do the cast in-place.
             Whether to do the cast in-place.
 
 
@@ -201,14 +196,14 @@ class Tensor(types.TensorMetaclass):
         """
         """
 
 
     def constant(self, value=0):
     def constant(self, value=0):
-        r"""Register as a variable with constant initializer.
+        r"""Register self to initialize from a scalar value.
 
 
         .. math:: \text{self} \leftarrow \text{value}
         .. math:: \text{self} \leftarrow \text{value}
 
 
         Parameters
         Parameters
         ----------
         ----------
         value : number, optional, default=0
         value : number, optional, default=0
-            The constant value.
+            The value to initialize.
 
 
         Returns
         Returns
         -------
         -------
@@ -219,7 +214,7 @@ class Tensor(types.TensorMetaclass):
         return self._register_as('constant', value=value)
         return self._register_as('constant', value=value)
 
 
     def copy(self):
     def copy(self):
-        """Return a tensor with containing data copied.
+        """Return a tensor with data copied.
 
 
         Returns
         Returns
         -------
         -------
@@ -228,29 +223,31 @@ class Tensor(types.TensorMetaclass):
 
 
         See Also
         See Also
         --------
         --------
-        `dragon.copy(...)`_ : Copy the value to ref.
+        `dragon.copy(...)`_ : Copy the input.
 
 
         """
         """
 
 
     def get_value(self):
     def get_value(self):
-        """Copy the data from storage.
+        """Return the value of implementation.
 
 
         Returns
         Returns
         -------
         -------
         numpy.ndarray
         numpy.ndarray
-            The deep copied value.
+            The deep-copied value.
 
 
         """
         """
 
 
-    def glorot_normal(self, scale=2.):
-        r"""Register as a variable with glorot normal initializer.
+    def glorot_normal(self, mode='fan_in', scale=2.0):
+        r"""Register self to initialize from a glorot uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow N(0, \sqrt{\frac{scale}{\text{FanIn}}})
+        .. math:: \text{self} \sim \mathcal{N}(0, \sqrt{\frac{scale}{\text{fan}}})
 
 
         Parameters
         Parameters
         ----------
         ----------
-        scale : number, optional, default=2.
-            The scale factor of distribution.
+        mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
+            The mode to compute fans.
+        scale : float, optional, default=2.0
+            The scale factor to distribution.
 
 
         Returns
         Returns
         -------
         -------
@@ -258,20 +255,20 @@ class Tensor(types.TensorMetaclass):
             The self.
             The self.
 
 
         """
         """
-        return self._register_as('glorot_normal', scale=scale)
+        return self._register_as('glorot_normal', mode=mode, scale=scale)
 
 
-    def glorot_uniform(self, scale=3.):
-        r"""Register as a variable with glorot uniform initializer.
+    def glorot_uniform(self, mode='fan_in', scale=3.0):
+        r"""Register self to initialize from a glorot uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow U(
-                -\sqrt{\frac{scale}{\text{FanIn}}},
-                \sqrt{\frac{scale}{\text{FanIn}}}
-            )
+        .. math:: \text{self} \sim \mathcal{U}(-\sqrt{\frac{scale}{\text{fan}}},
+                                                \sqrt{\frac{scale}{\text{fan}}})
 
 
         Parameters
         Parameters
         ----------
         ----------
-        scale : number, optional, default=3.
-            The scale factor of distribution.
+        mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
+            The mode to compute fans.
+        scale : float, optional, default=3.0
+            The scale factor to distribution.
 
 
         Returns
         Returns
         -------
         -------
@@ -279,19 +276,19 @@ class Tensor(types.TensorMetaclass):
             The self.
             The self.
 
 
         """
         """
-        return self._register_as('glorot_uniform', scale=scale)
+        return self._register_as('glorot_uniform', mode=mode, scale=scale)
 
 
     def normal(self, mean=0, std=1):
     def normal(self, mean=0, std=1):
-        r"""Register as a variable with normal initializer.
+        r"""Register self to initialize from a normal distribution.
 
 
-        .. math:: \text{self} \leftarrow N(\mu, \sigma)
+        .. math:: \text{self} \sim \mathcal{N}(\mu, \sigma)
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         std : number, optional, default=1
         std : number, optional, default=1
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         Returns
         Returns
         -------
         -------
@@ -301,19 +298,6 @@ class Tensor(types.TensorMetaclass):
         """
         """
         return self._register_as('normal', mean=mean, std=std)
         return self._register_as('normal', mean=mean, std=std)
 
 
-    def placeholder(self):
-        r"""Register as a placeholder with zero initializer.
-
-        .. math:: \text{self} \leftarrow 0
-
-        Returns
-        -------
-        dragon.Tensor
-            The self.
-
-        """
-        return self._register_as('placeholder')
-
     def reshape(self, shape):
     def reshape(self, shape):
         """Return a tensor containing the same data with new shape.
         """Return a tensor containing the same data with new shape.
 
 
@@ -334,12 +318,12 @@ class Tensor(types.TensorMetaclass):
         """
         """
 
 
     def set_value(self, value):
     def set_value(self, value):
-        """Feed the const value to the storage.
+        """Set value to the implementation.
 
 
         Parameters
         Parameters
         ----------
         ----------
         value : array_like
         value : array_like
-            The const value.
+            The value to set.
 
 
         Returns
         Returns
         -------
         -------
@@ -349,16 +333,16 @@ class Tensor(types.TensorMetaclass):
         """
         """
 
 
     def truncated_normal(self, mean=0, std=1):
     def truncated_normal(self, mean=0, std=1):
-        r"""Register as a variable with truncated normal initializer.
+        r"""Register self to initialize from a truncated normal distribution.
 
 
-        .. math:: \text{self} \leftarrow TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+        .. math:: \text{self} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         std : number, optional, default=1
         std : number, optional, default=1
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         Returns
         Returns
         -------
         -------
@@ -369,16 +353,16 @@ class Tensor(types.TensorMetaclass):
         return self._register_as('truncated_normal', mean=mean, std=std)
         return self._register_as('truncated_normal', mean=mean, std=std)
 
 
     def uniform(self, low=0, high=1):
     def uniform(self, low=0, high=1):
-        r"""Register as a variable with uniform initializer.
+        r"""Register self to initialize from an uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow U(\alpha, \beta)
+        .. math:: \text{self} \sim \mathcal{U}(\alpha, \beta)
 
 
         Parameters
         Parameters
         ----------
         ----------
         low : number, optional, default=0
         low : number, optional, default=0
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         high : number, optional, default=1
         high : number, optional, default=1
-            The value of :math:`\beta`.
+            The value to :math:`\beta`.
 
 
         Returns
         Returns
         -------
         -------
@@ -388,19 +372,6 @@ class Tensor(types.TensorMetaclass):
         """
         """
         return self._register_as('uniform', low=low, high=high)
         return self._register_as('uniform', low=low, high=high)
 
 
-    def variable(self):
-        r"""Register as a variable with zero initializer.
-
-        .. math:: self \leftarrow 0
-
-        Returns
-        -------
-        dragon.Tensor
-            The self.
-
-        """
-        return self._register_as('variable')
-
     @classmethod
     @classmethod
     def convert_to(cls, value, dtype=None, name=None):
     def convert_to(cls, value, dtype=None, name=None):
         """Convert the given ``value`` to a ``dragon.Tensor``.
         """Convert the given ``value`` to a ``dragon.Tensor``.
@@ -435,6 +406,7 @@ class Tensor(types.TensorMetaclass):
         """Fill self with the specific type of filler."""
         """Fill self with the specific type of filler."""
         filler = dragon_pb2.FillerInfo()
         filler = dragon_pb2.FillerInfo()
         filler.type = type.lower()
         filler.type = type.lower()
+        variance_norm = {'fan_in': 0, 'fan_out': 1, 'fan_avg': 2}
         if filler.type == 'constant':
         if filler.type == 'constant':
             filler.value = kwargs['value'] if 'value' in kwargs else 0
             filler.value = kwargs['value'] if 'value' in kwargs else 0
         elif filler.type in ['normal', 'gaussian']:
         elif filler.type in ['normal', 'gaussian']:
@@ -451,15 +423,15 @@ class Tensor(types.TensorMetaclass):
             filler.high = filler.mean + 2.0 * filler.std
             filler.high = filler.mean + 2.0 * filler.std
         elif filler.type in ['glorot_uniform', 'xavier']:
         elif filler.type in ['glorot_uniform', 'xavier']:
             filler.scale = kwargs['scale'] if 'scale' in kwargs else 3
             filler.scale = kwargs['scale'] if 'scale' in kwargs else 3
+            filler.variance_norm = variance_norm[kwargs.get('mode', 'fan_in')]
         elif filler.type in ['glorot_normal', 'msra']:
         elif filler.type in ['glorot_normal', 'msra']:
             filler.scale = kwargs['scale'] if 'scale' in kwargs else 2
             filler.scale = kwargs['scale'] if 'scale' in kwargs else 2
+            filler.variance_norm = variance_norm[kwargs.get('mode', 'fan_in')]
         workspace.get_workspace().create_tensor(self.name, filler)
         workspace.get_workspace().create_tensor(self.name, filler)
         return self
         return self
 
 
     def __add__(self, other):
     def __add__(self, other):
-        r"""Compute the element-wise addition.
-
-        .. math:: \text{out} = \text{self} + \text{other}
+        """Compute the element-wise addition.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -469,33 +441,8 @@ class Tensor(types.TensorMetaclass):
         Returns
         Returns
         -------
         -------
         dragon.Tensor
         dragon.Tensor
-            The **y**.
-
-        See Also
-        --------
-        `dragon.math.add(...)`_ : Compute the element-wise addition.
-
-        """
-
-    def __div__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{out} = \text{self} \div \text{other}
-
-        Parameters
-        ----------
-        other : Union[dragon.Tensor, number]
-            The value to divide.
-
-        Returns
-        -------
-        dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.div(...)`_ : Compute the element-wise division.
-
         """
         """
 
 
     def __float__(self):
     def __float__(self):
@@ -510,9 +457,7 @@ class Tensor(types.TensorMetaclass):
         return float(self.get_value())
         return float(self.get_value())
 
 
     def __ge__(self, other):
     def __ge__(self, other):
-        r"""Compute element-wise greater-equal comparison.
-
-        .. math:: \text{out} = (\text{self} \geq \text{other})
+        """Compute element-wise greater-equal comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -524,39 +469,25 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.greater_equal(...)`_ : Compute element-wise greater-equal comparison.
-
         """
         """
 
 
     def __getitem__(self, item):
     def __getitem__(self, item):
-        """Select the elements at the specific indices.
+        """Select elements at the specific index.
 
 
         Parameters
         Parameters
         ----------
         ----------
         item : Union[int, slice, dragon.Tensor]
         item : Union[int, slice, dragon.Tensor]
-            The indices.
+            The index.
 
 
         Returns
         Returns
         -------
         -------
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.slice(...)`_ : Select the elements according to the given sections.
-
-        See Also
-        --------
-        `dragon.masked_select(...)`_ : Select the elements where the given mask is 1.
-
         """
         """
 
 
     def __gt__(self, other):
     def __gt__(self, other):
-        r"""Compute element-wise greater comparison.
-
-        .. math:: \text{out} = (\text{self} > \text{other})
+        """Compute element-wise greater comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -568,30 +499,24 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.greater(...)`_ : Compute element-wise greater comparison.
-
         """
         """
 
 
     def __hash__(self):
     def __hash__(self):
         return id(self)
         return id(self)
 
 
     def __int__(self):
     def __int__(self):
-        """Return a int python scalar.
+        """Return an integer python scalar.
 
 
         Returns
         Returns
         -------
         -------
         int
         int
-            The int value.
+            The integer value.
 
 
         """
         """
         return int(self.get_value())
         return int(self.get_value())
 
 
     def __le__(self, other):
     def __le__(self, other):
-        r"""Compute element-wise less-equal comparison.
-
-        .. math:: \text{out} = (\text{self} \leq \text{other})
+        """Compute element-wise less-equal comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -603,16 +528,10 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.less_equal(...)`_ : Compute element-wise less-equal comparison.
-
         """
         """
 
 
     def __lt__(self, other):
     def __lt__(self, other):
-        r"""Compute element-wise less comparison.
-
-        .. math:: \text{out} = (\text{self} < \text{other})
+        """Compute element-wise less comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -624,16 +543,10 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.less(...)`_ : Compute element-wise less comparison.
-
         """
         """
 
 
     def __mul__(self, other):
     def __mul__(self, other):
-        r"""Compute the element-wise multiplication.
-
-        .. math:: \text{out} = \text{self} \times \text{other}
+        """Compute the element-wise multiplication.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -645,32 +558,20 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
         """
         """
 
 
     def __neg__(self):
     def __neg__(self):
-        r"""Compute the element-wise negative.
-
-        .. math:: y = -x
+        """Compute the element-wise negative.
 
 
         Returns
         Returns
         -------
         -------
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.negative(...)`_ : Compute the element-wise negative.
-
         """
         """
 
 
     def __radd__(self, other):
     def __radd__(self, other):
-        r"""Compute the element-wise addition.
-
-        .. math:: \text{out} = \text{other} + \text{self}
+        """Compute the element-wise addition.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -682,10 +583,6 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.add(...)`_ : Compute the element-wise addition.
-
         """
         """
 
 
     def __repr__(self):
     def __repr__(self):
@@ -697,10 +594,8 @@ class Tensor(types.TensorMetaclass):
         return 'Tensor("{}", shape={}, dtype={})' \
         return 'Tensor("{}", shape={}, dtype={})' \
             .format(self.name, shape_str, self.dtype)
             .format(self.name, shape_str, self.dtype)
 
 
-    def __rdiv__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{out} = \text{other} \div \text{self}
+    def __rtruediv__(self, other):
+        """Compute the element-wise division.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -712,16 +607,10 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.div(...)`_ : Compute the element-wise division.
-
         """
         """
 
 
     def __rmul__(self, other):
     def __rmul__(self, other):
-        r"""Compute the element-wise multiplication.
-
-        .. math:: \text{out} = \text{other} \times \text{self}
+        """Compute the element-wise multiplication.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -733,16 +622,10 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
         """
         """
 
 
     def __rsub__(self, other):
     def __rsub__(self, other):
-        r"""Compute the element-wise subtraction.
-
-        .. math:: \text{out} = \text{other} - \text{self}
+        """Compute the element-wise subtraction.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -754,36 +637,22 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
         """
         """
 
 
     def __setitem__(self, key, value):
     def __setitem__(self, key, value):
-        """Set the value at the specific indices.
+        """Set elements at the specific index.
 
 
         Parameters
         Parameters
         ----------
         ----------
         key : Union[int, slice, dragon.Tensor]
         key : Union[int, slice, dragon.Tensor]
-            The indices.
-        value : number or dragon.Tensor
-            The value.
-
-        See Also
-        --------
-        `dragon.assign(...)`_ : Assign the value to ref.
-
-        See Also
-        --------
-        `dragon.masked_assign(...)`_ : Assign the value to ref where mask is 1.
+            The index.
+        value : Union[dragon.Tensor, number]
+            The value to set.
 
 
         """
         """
 
 
     def __sub__(self, other):
     def __sub__(self, other):
-        r"""Compute the element-wise subtraction.
-
-        .. math:: \text{out} = \text{self} - \text{value}
+        """Compute the element-wise subtraction.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -795,9 +664,20 @@ class Tensor(types.TensorMetaclass):
         dragon.Tensor
         dragon.Tensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
+        """
+
+    def __truediv__(self, other):
+        """Compute the element-wise division.
+
+        Parameters
+        ----------
+        other : Union[dragon.Tensor, number]
+            The value to divide.
+
+        Returns
+        -------
+        dragon.Tensor
+            The output tensor.
 
 
         """
         """
 
 

dragon/python/core/eager/tensor.py

@@ -9,7 +9,7 @@
 #
 #
 # ------------------------------------------------------------
 # ------------------------------------------------------------
 
 
-"""Define the eager tensor abstraction."""
+"""The eager executing tensor."""
 
 
 from __future__ import absolute_import
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import division
@@ -23,10 +23,9 @@ from dragon.core.framework import workspace
 
 
 
 
 class EagerTensor(Tensor):
 class EagerTensor(Tensor):
-    """Tensor abstraction under the eager execution.
+    """Tensor abstraction for eager executing.
 
 
-    This abstraction involves the garbage collection,
-    thus, life-cycle should be considered to avoid memory leak.
+    Examples:
 
 
     ```python
     ```python
     # Create a tensor with shape and dtype
     # Create a tensor with shape and dtype
@@ -173,12 +172,12 @@ class EagerTensor(Tensor):
         return self._impl.size
         return self._impl.size
 
 
     def astype(self, dtype, inplace=False):
     def astype(self, dtype, inplace=False):
-        """Cast the data type to a specific one.
+        """Cast to the specified data type.
 
 
         Parameters
         Parameters
         ----------
         ----------
         dtype : str
         dtype : str
-            The specific data type.
+            The data type to cast to.
         inplace : bool, optional, default=False
         inplace : bool, optional, default=False
             Whether to do the cast in-place.
             Whether to do the cast in-place.
 
 
@@ -194,14 +193,14 @@ class EagerTensor(Tensor):
         """
         """
 
 
     def constant(self, value=0):
     def constant(self, value=0):
-        r"""Fill self with a constant value.
+        r"""Fill self from a scalar value.
 
 
         .. math:: \text{self} \leftarrow \text{value}
         .. math:: \text{self} \leftarrow \text{value}
 
 
         Parameters
         Parameters
         ----------
         ----------
         value : number, optional, default=0
         value : number, optional, default=0
-            The constant value.
+            The value to fill.
 
 
         Returns
         Returns
         -------
         -------
@@ -211,7 +210,7 @@ class EagerTensor(Tensor):
         """
         """
 
 
     def copy(self):
     def copy(self):
-        """Return a tensor with containing data copied.
+        """Return a tensor with data copied.
 
 
         Returns
         Returns
         -------
         -------
@@ -220,32 +219,32 @@ class EagerTensor(Tensor):
 
 
         See Also
         See Also
         --------
         --------
-        `dragon.copy(...)`_ : Copy the value to ref.
+        `dragon.copy(...)`_ : Copy the input.
 
 
         """
         """
 
 
     def get_value(self):
     def get_value(self):
-        """Return the value from storage.
+        """Return the value of implementation.
 
 
         Returns
         Returns
         -------
         -------
         numpy.ndarray
         numpy.ndarray
-            The shallow copied value.
+            The zero-copied value.
 
 
         """
         """
         return self.numpy()
         return self.numpy()
 
 
-    def glorot_normal(self, mode='FAN_IN', scale=2.):
+    def glorot_normal(self, mode='fan_in', scale=2.0):
         r"""Fill self from a glorot normal distribution.
         r"""Fill self from a glorot normal distribution.
 
 
-        .. math:: \text{self} \leftarrow N(0, \sqrt{\frac{scale}{\text{FAN}}})
+        .. math:: \text{self} \sim \mathcal{N}(0, \sqrt{\frac{scale}{\text{fan}}})
 
 
         Parameters
         Parameters
         ----------
         ----------
-        mode : {'FAN_IN, 'FAN_OUT', 'FAN_AVG'}, optional
+        mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
             The mode to compute fans.
             The mode to compute fans.
-        scale : number, optional, default=2.
-            The scale factor of distribution.
+        scale : float, optional, default=2.0
+            The scale factor to distribution.
 
 
         Returns
         Returns
         -------
         -------
@@ -254,20 +253,18 @@ class EagerTensor(Tensor):
 
 
         """
         """
 
 
-    def glorot_uniform(self, mode='FAN_IN', scale=3.):
+    def glorot_uniform(self, mode='fan_in', scale=3.0):
         r"""Fill self from a glorot uniform distribution.
         r"""Fill self from a glorot uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow U(
-                    -\sqrt{\frac{scale}{\text{FAN}}},
-                    \sqrt{\frac{scale}{\text{FAN}}}
-                )
+        .. math:: \text{self} \sim \mathcal{U}(-\sqrt{\frac{scale}{\text{fan}}},
+                                                \sqrt{\frac{scale}{\text{fan}}})
 
 
         Parameters
         Parameters
         ----------
         ----------
-        mode : {'FAN_IN, 'FAN_OUT', 'FAN_AVG'}, optional
+        mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
             The mode to compute fans.
             The mode to compute fans.
-        scale : number, optional, default=3.
-            The scale factor of distribution.
+        scale : float, optional, default=3.0
+            The scale factor to distribution.
 
 
         Returns
         Returns
         -------
         -------
@@ -295,14 +292,14 @@ class EagerTensor(Tensor):
     def normal(self, mean=0, std=1):
     def normal(self, mean=0, std=1):
         r"""Fill self from a normal distribution.
         r"""Fill self from a normal distribution.
 
 
-        .. math:: \text{self} \leftarrow N(\mu, \sigma)
+        .. math:: \text{self} \sim \mathcal{N}(\mu, \sigma)
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         std : number, optional, default=1
         std : number, optional, default=1
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         Returns
         Returns
         -------
         -------
@@ -331,12 +328,12 @@ class EagerTensor(Tensor):
         """
         """
 
 
     def set_value(self, value):
     def set_value(self, value):
-        """Map the value to storage.
+        """Set value to the implementation.
 
 
         Parameters
         Parameters
         ----------
         ----------
         value : array_like
         value : array_like
-            The value.
+            The value to set.
 
 
         Returns
         Returns
         -------
         -------
@@ -352,14 +349,14 @@ class EagerTensor(Tensor):
     def truncated_normal(self, mean=0, std=1):
     def truncated_normal(self, mean=0, std=1):
         r"""Fill self from a truncated normal distribution.
         r"""Fill self from a truncated normal distribution.
 
 
-        .. math:: \text{self} \leftarrow TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+        .. math:: \text{self} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         std : number, optional, default=1
         std : number, optional, default=1
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         Returns
         Returns
         -------
         -------
@@ -369,16 +366,16 @@ class EagerTensor(Tensor):
         """
         """
 
 
     def uniform(self, low=0, high=1):
     def uniform(self, low=0, high=1):
-        self.self__ = r"""Fill self from a uniform distribution.
+        r"""Fill self from an uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow U(\alpha, \beta)
+        .. math:: \text{self} \sim \mathcal{U}(\alpha, \beta)
 
 
         Parameters
         Parameters
         ----------
         ----------
         low : number, optional, default=0
         low : number, optional, default=0
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         high : number, optional, default=1
         high : number, optional, default=1
-            The value of :math:`\beta`.
+            The value to :math:`\beta`.
 
 
         Returns
         Returns
         -------
         -------
@@ -404,9 +401,7 @@ class EagerTensor(Tensor):
             context.get_eager_scope())).FromShape(shape, dtype)
             context.get_eager_scope())).FromShape(shape, dtype)
 
 
     def __add__(self, other):
     def __add__(self, other):
-        r"""Compute the element-wise addition.
-
-        .. math:: \text{out} = \text{self} + \text{value}
+        """Compute the element-wise addition.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -418,10 +413,6 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.add(...)`_ : Compute the element-wise addition.
-
         """
         """
 
 
     def __del__(self):
     def __del__(self):
@@ -430,27 +421,6 @@ class EagerTensor(Tensor):
             # PyGC will detect them automatically.
             # PyGC will detect them automatically.
             self._gc.collect(self.id)
             self._gc.collect(self.id)
 
 
-    def __div__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{out} = \text{self} \div \text{value}
-
-        Parameters
-        ----------
-        other : Union[dragon.EagerTensor, number]
-            The value to divide.
-
-        Returns
-        -------
-        dragon.EagerTensor
-            The output tensor.
-
-        See Also
-        --------
-        `dragon.math.div(...)`_ : Compute the element-wise division.
-
-        """
-
     def __float__(self):
     def __float__(self):
         """Return a float python scalar.
         """Return a float python scalar.
 
 
@@ -463,9 +433,7 @@ class EagerTensor(Tensor):
         return float(self.numpy())
         return float(self.numpy())
 
 
     def __ge__(self, other):
     def __ge__(self, other):
-        r"""Compute element-wise greater-equal comparison.
-
-        .. math:: \text{out} = (\text{self} \geq \text{other})
+        """Compute element-wise greater-equal comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -477,39 +445,25 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.greater_equal(...)`_ : Compute element-wise greater-equal comparison.
-
         """
         """
 
 
     def __getitem__(self, item):
     def __getitem__(self, item):
-        """Select the elements at the specific indices.
+        """Select elements at the specific index.
 
 
         Parameters
         Parameters
         ----------
         ----------
         item : Union[int, slice, dragon.EagerTensor]
         item : Union[int, slice, dragon.EagerTensor]
-            The indices.
+            The index.
 
 
         Returns
         Returns
         -------
         -------
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.slice(...)`_ : Select the elements according to the given sections.
-
-        See Also
-        --------
-        `dragon.masked_select(...)`_ : Select the elements where the given mask is 1.
-
         """
         """
 
 
     def __gt__(self, other):
     def __gt__(self, other):
-        r"""Compute element-wise greater comparison.
-
-        .. math:: \text{out} = (\text{self} > \text{other})
+        """Compute element-wise greater comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -521,19 +475,13 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.greater(...)`_ : Compute element-wise greater comparison.
-
         """
         """
 
 
     def __hash__(self):
     def __hash__(self):
         return id(self)
         return id(self)
 
 
     def __iadd__(self, other):
     def __iadd__(self, other):
-        r"""Compute the element-wise addition.
-
-        .. math:: \text{self} \mathrel{+}= \text{other}
+        """Compute the element-wise addition.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -545,37 +493,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The self.
             The self.
 
 
-        See Also
-        --------
-        `dragon.math.add(...)`_ : Compute the element-wise addition.
-
-        """
-
-    def __idiv__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{self} \mathrel{\div}= \text{other}
-
-        Parameters
-        ----------
-        other : Union[dragon.EagerTensor, number]
-            The value to divide.
-
-        Returns
-        -------
-        dragon.EagerTensor
-            The self.
-
-        See Also
-        --------
-        `dragon.math.div(...)`_ : Compute the element-wise division.
-
         """
         """
 
 
     def __imul__(self, other):
     def __imul__(self, other):
-        r"""Compute the element-wise multiplication.
-
-        .. math:: \text{self} \mathrel{\times}= \text{other}
+        """Compute the element-wise multiplication.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -587,27 +508,21 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The self.
             The self.
 
 
-        See Also
-        --------
-        `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
         """
         """
 
 
     def __int__(self):
     def __int__(self):
-        """Return a int python scalar.
+        """Return a integer python scalar.
 
 
         Returns
         Returns
         -------
         -------
         int
         int
-            The int value.
+            The integer value.
 
 
         """
         """
         return int(self.__float__())
         return int(self.__float__())
 
 
     def __isub__(self, other):
     def __isub__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{self} \mathrel{-}= \text{other}
+        """Compute the element-wise subtraction.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -619,16 +534,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The self.
             The self.
 
 
-        See Also
-        --------
-        `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
         """
         """
 
 
     def __le__(self, other):
     def __le__(self, other):
-        r"""Compute element-wise less-equal comparison.
-
-        .. math:: \text{out} = (\text{self} \leq \text{other})
+        """Compute element-wise less-equal comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -640,16 +549,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.less_equal(...)`_ : Compute element-wise less-equal comparison.
-
         """
         """
 
 
     def __lt__(self, other):
     def __lt__(self, other):
-        r"""Compute element-wise less comparison.
-
-        .. math:: \text{out} = (\text{self} < \text{other})
+        """Compute element-wise less comparison.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -661,16 +564,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.less(...)`_ : Compute element-wise less comparison.
-
         """
         """
 
 
     def __mul__(self, other):
     def __mul__(self, other):
-        r"""Compute the element-wise multiplication.
-
-        .. math:: \text{out} = \text{self} \times \text{other}
+        """Compute the element-wise multiplication.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -682,32 +579,20 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
         """
         """
 
 
     def __neg__(self):
     def __neg__(self):
-        r"""Compute the element-wise negative.
-
-        .. math:: y = -x
+        """Compute the element-wise negative.
 
 
         Returns
         Returns
         -------
         -------
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.negative(...)`_ : Compute the element-wise negative.
-
         """
         """
 
 
     def __radd__(self, other):
     def __radd__(self, other):
-        r"""Compute the element-wise addition.
-
-        .. math:: \text{out} = \text{other} + \text{self}
+        """Compute the element-wise addition.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -719,10 +604,6 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.add(...)`_ : Compute the element-wise addition.
-
         """
         """
 
 
     def __repr__(self):
     def __repr__(self):
@@ -738,10 +619,8 @@ class EagerTensor(Tensor):
             .format(shape_str, self.dtype, str(self._device))
             .format(shape_str, self.dtype, str(self._device))
         return content_str + meta_str
         return content_str + meta_str
 
 
-    def __rdiv__(self, other):
-        r"""Compute the element-wise division.
-
-        .. math:: \text{out} = \text{value} \div \text{self}
+    def __rtruediv__(self, other):
+        """Compute the element-wise division.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -753,16 +632,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.div(...)`_ : Compute the element-wise division.
-
         """
         """
 
 
     def __rmul__(self, other):
     def __rmul__(self, other):
-        r"""Compute the element-wise multiplication.
-
-        .. math:: \text{out} = \text{other} \times \text{self}
+        """Compute the element-wise multiplication.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -774,16 +647,10 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
         """
         """
 
 
     def __rsub__(self, other):
     def __rsub__(self, other):
-        r"""Compute the element-wise subtraction.
-
-        .. math:: \text{out} = \text{other} - \text{self}
+        """Compute the element-wise subtraction.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -795,36 +662,22 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
         """
         """
 
 
     def __setitem__(self, key, value):
     def __setitem__(self, key, value):
-        """Set the value at the specific indices.
+        """Set elements at the specific index.
 
 
         Parameters
         Parameters
         ----------
         ----------
         key : Union[int, slice, dragon.EagerTensor]
         key : Union[int, slice, dragon.EagerTensor]
-            The indices.
-        value : number or dragon.EagerTensor
-            The value.
-
-        See Also
-        --------
-        `dragon.assign(...)`_ : Assign the value to ref.
-
-        See Also
-        --------
-        `dragon.masked_assign(...)`_ : Assign the value to ref where mask is 1.
+            The index.
+        value : Union[dragon.EagerTensor, number]
+            The value to set.
 
 
         """
         """
 
 
     def __sub__(self, other):
     def __sub__(self, other):
-        r"""Compute the element-wise subtraction.
-
-        .. math:: \text{out} = \text{self} - \text{other}
+        """Compute the element-wise subtraction.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -836,8 +689,19 @@ class EagerTensor(Tensor):
         dragon.EagerTensor
         dragon.EagerTensor
             The output tensor.
             The output tensor.
 
 
-        See Also
-        --------
-        `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
+        """
+
+    def __truediv__(self, other):
+        """Compute the element-wise division.
+
+        Parameters
+        ----------
+        other : Union[dragon.EagerTensor, number]
+            The value to divide.
+
+        Returns
+        -------
+        dragon.EagerTensor
+            The output tensor.
 
 
         """
         """

dragon/python/core/framework/workspace.py

@@ -261,7 +261,7 @@ class Workspace(backend.Workspace):
         return self.HasTensor(_stringify_object(tensor))
         return self.HasTensor(_stringify_object(tensor))
 
 
     def merge_from(self, other):
     def merge_from(self, other):
-        """Merge resources from another workspace.
+        """Merge resources from the other.
 
 
         The ``other`` will not be reset until ``self`` is reset.
         The ``other`` will not be reset until ``self`` is reset.
         Carefulness should be taken to associate with the workspaces.
         Carefulness should be taken to associate with the workspaces.

dragon/python/core/ops/activation_ops.py

@@ -202,7 +202,7 @@ def elu(inputs, alpha=1., **kwargs):
     inputs : dragon.Tensor
     inputs : dragon.Tensor
         The input tensor.
         The input tensor.
     alpha : float, optional, default=1.
     alpha : float, optional, default=1.
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
 
 
     Returns
     Returns
     -------
     -------
@@ -247,7 +247,7 @@ def leaky_relu(inputs, alpha=0.2, **kwargs):
     inputs : dragon.Tensor
     inputs : dragon.Tensor
         The input tensor.
         The input tensor.
     alpha : number, optional, default=0.2
     alpha : number, optional, default=0.2
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
 
 
     Returns
     Returns
     -------
     -------
@@ -466,9 +466,9 @@ def selu(inputs, alpha=1.67326, gamma=1.0507, **kwargs):
     inputs : dragon.Tensor
     inputs : dragon.Tensor
         The input tensor.
         The input tensor.
     alpha : float, optional, default=1.67326
     alpha : float, optional, default=1.67326
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     gamma : float, optional, default=1.0507
     gamma : float, optional, default=1.0507
-        The value of :math:`\gamma`.
+        The value to :math:`\gamma`.
 
 
     Returns
     Returns
     -------
     -------

dragon/python/core/ops/array_ops.py

@@ -25,7 +25,7 @@ from dragon.core.util import nest
 
 
 
 
 def arange(start, stop=None, step=1, dtype='int64', **kwargs):
 def arange(start, stop=None, step=1, dtype='int64', **kwargs):
-    r"""Return a tensor with evenly spaced values within a interval.
+    r"""Return a tensor of evenly spaced values within a interval.
 
 
     Specify ``start`` and ``stop`` to determine an interval:
     Specify ``start`` and ``stop`` to determine an interval:
 
 
@@ -537,7 +537,7 @@ def flatten(inputs, axis=0, num_axes=-1, keep_axes=None, **kwargs):
     Examples:
     Examples:
 
 
     ```python
     ```python
-    x = dragon.Tensor(shape=[1, 2, 3, 4]).variable()
+    x = dragon.Tensor(shape=[1, 2, 3, 4]).constant()
     print(dragon.flatten(x, axis=1, num_axes=-1).shape)  # (1, 24)
     print(dragon.flatten(x, axis=1, num_axes=-1).shape)  # (1, 24)
     print(dragon.flatten(x, axis=1, num_axes=2).shape)  # (1, 6, 4)
     print(dragon.flatten(x, axis=1, num_axes=2).shape)  # (1, 6, 4)
     print(dragon.flatten(x, keep_axes=1))  # (24,)
     print(dragon.flatten(x, keep_axes=1))  # (24,)
@@ -634,7 +634,7 @@ def index_select(inputs, indices, axis=0, **kwargs):
 
 
 @OpSchema.num_inputs(2)
 @OpSchema.num_inputs(2)
 def masked_select(inputs, **kwargs):
 def masked_select(inputs, **kwargs):
-    """Select the elements where the given mask is **1**.
+    """Select the elements of input where mask is 1.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -908,14 +908,14 @@ def multinomial(inputs, num_samples=1, eps=0., normalize=False, **kwargs):
 
 
 @OpSchema.num_inputs(1)
 @OpSchema.num_inputs(1)
 def nonzero(inputs, **kwargs):
 def nonzero(inputs, **kwargs):
-    r"""Return the indices of non-zero elements.
+    r"""Return the index of non-zero elements.
 
 
-    .. math:: y = \{i, \text{ if } x[i] \text{ is True }\}
+    .. math:: \text{out} = \{i, \text{ if } \text{input}[i] \neq 0
 
 
     Parameters
     Parameters
     ----------
     ----------
     inputs : dragon.Tensor
     inputs : dragon.Tensor
-        The tensor :math:`x`.
+        The input tensor.
 
 
     Returns
     Returns
     -------
     -------
@@ -938,8 +938,8 @@ def one_hot(inputs, depth, on_value=1, off_value=0, **kwargs):
     .. math::
     .. math::
         \text{out}[i][j] =
         \text{out}[i][j] =
         \begin{cases}
         \begin{cases}
-            \text{Val}_{off}, & \text{ if } x[i] \neq j \\
-            \text{Val}_{on}, & \text{ otherwise }
+            \text{off\_value}, & \text{ if } \text{input}[i] \neq j \\
+            \text{on\_value}, & \text{ otherwise }
         \end{cases}
         \end{cases}
 
 
     The max value of indices, i.e., the ``depth`` should be specified:
     The max value of indices, i.e., the ``depth`` should be specified:

dragon/python/core/ops/control_flow_ops.py

@@ -26,23 +26,23 @@ from dragon.core.util import nest
 @ArgHelper.repeated_desc('starts')
 @ArgHelper.repeated_desc('starts')
 @ArgHelper.repeated_desc('sizes')
 @ArgHelper.repeated_desc('sizes')
 def assign(inputs, starts=None, sizes=None, **kwargs):
 def assign(inputs, starts=None, sizes=None, **kwargs):
-    r"""Assign the value to ref.
+    r"""Assign the value to input.
 
 
-    .. math:: \text{Ref}[start:start + size, ...] = \text{Value}
+    .. math:: \text{input}[\text{start}:\text{start} + \text{size}, ...] = \text{value}
 
 
     Parameters
     Parameters
     ----------
     ----------
     inputs : Sequence[dragon.Tensor]
     inputs : Sequence[dragon.Tensor]
-        The **ref** and **value**.
+        The input and value tensor.
     starts : Sequence[Union[int, dragon.Tensor]], optional
     starts : Sequence[Union[int, dragon.Tensor]], optional
-        The start pos of each dimension.
+        The start location for each dimension.
     sizes : Sequence[Union[int, dragon.Tensor]], optional
     sizes : Sequence[Union[int, dragon.Tensor]], optional
-        The size of each dimension.
+        The number of elements assigned from start.
 
 
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
-        The **ref**.
+        The input tensor.
 
 
     """
     """
     args = parse_args(locals())
     args = parse_args(locals())
@@ -104,24 +104,24 @@ def copy(inputs, **kwargs):
 
 
 @OpSchema.num_inputs(3)
 @OpSchema.num_inputs(3)
 def masked_assign(inputs, **kwargs):
 def masked_assign(inputs, **kwargs):
-    r"""Assign the value to ref where mask is **1**.
+    r"""Assign the value to input where mask is 1.
 
 
     .. math::
     .. math::
-        \text{Ref}[i] =
+        \text{input}[i] =
             \begin{cases}
             \begin{cases}
-            \text{Value}[i], & \text{ if } \text{Mask}[i] = 1 \\
-            \text{Ref}[i], & \text{ otherwise }
+                \text{value}[i], & \text{ if } \text{mask}[i] = 1 \\
+                \text{input}[i], & \text{ otherwise }
         \end{cases}
         \end{cases}
 
 
     Parameters
     Parameters
     ----------
     ----------
     inputs : Sequence[dragon.Tensor]
     inputs : Sequence[dragon.Tensor]
-        The **ref**, **value** and **mask** tensor.
+        The input, value and mask tensor.
 
 
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
-        The **ref** tensor..
+        The input tensor.
 
 
     """
     """
     args = parse_args(locals())
     args = parse_args(locals())

dragon/python/core/ops/init_ops.py

@@ -27,7 +27,7 @@ from dragon.core.ops.utils import parse_args
 
 
 
 
 def constant(value, dtype=None, shape=None, name=None):
 def constant(value, dtype=None, shape=None, name=None):
-    r"""Return a tensor taking the value content.
+    r"""Return a tensor initialized from the value.
 
 
     Examples:
     Examples:
 
 
@@ -40,7 +40,7 @@ def constant(value, dtype=None, shape=None, name=None):
     Parameters
     Parameters
     ----------
     ----------
     value : array_like
     value : array_like
-        The constant value.
+        The value to initialize from.
     dtype : str, optional
     dtype : str, optional
         The optional data type.
         The optional data type.
     shape : Sequence[int], optional
     shape : Sequence[int], optional
@@ -68,7 +68,7 @@ def constant(value, dtype=None, shape=None, name=None):
 
 
 
 
 def eye(n, m=None, k=0, dtype='float32', **kwargs):
 def eye(n, m=None, k=0, dtype='float32', **kwargs):
-    r"""Return a tensor constructed as the identity matrix.
+    """Return a tensor constructed as the identity matrix.
 
 
     The rows and cols of matrix are determined by ``n`` and ``m``:
     The rows and cols of matrix are determined by ``n`` and ``m``:
 
 
@@ -131,7 +131,7 @@ def eye(n, m=None, k=0, dtype='float32', **kwargs):
 
 
 @OpSchema.num_inputs(1)
 @OpSchema.num_inputs(1)
 def eye_like(other, k=0, dtype='float32', **kwargs):
 def eye_like(other, k=0, dtype='float32', **kwargs):
-    r"""Return a tensor shaping like another constructed as the identity matrix.
+    """Return a tensor of identity matrix with shape as the other.
 
 
     The rows and cols of matrix are hinted by the input tensor:
     The rows and cols of matrix are hinted by the input tensor:
 
 
@@ -183,14 +183,14 @@ def eye_like(other, k=0, dtype='float32', **kwargs):
 
 
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
 def fill(shape, value=0, dtype=None, **kwargs):
 def fill(shape, value=0, dtype=None, **kwargs):
-    r"""Return a tensor filled with the specific value.
+    r"""Return a tensor filled with the scalar value.
 
 
-    .. math:: y \leftarrow \text{value}
+    .. math:: \text{out} \leftarrow \text{value}
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     value : number, optional, default=0
     value : number, optional, default=0
         The value to fill.
         The value to fill.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
@@ -224,21 +224,19 @@ def fill(shape, value=0, dtype=None, **kwargs):
 
 
 
 
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
-def glorot_normal(shape, scale=2, mode='FAN_IN', dtype='float32', **kwargs):
+def glorot_normal(shape, scale=2.0, mode='fan_in', dtype='float32', **kwargs):
     r"""Return a tensor initialized from the glorot normal distribution.
     r"""Return a tensor initialized from the glorot normal distribution.
 
 
-    The **GlorotNormal** distribution is defined as:
-
-    .. math:: X \sim N(0, \sqrt{\frac{scale}{\text{FAN}}})
+    .. math:: \text{out} \sim \mathcal{N}(0, \sqrt{\frac{scale}{\text{fan}}})
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
-    scale : number, optional, default=2
-        The scale factor of distribution.
-    mode : {'FAN_IN', 'FAN_OUT', 'FAN_AVG'}, optional
-        The mode to compute the normalizer.
+        The tensor shape.
+    mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
+        The mode to compute fans.
+    scale : float, optional, default=2.0
+        The scale factor to distribution.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -266,25 +264,21 @@ def glorot_normal(shape, scale=2, mode='FAN_IN', dtype='float32', **kwargs):
 
 
 
 
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
 @ArgHelper.repeated_desc(name='shape', name_v2='dims')
-def glorot_uniform(shape, scale=3, mode='FAN_IN', dtype='float32', **kwargs):
+def glorot_uniform(shape, mode='fan_in', scale=3.0, dtype='float32', **kwargs):
     r"""Return a tensor initialized from the glorot uniform distribution.
     r"""Return a tensor initialized from the glorot uniform distribution.
 
 
-    The **GlorotUniform** distribution is defined as:
-
     .. math::
     .. math::
-        X \sim U(
-            -\sqrt{\frac{scale}{\text{FAN}}},
-             \sqrt{\frac{scale}{\text{FAN}}}
-            )
+        \text{out} \sim \mathcal{U}(-\sqrt{\frac{scale}{\text{fan}}},
+                                     \sqrt{\frac{scale}{\text{fan}}})
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
-    scale : number, optional, default=3
-        The scale factor of distribution.
-    mode : {'FAN_IN', 'FAN_OUT', 'FAN_AVG'}, optional
-        The mode to compute the normalizer.
+        The tensor shape.
+    mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
+        The mode to compute fans.
+    scale : float, optional, default=3.0
+        The scale factor to distribution.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -315,7 +309,7 @@ def glorot_uniform(shape, scale=3, mode='FAN_IN', dtype='float32', **kwargs):
 def ones(shape, dtype='float32', **kwargs):
 def ones(shape, dtype='float32', **kwargs):
     r"""Return a tensor filled with ones.
     r"""Return a tensor filled with ones.
 
 
-    .. math:: y \leftarrow 1
+    .. math:: \text{out} \leftarrow 1
 
 
     ```python
     ```python
     x = dragon.ones(shape=(2, 3), dtype='float32')
     x = dragon.ones(shape=(2, 3), dtype='float32')
@@ -324,7 +318,7 @@ def ones(shape, dtype='float32', **kwargs):
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -338,9 +332,9 @@ def ones(shape, dtype='float32', **kwargs):
 
 
 
 
 def ones_like(other, dtype='float32', **kwargs):
 def ones_like(other, dtype='float32', **kwargs):
-    r"""Return a tensor shaping like another filled with ones.
+    r"""Return a tensor of ones with shape as the other.
 
 
-    .. math:: y \leftarrow 1
+    .. math:: \text{out} \leftarrow 1
 
 
     Examples:
     Examples:
 
 
@@ -384,18 +378,16 @@ def ones_like(other, dtype='float32', **kwargs):
 def random_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
 def random_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
     r"""Return a tensor initialized from the normal distribution.
     r"""Return a tensor initialized from the normal distribution.
 
 
-    The **RandomNormal** distribution is defined as:
-
-    .. math:: X \sim N(\mu, \sigma)
+    .. math:: \text{out} \sim \mathcal{N}(\mu, \sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -424,20 +416,18 @@ def random_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
 
 
 @OpSchema.num_inputs(1)
 @OpSchema.num_inputs(1)
 def random_normal_like(other, mean=0, std=1, dtype='float32', **kwargs):
 def random_normal_like(other, mean=0, std=1, dtype='float32', **kwargs):
-    r"""Return a tensor shaping like another initialized from the normal distribution.
+    r"""Return a tensor initialized from the normal distribution with shape as the other.
 
 
-    The **RandomNormal** distribution is defined as:
-
-    .. math:: X \sim N(\mu, \sigma)
+    .. math:: \text{out} \sim \mathcal{N}(\mu, \sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     other : dragon.Tensor
     other : dragon.Tensor
         The tensor to hint the shape.
         The tensor to hint the shape.
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -472,18 +462,16 @@ def random_normal_like(other, mean=0, std=1, dtype='float32', **kwargs):
 def random_uniform(shape, low=-1, high=1, dtype='float32', **kwargs):
 def random_uniform(shape, low=-1, high=1, dtype='float32', **kwargs):
     r"""Return a tensor initialized from the uniform distribution.
     r"""Return a tensor initialized from the uniform distribution.
 
 
-    The **RandomUniform** distribution is defined as:
-
-    .. math:: X \sim U(\alpha, \beta)
+    .. math:: \text{out} \sim \mathcal{U}(\alpha, \beta)
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     low : number, optional, default=-1
     low : number, optional, default=-1
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     high : number, optional, default=1
     high : number, optional, default=1
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -511,20 +499,18 @@ def random_uniform(shape, low=-1, high=1, dtype='float32', **kwargs):
 
 
 @OpSchema.num_inputs(1)
 @OpSchema.num_inputs(1)
 def random_uniform_like(other, low=-1, high=1, dtype='float32', **kwargs):
 def random_uniform_like(other, low=-1, high=1, dtype='float32', **kwargs):
-    r"""Return a tensor shaping like another initialized from the uniform distribution.
+    r"""Return a tensor initialized from the uniform distribution with shape as the other.
 
 
-    The **RandomUniform** distribution is defined as:
-
-    .. math:: X \sim U(\alpha, \beta)
+    .. math:: \text{out} \sim \mathcal{U}(\alpha, \beta)
 
 
     Parameters
     Parameters
     ----------
     ----------
     other : dragon.Tensor
     other : dragon.Tensor
         The tensor to hint the shape.
         The tensor to hint the shape.
     low : number, optional, default=-1
     low : number, optional, default=-1
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     high : number, optional, default=1
     high : number, optional, default=1
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -558,18 +544,16 @@ def random_uniform_like(other, low=-1, high=1, dtype='float32', **kwargs):
 def truncated_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
 def truncated_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
     r"""Return a tensor initialized from the truncated normal distribution.
     r"""Return a tensor initialized from the truncated normal distribution.
 
 
-    The **TruncatedNormal** distribution is defined as:
-
-    .. math:: X \sim TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+    .. math:: \text{out} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -599,7 +583,7 @@ def truncated_normal(shape, mean=0, std=1, dtype='float32', **kwargs):
 def zeros(shape, dtype='float32', **kwargs):
 def zeros(shape, dtype='float32', **kwargs):
     r"""Return a tensor filled with zeros.
     r"""Return a tensor filled with zeros.
 
 
-    .. math:: y \leftarrow 0
+    .. math:: \text{out} \leftarrow 0
 
 
     ```python
     ```python
     x = dragon.zeros(shape=(2, 3), dtype='float32')
     x = dragon.zeros(shape=(2, 3), dtype='float32')
@@ -608,7 +592,7 @@ def zeros(shape, dtype='float32', **kwargs):
     Parameters
     Parameters
     ----------
     ----------
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
-        The shape of the tensor.
+        The tensor shape.
     dtype : str, optional, default='float32'
     dtype : str, optional, default='float32'
         The optional data type.
         The optional data type.
 
 
@@ -623,9 +607,9 @@ def zeros(shape, dtype='float32', **kwargs):
 
 
 @OpSchema.num_inputs(1)
 @OpSchema.num_inputs(1)
 def zeros_like(other, dtype='float32', **kwargs):
 def zeros_like(other, dtype='float32', **kwargs):
-    r"""Return a tensor shaping like another filled with zeros.
+    r"""Return a tensor of zeros with shape as the other.
 
 
-    .. math:: y \leftarrow 0
+    .. math:: \text{out} \leftarrow 0
 
 
     Examples:
     Examples:
 
 

dragon/python/core/ops/init_ops_lib.py

@@ -90,7 +90,7 @@ class GlorotNormal(Initializer):
     def __init__(self, key, dev, **kwargs):
     def __init__(self, key, dev, **kwargs):
         super(GlorotNormal, self).__init__(key, dev, **kwargs)
         super(GlorotNormal, self).__init__(key, dev, **kwargs)
         self.scale = kwargs.get('scale', 2.)
         self.scale = kwargs.get('scale', 2.)
-        self.mode = kwargs.get('mode', 'FAN_IN')
+        self.mode = kwargs.get('mode', 'fan_in')
 
 
     def attributes(self):
     def attributes(self):
         return {
         return {
@@ -110,7 +110,7 @@ class GlorotUniform(Initializer):
     def __init__(self, key, dev, **kwargs):
     def __init__(self, key, dev, **kwargs):
         super(GlorotUniform, self).__init__(key, dev, **kwargs)
         super(GlorotUniform, self).__init__(key, dev, **kwargs)
         self.scale = kwargs.get('scale', 3.)
         self.scale = kwargs.get('scale', 3.)
-        self.mode = kwargs.get('mode', 'FAN_IN')
+        self.mode = kwargs.get('mode', 'fan_in')
 
 
     def attributes(self):
     def attributes(self):
         return {
         return {

dragon/python/core/ops/math_ops.py

@@ -144,9 +144,9 @@ def axpby(inputs, outputs=None, alpha=1., beta=1., **kwargs):
     outputs : Union[dragon.Tensor, Sequence[dragon.Tensor]], optional
     outputs : Union[dragon.Tensor, Sequence[dragon.Tensor]], optional
         The tensor :math:`y`.
         The tensor :math:`y`.
     alpha : number, optional, default=1.
     alpha : number, optional, default=1.
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     beta : number, optional, default=1.
     beta : number, optional, default=1.
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
 
 
     Returns
     Returns
     -------
     -------
@@ -301,9 +301,9 @@ def clip(inputs, low=None, high=None, **kwargs):
     inputs : dragon.Tensor
     inputs : dragon.Tensor
         The tensor :math:`x`.
         The tensor :math:`x`.
     low : number, optional
     low : number, optional
-        The value of :math:`\text{low}`.
+        The value to :math:`\text{low}`.
     high : number, optional
     high : number, optional
-        The value of :math:`\text{high}`.
+        The value to :math:`\text{high}`.
 
 
     Returns
     Returns
     -------
     -------

dragon/python/core/ops/normalization_ops.py

@@ -58,7 +58,7 @@ def batch_norm(
     momentum : float, optional, default=0.9
     momentum : float, optional, default=0.9
         The momentum for running average.
         The momentum for running average.
     eps : float, optional, default=1e-5
     eps : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
     use_stats : int, optional, default=-1
     use_stats : int, optional, default=-1
         Whether to use estimated statistics or not.
         Whether to use estimated statistics or not.
 
 
@@ -111,7 +111,7 @@ def group_norm(inputs, axis=-1, group=32, eps=1e-5, **kwargs):
     group : int, optional, default=32
     group : int, optional, default=32
         The group size.
         The group size.
     eps : float, optional, default=1e-5
     eps : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
 
 
     Returns
     Returns
     -------
     -------
@@ -156,7 +156,7 @@ def instance_norm(inputs, axis=-1, eps=1e-5, **kwargs):
     axis : int, optional, default=-1
     axis : int, optional, default=-1
         The channel axis.
         The channel axis.
     eps : float, optional, default=1e-5
     eps : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
 
 
     Returns
     Returns
     -------
     -------
@@ -201,7 +201,7 @@ def lp_normalize(inputs, axis=None, p=2, eps=1e-12, reduction='sum', **kwargs):
     axis : Union[int, Sequence[int]], optional
     axis : Union[int, Sequence[int]], optional
         The axis to compute the norm.
         The axis to compute the norm.
     eps : float, optional, default=1e-12
     eps : float, optional, default=1e-12
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
     reduction : {'sum', 'mean'}, optional
     reduction : {'sum', 'mean'}, optional
         The reduction method for norm.
         The reduction method for norm.
 
 
@@ -260,7 +260,7 @@ def layer_norm(inputs, axis=-1, eps=1e-5, **kwargs):
     axis : int, optional, default=-1
     axis : int, optional, default=-1
         The channel axis.
         The channel axis.
     eps : float, optional, default=1e-5
     eps : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
 
 
     Returns
     Returns
     -------
     -------
@@ -369,7 +369,7 @@ def sync_batch_norm(
     momentum : float, optional, default=0.9
     momentum : float, optional, default=0.9
         The momentum for average.
         The momentum for average.
     eps : float, optional, default=1e-5
     eps : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
     use_stats : int, optional, default=-1
     use_stats : int, optional, default=-1
         Whether to use estimated statistics or not.
         Whether to use estimated statistics or not.
     process_group : ProcessGroup, optional
     process_group : ProcessGroup, optional

dragon/python/core/ops/tensorbind_eager.py

@@ -26,9 +26,7 @@ from dragon.core.ops import math_ops_lib
 
 
 
 
 def add(self, other):
 def add(self, other):
-    r"""Compute the element-wise addition.
-
-    .. math:: \text{out} = \text{self} + \text{value}
+    """Compute the element-wise addition.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -40,10 +38,6 @@ def add(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.add(...)`_ : Compute the element-wise addition.
-
     """
     """
     return _binary_op(self, other, 'Add')
     return _binary_op(self, other, 'Add')
 
 
@@ -73,14 +67,14 @@ def astype(self, dtype, inplace=False):
 
 
 
 
 def constant(self, value=0):
 def constant(self, value=0):
-    r"""Fill self with a constant value.
+    r"""Fill self with a scalar value.
 
 
     .. math:: \text{self} \leftarrow \text{value}
     .. math:: \text{self} \leftarrow \text{value}
 
 
     Parameters
     Parameters
     ----------
     ----------
     value : number, optional, default=0
     value : number, optional, default=0
-        The constant value.
+        The value to fill.
 
 
     Returns
     Returns
     -------
     -------
@@ -115,9 +109,7 @@ def copy(self):
 
 
 
 
 def div(self, other):
 def div(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{out} = \text{self} \div \text{value}
+    """Compute the element-wise division.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -129,18 +121,12 @@ def div(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.div(...)`_ : Compute the element-wise division.
-
     """
     """
     return _binary_op(self, other, 'Div')
     return _binary_op(self, other, 'Div')
 
 
 
 
 def ge(self, other):
 def ge(self, other):
-    r"""Compute element-wise greater-equal comparison.
-
-    .. math:: \text{out} = (\text{self} \geq \text{other})
+    """Compute element-wise greater-equal comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -152,35 +138,23 @@ def ge(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.greater_equal(...)`_ : Compute element-wise greater-equal comparison.
-
     """
     """
     return _binary_op(self, other, 'GreaterEqual')
     return _binary_op(self, other, 'GreaterEqual')
 
 
 
 
 def getitem(self, item):
 def getitem(self, item):
-    """Select the elements at the specific indices.
+    """Select elements at the specific index.
 
 
     Parameters
     Parameters
     ----------
     ----------
     item : Union[int, slice, dragon.EagerTensor]
     item : Union[int, slice, dragon.EagerTensor]
-        The indices.
+        The index.
 
 
     Returns
     Returns
     -------
     -------
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.slice(...)`_ : Select the elements according to the given sections.
-
-    See Also
-    --------
-    `dragon.masked_select(...)`_ : Select the elements where the given mask is 1.
-
     """
     """
     if isinstance(item, EagerTensor):
     if isinstance(item, EagerTensor):
         return _masked_select(self, item)
         return _masked_select(self, item)
@@ -189,16 +163,16 @@ def getitem(self, item):
         return _section_select(self, starts, sizes)
         return _section_select(self, starts, sizes)
 
 
 
 
-def glorot_normal(self, mode='FAN_IN', scale=2.):
+def glorot_normal(self, mode='fan_in', scale=2.0):
     r"""Fill self from a glorot normal distribution.
     r"""Fill self from a glorot normal distribution.
 
 
-    .. math:: \text{self} \leftarrow N(0, \sqrt{\frac{scale}{\text{FAN}}})
+    .. math:: \text{self} \sim \mathcal{N}(0, \sqrt{\frac{scale}{\text{fan}}})
 
 
     Parameters
     Parameters
     ----------
     ----------
-    mode : {'FAN_IN, 'FAN_OUT', 'FAN_AVG'}, optional
+    mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
         The mode to compute fans.
         The mode to compute fans.
-    scale : number, optional, default=2.
+    scale : float, optional, default=2.0
         The scale factor of distribution.
         The scale factor of distribution.
 
 
     Returns
     Returns
@@ -217,19 +191,17 @@ def glorot_normal(self, mode='FAN_IN', scale=2.):
         ).apply(shape, out=self)
         ).apply(shape, out=self)
 
 
 
 
-def glorot_uniform(self, mode='FAN_IN', scale=3.):
+def glorot_uniform(self, mode='fan_in', scale=3.0):
     r"""Fill self from a glorot uniform distribution.
     r"""Fill self from a glorot uniform distribution.
 
 
-    .. math:: \text{self} \leftarrow U(
-                -\sqrt{\frac{scale}{\text{FAN}}},
-                \sqrt{\frac{scale}{\text{FAN}}}
-            )
+    .. math:: \text{self} \sim \mathcal{U}(-\sqrt{\frac{scale}{\text{fan}}},
+                                            \sqrt{\frac{scale}{\text{fan}}})
 
 
     Parameters
     Parameters
     ----------
     ----------
-    mode : {'FAN_IN, 'FAN_OUT', 'FAN_AVG'}, optional
+    mode : {'fan_in, 'fan_out', 'fan_avg'}, optional
         The mode to compute fans.
         The mode to compute fans.
-    scale : number, optional, default=3.
+    scale : float, optional, default=3.0
         The scale factor of distribution.
         The scale factor of distribution.
 
 
     Returns
     Returns
@@ -249,9 +221,7 @@ def glorot_uniform(self, mode='FAN_IN', scale=3.):
 
 
 
 
 def gt(self, other):
 def gt(self, other):
-    r"""Compute element-wise greater comparison.
-
-    .. math:: \text{out} = (\text{self} > \text{other})
+    """Compute element-wise greater comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -263,18 +233,12 @@ def gt(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.greater(...)`_ : Compute element-wise greater comparison.
-
     """
     """
     return _binary_op(self, other, 'Greater')
     return _binary_op(self, other, 'Greater')
 
 
 
 
 def iadd(self, other):
 def iadd(self, other):
-    r"""Compute the element-wise addition.
-
-    .. math:: \text{self} \mathrel{+}= \text{other}
+    """Compute the element-wise addition.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -286,18 +250,12 @@ def iadd(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The self.
         The self.
 
 
-    See Also
-    --------
-    `dragon.math.add(...)`_ : Compute the element-wise addition.
-
     """
     """
     return _binary_op(self, other, 'Add', [self])
     return _binary_op(self, other, 'Add', [self])
 
 
 
 
 def idiv(self, other):
 def idiv(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{self} \mathrel{\div}= \text{other}
+    """Compute the element-wise division.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -309,18 +267,12 @@ def idiv(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The self.
         The self.
 
 
-    See Also
-    --------
-    `dragon.math.div(...)`_ : Compute the element-wise division.
-
     """
     """
     return _binary_op(self, other, 'Div', [self])
     return _binary_op(self, other, 'Div', [self])
 
 
 
 
 def imul(self, other):
 def imul(self, other):
-    r"""Compute the element-wise multiplication.
-
-    .. math:: \text{self} \mathrel{\times}= \text{other}
+    """Compute the element-wise multiplication.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -332,18 +284,12 @@ def imul(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The self.
         The self.
 
 
-    See Also
-    --------
-    `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
     """
     """
     return _binary_op(self, other, 'Mul', [self])
     return _binary_op(self, other, 'Mul', [self])
 
 
 
 
 def isub(self, other):
 def isub(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{self} \mathrel{-}= \text{other}
+    """Compute the element-wise subtraction.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -355,18 +301,12 @@ def isub(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The self.
         The self.
 
 
-    See Also
-    --------
-    `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
     """
     """
     return _binary_op(self, other, 'Sub', [self])
     return _binary_op(self, other, 'Sub', [self])
 
 
 
 
 def le(self, other):
 def le(self, other):
-    r"""Compute element-wise less-equal comparison.
-
-    .. math:: \text{out} = (\text{self} \leq \text{other})
+    """Compute element-wise less-equal comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -378,18 +318,12 @@ def le(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.less_equal(...)`_ : Compute element-wise less-equal comparison.
-
     """
     """
     return _binary_op(self, other, 'LessEqual')
     return _binary_op(self, other, 'LessEqual')
 
 
 
 
 def lt(self, other):
 def lt(self, other):
-    r"""Compute element-wise less comparison.
-
-    .. math:: \text{out} = (\text{self} < \text{other})
+    """Compute element-wise less comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -401,18 +335,12 @@ def lt(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.less(...)`_ : Compute element-wise less comparison.
-
     """
     """
     return _binary_op(self, other, 'Less')
     return _binary_op(self, other, 'Less')
 
 
 
 
 def mul(self, other):
 def mul(self, other):
-    r"""Compute the element-wise multiplication.
-
-    .. math:: \text{out} = \text{self} \times \text{other}
+    """Compute the element-wise multiplication.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -424,28 +352,18 @@ def mul(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
     """
     """
     return _binary_op(self, other, 'Mul')
     return _binary_op(self, other, 'Mul')
 
 
 
 
 def neg(self):
 def neg(self):
-    r"""Compute the element-wise negative.
-
-    .. math:: y = -x
+    """Compute the element-wise negative.
 
 
     Returns
     Returns
     -------
     -------
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.negative(...)`_ : Compute the element-wise negative.
-
     """
     """
     return _unary_op(self, 'Neg')
     return _unary_op(self, 'Neg')
 
 
@@ -453,14 +371,14 @@ def neg(self):
 def normal(self, mean=0, std=1):
 def normal(self, mean=0, std=1):
     r"""Fill self from a normal distribution.
     r"""Fill self from a normal distribution.
 
 
-    .. math:: \text{self} \leftarrow N(\mu, \sigma)
+    .. math:: \text{self} \sim \mathcal{N}(\mu, \sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
 
 
     Returns
     Returns
     -------
     -------
@@ -479,9 +397,7 @@ def normal(self, mean=0, std=1):
 
 
 
 
 def radd(self, other):
 def radd(self, other):
-    r"""Compute the element-wise addition.
-
-    .. math:: \text{out} = \text{other} + \text{self}
+    """Compute the element-wise addition.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -493,18 +409,12 @@ def radd(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.add(...)`_ : Compute the element-wise addition.
-
     """
     """
     return _binary_op(other, self, 'Add')
     return _binary_op(other, self, 'Add')
 
 
 
 
 def rdiv(self, other):
 def rdiv(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{out} = \text{value} \div \text{self}
+    """Compute the element-wise division.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -516,10 +426,6 @@ def rdiv(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.div(...)`_ : Compute the element-wise division.
-
     """
     """
     return _binary_op(other, self, 'Div')
     return _binary_op(other, self, 'Div')
 
 
@@ -547,9 +453,7 @@ def reshape(self, shape):
 
 
 
 
 def rmul(self, other):
 def rmul(self, other):
-    r"""Compute the element-wise multiplication.
-
-    .. math:: \text{out} = \text{other} \times \text{self}
+    """Compute the element-wise multiplication.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -561,18 +465,12 @@ def rmul(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
     """
     """
     return _binary_op(other, self, 'Mul')
     return _binary_op(other, self, 'Mul')
 
 
 
 
 def rsub(self, other):
 def rsub(self, other):
-    r"""Compute the element-wise subtraction.
-
-    .. math:: \text{out} = \text{other} - \text{self}
+    """Compute the element-wise subtraction.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -584,31 +482,19 @@ def rsub(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
     """
     """
     return _binary_op(other, self, 'Sub')
     return _binary_op(other, self, 'Sub')
 
 
 
 
 def setitem(self, key, value):
 def setitem(self, key, value):
-    """Set the value at the specific indices.
+    """Set elements at the specific index.
 
 
     Parameters
     Parameters
     ----------
     ----------
     key : Union[int, slice, dragon.EagerTensor]
     key : Union[int, slice, dragon.EagerTensor]
-        The indices.
-    value : number or dragon.EagerTensor
-        The value.
-
-    See Also
-    --------
-    `dragon.assign(...)`_ : Assign the value to ref.
-
-    See Also
-    --------
-    `dragon.masked_assign(...)`_ : Assign the value to ref where mask is 1.
+        The index.
+    value : Union[dragon.EagerTensor, number]
+        The value to set.
 
 
     """
     """
     if isinstance(key, EagerTensor):
     if isinstance(key, EagerTensor):
@@ -619,9 +505,7 @@ def setitem(self, key, value):
 
 
 
 
 def sub(self, other):
 def sub(self, other):
-    r"""Compute the element-wise subtraction.
-
-    .. math:: \text{out} = \text{self} - \text{other}
+    """Compute the element-wise subtraction.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -633,10 +517,6 @@ def sub(self, other):
     dragon.EagerTensor
     dragon.EagerTensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
     """
     """
     return _binary_op(self, other, 'Sub')
     return _binary_op(self, other, 'Sub')
 
 
@@ -644,14 +524,14 @@ def sub(self, other):
 def truncated_normal(self, mean=0, std=1):
 def truncated_normal(self, mean=0, std=1):
     r"""Fill self from a truncated normal distribution.
     r"""Fill self from a truncated normal distribution.
 
 
-    .. math:: \text{self} \leftarrow TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+    .. math:: \text{self} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
 
 
     Returns
     Returns
     -------
     -------
@@ -672,14 +552,14 @@ def truncated_normal(self, mean=0, std=1):
 def uniform(self, low=0, high=1):
 def uniform(self, low=0, high=1):
     r"""Fill self from a uniform distribution.
     r"""Fill self from a uniform distribution.
 
 
-    .. math:: \text{self} \leftarrow U(\alpha, \beta)
+    .. math:: \text{self} \sim \mathcal{U}(\alpha, \beta)
 
 
     Parameters
     Parameters
     ----------
     ----------
     low : number, optional, default=0
     low : number, optional, default=0
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     high : number, optional, default=1
     high : number, optional, default=1
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
 
 
     Returns
     Returns
     -------
     -------
@@ -789,12 +669,10 @@ EagerTensor.reshape = reshape
 EagerTensor.truncated_normal = truncated_normal
 EagerTensor.truncated_normal = truncated_normal
 EagerTensor.uniform = uniform
 EagerTensor.uniform = uniform
 EagerTensor.__add__ = add
 EagerTensor.__add__ = add
-EagerTensor.__div__ = div
 EagerTensor.__ge__ = ge
 EagerTensor.__ge__ = ge
 EagerTensor.__getitem__ = getitem
 EagerTensor.__getitem__ = getitem
 EagerTensor.__gt__ = gt
 EagerTensor.__gt__ = gt
 EagerTensor.__iadd__ = iadd
 EagerTensor.__iadd__ = iadd
-EagerTensor.__idiv__ = idiv
 EagerTensor.__imul__ = imul
 EagerTensor.__imul__ = imul
 EagerTensor.__isub__ = isub
 EagerTensor.__isub__ = isub
 EagerTensor.__itruediv__ = idiv
 EagerTensor.__itruediv__ = idiv
@@ -803,7 +681,6 @@ EagerTensor.__lt__ = lt
 EagerTensor.__mul__ = mul
 EagerTensor.__mul__ = mul
 EagerTensor.__neg__ = neg
 EagerTensor.__neg__ = neg
 EagerTensor.__radd__ = radd
 EagerTensor.__radd__ = radd
-EagerTensor.__rdiv__ = rdiv
 EagerTensor.__rmul__ = rmul
 EagerTensor.__rmul__ = rmul
 EagerTensor.__rsub__ = rsub
 EagerTensor.__rsub__ = rsub
 EagerTensor.__rtruediv__ = rdiv
 EagerTensor.__rtruediv__ = rdiv

dragon/python/core/ops/tensorbind_symbol.py

@@ -24,9 +24,7 @@ from dragon.core.ops import array_ops
 
 
 
 
 def add(self, other):
 def add(self, other):
-    r"""Compute the element-wise addition.
-
-    .. math:: \text{out} = \text{self} + \text{other}
+    """Compute the element-wise addition.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -36,11 +34,7 @@ def add(self, other):
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
-        The **y**.
-
-    See Also
-    --------
-    `dragon.math.add(...)`_ : Compute the element-wise addition.
+        The output tensor.
 
 
     """
     """
     return _binary_op(self, other, 'Add')
     return _binary_op(self, other, 'Add')
@@ -90,9 +84,7 @@ def copy(self):
 
 
 
 
 def div(self, other):
 def div(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{out} = \text{self} \div \text{other}
+    """Compute the element-wise division.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -104,18 +96,12 @@ def div(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.div(...)`_ : Compute the element-wise division.
-
     """
     """
     return _binary_op(self, other, 'Div')
     return _binary_op(self, other, 'Div')
 
 
 
 
 def ge(self, other):
 def ge(self, other):
-    r"""Compute element-wise greater-equal comparison.
-
-    .. math:: \text{out} = (\text{self} \geq \text{other})
+    """Compute element-wise greater-equal comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -127,35 +113,23 @@ def ge(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.greater_equal(...)`_ : Compute element-wise greater-equal comparison.
-
     """
     """
     return _binary_op(self, other, 'GreaterEqual')
     return _binary_op(self, other, 'GreaterEqual')
 
 
 
 
 def getitem(self, item):
 def getitem(self, item):
-    """Select the elements at the specific indices.
+    """Select elements at the specific index.
 
 
     Parameters
     Parameters
     ----------
     ----------
     item : Union[int, slice, dragon.Tensor]
     item : Union[int, slice, dragon.Tensor]
-        The indices.
+        The index.
 
 
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.slice(...)`_ : Select the elements according to the given sections.
-
-    See Also
-    --------
-    `dragon.masked_select(...)`_ : Select the elements where the given mask is 1.
-
     """
     """
     if isinstance(item, Tensor):
     if isinstance(item, Tensor):
         return _masked_select(self, item)
         return _masked_select(self, item)
@@ -165,21 +139,19 @@ def getitem(self, item):
 
 
 
 
 def get_value(self):
 def get_value(self):
-    """Copy the data from storage.
+    """Return the value of implementation.
 
 
     Returns
     Returns
     -------
     -------
     numpy.ndarray
     numpy.ndarray
-        The deep copied value.
+        The deep-copied value.
 
 
     """
     """
     return workspace.get_workspace().fetch_tensor(self)
     return workspace.get_workspace().fetch_tensor(self)
 
 
 
 
 def gt(self, other):
 def gt(self, other):
-    r"""Compute element-wise greater comparison.
-
-    .. math:: \text{out} = (\text{self} > \text{other})
+    """Compute element-wise greater comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -191,18 +163,12 @@ def gt(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.greater(...)`_ : Compute element-wise greater comparison.
-
     """
     """
     return _binary_op(self, other, 'Greater')
     return _binary_op(self, other, 'Greater')
 
 
 
 
 def le(self, other):
 def le(self, other):
-    r"""Compute element-wise less-equal comparison.
-
-    .. math:: \text{out} = (\text{self} \leq \text{other})
+    """Compute element-wise less-equal comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -214,18 +180,12 @@ def le(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.less_equal(...)`_ : Compute element-wise less-equal comparison.
-
     """
     """
     return _binary_op(self, other, 'LessEqual')
     return _binary_op(self, other, 'LessEqual')
 
 
 
 
 def lt(self, other):
 def lt(self, other):
-    r"""Compute element-wise less comparison.
-
-    .. math:: \text{out} = (\text{self} < \text{other})
+    """Compute element-wise less comparison.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -237,18 +197,12 @@ def lt(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.less(...)`_ : Compute element-wise less comparison.
-
     """
     """
     return _binary_op(self, other, 'Less')
     return _binary_op(self, other, 'Less')
 
 
 
 
 def mul(self, other):
 def mul(self, other):
-    r"""Compute the element-wise multiplication.
-
-    .. math:: \text{out} = \text{self} \times \text{other}
+    """Compute the element-wise multiplication.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -260,36 +214,24 @@ def mul(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
     """
     """
     return _binary_op(self, other, 'Mul')
     return _binary_op(self, other, 'Mul')
 
 
 
 
 def neg(self):
 def neg(self):
-    r"""Compute the element-wise negative.
-
-    .. math:: y = -x
+    """Compute the element-wise negative.
 
 
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.negative(...)`_ : Compute the element-wise negative.
-
     """
     """
     return _unary_op(self, 'Neg')
     return _unary_op(self, 'Neg')
 
 
 
 
 def radd(self, other):
 def radd(self, other):
-    r"""Compute the element-wise addition.
-
-    .. math:: \text{out} = \text{other} + \text{self}
+    """Compute the element-wise addition.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -301,18 +243,12 @@ def radd(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.add(...)`_ : Compute the element-wise addition.
-
     """
     """
     return _binary_op(other, self, 'Add')
     return _binary_op(other, self, 'Add')
 
 
 
 
 def rdiv(self, other):
 def rdiv(self, other):
-    r"""Compute the element-wise division.
-
-    .. math:: \text{out} = \text{other} \div \text{self}
+    """Compute the element-wise division.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -324,10 +260,6 @@ def rdiv(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.div(...)`_ : Compute the element-wise division.
-
     """
     """
     return _binary_op(other, self, 'Div')
     return _binary_op(other, self, 'Div')
 
 
@@ -355,9 +287,7 @@ def reshape(self, shape):
 
 
 
 
 def rmul(self, other):
 def rmul(self, other):
-    r"""Compute the element-wise multiplication.
-
-    .. math:: \text{out} = \text{other} \times \text{self}
+    """Compute the element-wise multiplication.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -369,18 +299,12 @@ def rmul(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.mul(...)`_ : Compute the element-wise multiplication.
-
     """
     """
     return _binary_op(other, self, 'Mul')
     return _binary_op(other, self, 'Mul')
 
 
 
 
 def rsub(self, other):
 def rsub(self, other):
-    r"""Compute the element-wise subtraction.
-
-    .. math:: \text{out} = \text{other} - \text{self}
+    """Compute the element-wise subtraction.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -392,31 +316,19 @@ def rsub(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
     """
     """
     return _binary_op(other, self, 'Sub')
     return _binary_op(other, self, 'Sub')
 
 
 
 
 def setitem(self, key, value):
 def setitem(self, key, value):
-    """Set the value at the specific indices.
+    """Set elements at the specific index.
 
 
     Parameters
     Parameters
     ----------
     ----------
     key : Union[int, slice, dragon.Tensor]
     key : Union[int, slice, dragon.Tensor]
-        The indices.
-    value : number or dragon.Tensor
-        The value.
-
-    See Also
-    --------
-    `dragon.assign(...)`_ : Assign the value to ref.
-
-    See Also
-    --------
-    `dragon.masked_assign(...)`_ : Assign the value to ref where mask is 1.
+        The index.
+    value : Union[dragon.Tensor, number]
+        The value to set.
 
 
     """
     """
     if isinstance(key, Tensor):
     if isinstance(key, Tensor):
@@ -427,12 +339,12 @@ def setitem(self, key, value):
 
 
 
 
 def set_value(self, value):
 def set_value(self, value):
-    """Feed the const value to storage.
+    """Set value to the implementation.
 
 
     Parameters
     Parameters
     ----------
     ----------
     value : array_like
     value : array_like
-        The const value.
+        The value to set.
 
 
     Returns
     Returns
     -------
     -------
@@ -445,9 +357,7 @@ def set_value(self, value):
 
 
 
 
 def sub(self, other):
 def sub(self, other):
-    r"""Compute the element-wise subtraction.
-
-    .. math:: \text{out} = \text{self} - \text{value}
+    """Compute the element-wise subtraction.
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -459,10 +369,6 @@ def sub(self, other):
     dragon.Tensor
     dragon.Tensor
         The output tensor.
         The output tensor.
 
 
-    See Also
-    --------
-    `dragon.math.sub(...)`_ : Compute the element-wise subtraction.
-
     """
     """
     return _binary_op(self, other, 'Sub')
     return _binary_op(self, other, 'Sub')
 
 
@@ -547,7 +453,6 @@ Tensor.get_value = get_value
 Tensor.reshape = reshape
 Tensor.reshape = reshape
 Tensor.set_value = set_value
 Tensor.set_value = set_value
 Tensor.__add__ = add
 Tensor.__add__ = add
-Tensor.__div__ = div
 Tensor.__ge__ = ge
 Tensor.__ge__ = ge
 Tensor.__getitem__ = getitem
 Tensor.__getitem__ = getitem
 Tensor.__gt__ = gt
 Tensor.__gt__ = gt
@@ -556,7 +461,6 @@ Tensor.__lt__ = lt
 Tensor.__mul__ = mul
 Tensor.__mul__ = mul
 Tensor.__neg__ = neg
 Tensor.__neg__ = neg
 Tensor.__radd__ = radd
 Tensor.__radd__ = radd
-Tensor.__rdiv__ = rdiv
 Tensor.__rmul__ = rmul
 Tensor.__rmul__ = rmul
 Tensor.__rtruediv__ = rdiv
 Tensor.__rtruediv__ = rdiv
 Tensor.__rsub__ = rsub
 Tensor.__rsub__ = rsub

tensorflow/core/framework/constant_op.py

@@ -23,7 +23,7 @@ from dragon.core.framework import workspace
 
 
 
 
 def constant(value, dtype=None, shape=None, name='Const'):
 def constant(value, dtype=None, shape=None, name='Const'):
-    r"""Return a tensor taking the value content.
+    """Return a tensor initialized from the value.
 
 
     Examples:
     Examples:
 
 
@@ -47,7 +47,7 @@ def constant(value, dtype=None, shape=None, name='Const'):
     Returns
     Returns
     -------
     -------
     dragon.Tensor
     dragon.Tensor
-        The constant tensor.
+        The output tensor.
 
 
     """
     """
     if dtype is not None:
     if dtype is not None:
@@ -82,7 +82,7 @@ def constant(value, dtype=None, shape=None, name='Const'):
     else:
     else:
         return TensorRef(
         return TensorRef(
             name=workspace.get_workspace().unique_name(
             name=workspace.get_workspace().unique_name(
-                name, ':0', 'dragon.Tensor'),
+                name, ':0', 'Tensor'),
             shape=list(value.shape),
             shape=list(value.shape),
             dtype=str(value.dtype),
             dtype=str(value.dtype),
         ).set_value(value)
         ).set_value(value)

tensorflow/core/keras/activations.py

@@ -47,7 +47,7 @@ def elu(x, alpha=1., **kwargs):
     x : dragon.Tensor
     x : dragon.Tensor
         The tensor :math:`x`.
         The tensor :math:`x`.
     alpha : float, optional, default=1.
     alpha : float, optional, default=1.
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
 
 
     Returns
     Returns
     -------
     -------
@@ -141,7 +141,7 @@ def relu(x, alpha=0, max_value=None, **kwargs):
     alpha : number, optional, default=0
     alpha : number, optional, default=0
         The valve of :math:`\alpha`.
         The valve of :math:`\alpha`.
     max_value : number, optional
     max_value : number, optional
-        The value of :math:`v_{max}`.
+        The value to :math:`v_{max}`.
 
 
     """
     """
     if max_value is not None:
     if max_value is not None:

tensorflow/core/keras/engine/input_layer.py

@@ -43,7 +43,7 @@ def Input(
     x = tf.keras.Input(shape=(8,), batch_size=8, dtype='float32')
     x = tf.keras.Input(shape=(8,), batch_size=8, dtype='float32')
 
 
     # Create a placeholder aliasing an existing tensor
     # Create a placeholder aliasing an existing tensor
-    x = dragon.Tensor('x', shape=(8,), dtype='float32').variable()
+    x = dragon.Tensor('x', shape=(8,), dtype='float32').constant()
     xx = tf.keras.Input(tensor=x)
     xx = tf.keras.Input(tensor=x)
     ```
     ```
 
 

tensorflow/core/keras/layers/advanced_activations.py

@@ -49,7 +49,7 @@ class ELU(Layer):
         Parameters
         Parameters
         ----------
         ----------
         alpha : float, optional, default=0.3
         alpha : float, optional, default=0.3
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
 
 
         """
         """
         super(ELU, self).__init__(**kwargs)
         super(ELU, self).__init__(**kwargs)
@@ -92,7 +92,7 @@ class LeakyReLU(Layer):
         Parameters
         Parameters
         ----------
         ----------
         alpha : float, optional, default=0.3
         alpha : float, optional, default=0.3
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
 
 
         """
         """
         super(LeakyReLU, self).__init__(**kwargs)
         super(LeakyReLU, self).__init__(**kwargs)
@@ -135,9 +135,9 @@ class ReLU(Layer):
         Parameters
         Parameters
         ----------
         ----------
         max_value : number, optional
         max_value : number, optional
-            The value of :math:`v_{max}`.
+            The value to :math:`v_{max}`.
         negative_slope : float, optional, default=0.
         negative_slope : float, optional, default=0.
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
 
 
         """
         """
         super(ReLU, self).__init__(**kwargs)
         super(ReLU, self).__init__(**kwargs)

tensorflow/core/keras/losses.py

@@ -26,6 +26,16 @@ class Loss(object):
     """The base class for loss criterion."""
     """The base class for loss criterion."""
 
 
     def __init__(self, reduction=losses_utils.Reduction.MEAN, name=None):
     def __init__(self, reduction=losses_utils.Reduction.MEAN, name=None):
+        """Create a ``Loss`` criterion.
+
+        Parameters
+        ----------
+        reduction : {'none', 'sum', 'mean', 'valid'}, optional
+            The reduction method.
+        name : str, optional
+            A optional name for the operation.
+
+        """
         losses_utils.Reduction.validate(reduction)
         losses_utils.Reduction.validate(reduction)
         self.reduction = reduction
         self.reduction = reduction
         self.name = name
         self.name = name

tensorflow/core/keras/optimizer/adam.py

@@ -21,7 +21,7 @@ from dragon.vm.tensorflow.core.keras.optimizer import optimizer
 
 
 
 
 class Adam(optimizer.Optimizer):
 class Adam(optimizer.Optimizer):
-    r"""The optimizer which implements Adam algorithm.
+    r"""The optimizer to apply Adam algorithm.
     `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
     `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
 
 
     The **Adam** update is defined as:
     The **Adam** update is defined as:

tensorflow/core/keras/optimizer/gradient_descent.py

@@ -21,7 +21,7 @@ from dragon.vm.tensorflow.core.keras.optimizer import optimizer
 
 
 
 
 class SGD(optimizer.Optimizer):
 class SGD(optimizer.Optimizer):
-    r"""The optimizer which implements SGD algorithm.
+    r"""The optimizer to apply SGD algorithm.
 
 
     Following SGD algorithms are supported:
     Following SGD algorithms are supported:
 
 

tensorflow/core/keras/optimizer/rmsprop.py

@@ -17,7 +17,7 @@ from dragon.vm.tensorflow.core.keras.optimizer import optimizer
 
 
 
 
 class RMSprop(optimizer.Optimizer):
 class RMSprop(optimizer.Optimizer):
-    r"""The optimizer which implements RMSprop algorithm.
+    r"""The optimizer to apply RMSprop algorithm.
     `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
     `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
 
 
     The **RMSprop** update is defined as:
     The **RMSprop** update is defined as:

tensorflow/core/keras/regularizers.py

@@ -56,9 +56,9 @@ class L1L2(Regularizer):
         Parameters
         Parameters
         ----------
         ----------
         l1 : float, optional, default=0.01
         l1 : float, optional, default=0.01
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         l2 : float, optional, default=0.01
         l2 : float, optional, default=0.01
-            The value of :math:`\beta`.
+            The value to :math:`\beta`.
 
 
         """
         """
         if l1 <= 0. or l2 <= 0.:
         if l1 <= 0. or l2 <= 0.:
@@ -90,7 +90,7 @@ def l1(l=0.01):
     Parameters
     Parameters
     ----------
     ----------
     l : float, optional, default=0.01
     l : float, optional, default=0.01
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
 
 
     Returns
     Returns
     -------
     -------
@@ -111,9 +111,9 @@ def l1_l2(l1=0.01, l2=0.01):
     Parameters
     Parameters
     ----------
     ----------
     l1 : float, optional, default=0.01
     l1 : float, optional, default=0.01
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     l2 : float, optional, default=0.01
     l2 : float, optional, default=0.01
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
 
 
     Returns
     Returns
     -------
     -------
@@ -134,7 +134,7 @@ def l2(l=0.01):
     Parameters
     Parameters
     ----------
     ----------
     l : float, optional, default=0.01
     l : float, optional, default=0.01
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
 
 
     Returns
     Returns
     -------
     -------

tensorflow/core/keras/utils/losses_utils.py

@@ -19,5 +19,4 @@ from __future__ import print_function
 
 
 from dragon.vm.tensorflow.core.ops.losses import loss_reduction
 from dragon.vm.tensorflow.core.ops.losses import loss_reduction
 
 
-
 Reduction = loss_reduction.Reduction
 Reduction = loss_reduction.Reduction

tensorflow/core/ops/array_ops.py

@@ -183,9 +183,9 @@ def expand_dims(input, axis, name=None):
 
 
 
 
 def fill(dims, value=0, dtype=None, name=None):
 def fill(dims, value=0, dtype=None, name=None):
-    r"""Return a tensor filled with the specific value.
+    r"""Return a tensor filled with the scalar value.
 
 
-    .. math:: y \leftarrow \text{Constant}
+    .. math:: \text{out} \leftarrow \text{value}
 
 
     Examples:
     Examples:
 
 
@@ -298,7 +298,7 @@ def identity(input, name=None):
 def ones(shape, dtype='float32', name=None):
 def ones(shape, dtype='float32', name=None):
     r"""Return a tensor filled with ones.
     r"""Return a tensor filled with ones.
 
 
-    .. math:: y \leftarrow 1
+    .. math:: \text{out} \leftarrow 1
 
 
     ```python
     ```python
     x = tf.ones(shape=(2, 3), dtype=tf.float32)
     x = tf.ones(shape=(2, 3), dtype=tf.float32)
@@ -318,9 +318,9 @@ def ones(shape, dtype='float32', name=None):
 
 
 
 
 def ones_like(input, dtype='float32', name=None):
 def ones_like(input, dtype='float32', name=None):
-    r"""Return a tensor shaping like another filled with ones.
+    r"""Return a tensor of ones with shape as the other.
 
 
-    .. math:: y \leftarrow 1
+    .. math:: \text{out} \leftarrow 1
 
 
     Examples:
     Examples:
 
 
@@ -349,13 +349,13 @@ def one_hot(
     off_value=0,
     off_value=0,
     name=None,
     name=None,
 ):
 ):
-    r"""Return the one-hot representation from indices.
+    r"""Return the one-hot representation for input.
 
 
     .. math::
     .. math::
-        y[i][j] =
+        \text{out}[i][j] =
             \begin{cases}
             \begin{cases}
-            \text{Val}_{off}, & \text{ if } \text{indices}[i] \neq j \\
-            \text{Val}_{on}, & \text{ otherwise }
+                \text{off\_value}, & \text{ if } \text{input}[i] \neq j \\
+                \text{on\_value}, & \text{ otherwise }
             \end{cases}
             \end{cases}
 
 
     The max value of indices, i.e., the ``depth`` should be specified:
     The max value of indices, i.e., the ``depth`` should be specified:
@@ -374,7 +374,7 @@ def one_hot(
     Parameters
     Parameters
     ----------
     ----------
     indices : dragon.Tensor
     indices : dragon.Tensor
-        The tensor ``indices``.
+        The input tensor.
     depth : int
     depth : int
         The depth of representation.
         The depth of representation.
     on_value : int, optional, default=1
     on_value : int, optional, default=1
@@ -390,7 +390,13 @@ def one_hot(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    return array_ops.one_hot(indices, depth, on_value, off_value, name=name)
+    return array_ops.one_hot(
+        indices,
+        depth=depth,
+        on_value=on_value,
+        off_value=off_value,
+        name=name,
+    )
 
 
 
 
 def pad(
 def pad(
@@ -483,7 +489,7 @@ def placeholder(dtype=None, shape=None, name=None):
             suffix=':0', namespace='Tensor'),
             suffix=':0', namespace='Tensor'),
         dtype=str(dtype) if dtype else dtype,
         dtype=str(dtype) if dtype else dtype,
         shape=shape,
         shape=shape,
-    ).placeholder()
+    ).constant()
 
 
 
 
 def reshape(tensor, shape, name=None):
 def reshape(tensor, shape, name=None):
@@ -752,7 +758,7 @@ def transpose(a, perm=None, name=None):
 def zeros(shape, dtype='float32', name=None):
 def zeros(shape, dtype='float32', name=None):
     r"""Return a tensor filled with zeros.
     r"""Return a tensor filled with zeros.
 
 
-    .. math:: y \leftarrow 0
+    .. math:: \text{out} \leftarrow 0
 
 
     ```python
     ```python
     x = tf.zeros(shape=(2, 3), dtype=tf.float32)
     x = tf.zeros(shape=(2, 3), dtype=tf.float32)
@@ -772,9 +778,9 @@ def zeros(shape, dtype='float32', name=None):
 
 
 
 
 def zeros_like(input, dtype='float32', name=None):
 def zeros_like(input, dtype='float32', name=None):
-    r"""Return a tensor shaping like another filled with zeros.
+    r"""Return a tensor of zeros with shape as the other.
 
 
-    .. math:: y \leftarrow 0
+    .. math:: \text{out} \leftarrow 0
 
 
     Examples:
     Examples:
 
 

tensorflow/core/ops/clip_ops.py

@@ -42,9 +42,9 @@ def clip_by_value(
     t : dragon.Tensor
     t : dragon.Tensor
         The tensor :math:`x`.
         The tensor :math:`x`.
     clip_value_min : number, optional
     clip_value_min : number, optional
-        The value of :math:`\text{low}`.
+        The value to :math:`\text{low}`.
     clip_value_max : number, optional
     clip_value_max : number, optional
-        The value of :math:`\text{high}`.
+        The value to :math:`\text{high}`.
     name : str, optional
     name : str, optional
         A optional name for the operation.
         A optional name for the operation.
 
 

tensorflow/core/ops/init_ops.py

@@ -43,7 +43,11 @@ class Initializer(object):
 
 
 
 
 class Constant(Initializer):
 class Constant(Initializer):
-    """Fill tensors with a scalar."""
+    r"""Fill tensor with a scalar value.
+
+    .. math:: \text{tensor} \leftarrow \text{value}
+
+    """
 
 
     def __init__(self, value=0, dtype='float32'):
     def __init__(self, value=0, dtype='float32'):
         """Create a ``Constant`` initializer.
         """Create a ``Constant`` initializer.
@@ -78,19 +82,27 @@ class Constant(Initializer):
         return init_ops.fill(shape, value=self.value, dtype=dtype)
         return init_ops.fill(shape, value=self.value, dtype=dtype)
 
 
 
 
-class Ones(Initializer):
-    """Fill tensors with ones."""
+class RandomNormal(Initializer):
+    r"""Fill tensor from a normal distribution.
 
 
-    def __init__(self, dtype='float32'):
-        """Create a ``Ones`` initializer.
+    .. math:: \text{tensor} \sim \mathcal{N}(\mu, \sigma)
+
+    """
+
+    def __init__(self, mean=0, stddev=1, dtype='float32'):
+        r"""Create a ``RandomNormal`` initializer.
 
 
         Parameters
         Parameters
         ----------
         ----------
+        mean : number, optional, default=0
+            The value to :math:`\mu`.
+        stddev : number, optional, default=1
+            The value to :math:`\sigma`.
         dtype : str, optional, default='float32'
         dtype : str, optional, default='float32'
             The data type to set as default.
             The data type to set as default.
 
 
         """
         """
-        self.dtype = dtype
+        self.mean, self.stddev, self.dtype = mean, stddev, dtype
 
 
     def __call__(self, shape, dtype=None, **kwargs):
     def __call__(self, shape, dtype=None, **kwargs):
         """Return a tensor initialized from the initializer.
         """Return a tensor initialized from the initializer.
@@ -108,22 +120,30 @@ class Ones(Initializer):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        dtype = str(self.dtype) if dtype is None else str(dtype)
-        return init_ops.fill(shape, value=1, dtype=str(dtype))
+        return init_ops.random_normal(
+            shape=shape,
+            mean=self.mean,
+            std=self.stddev,
+            dtype=str(self.dtype) if dtype is None else str(dtype),
+        )
 
 
 
 
 class RandomUniform(Initializer):
 class RandomUniform(Initializer):
-    """Fill tensors according to a uniform distribution."""
+    r"""Fill tensor from an uniform distribution.
+
+    .. math:: \text{tensor} \sim \mathcal{U}(\alpha, \beta)
+
+    """
 
 
     def __init__(self, minval=0, maxval=1, dtype='float32'):
     def __init__(self, minval=0, maxval=1, dtype='float32'):
-        """Create a ``RandomUniform`` initializer.
+        r"""Create a ``RandomUniform`` initializer.
 
 
         Parameters
         Parameters
         ----------
         ----------
         minval : number, optional, default=0
         minval : number, optional, default=0
-            The lower bound of distribution.
+            The value to :math:`\alpha`.
         maxval : number, optional, default=1
         maxval : number, optional, default=1
-            The higher bound of distribution.
+            The value to :math:`\beta`.
         dtype : str, optional, default='float32'
         dtype : str, optional, default='float32'
             The data type to set as default.
             The data type to set as default.
 
 
@@ -155,67 +175,22 @@ class RandomUniform(Initializer):
         )
         )
 
 
 
 
-class RandomNormal(Initializer):
-    """Fill tensors according to a normal distribution."""
-
-    def __init__(self, mean=0, stddev=1, dtype='float32'):
-        """Create a ``RandomNormal`` initializer.
-
-        Parameters
-        ----------
-        mean : number, optional, default=0
-            The mean of distribution.
-        stddev : number, optional, default=1
-            The stddev of distribution.
-        dtype : str, optional, default='float32'
-            The data type to set as default.
-
-        """
-        self.mean, self.stddev, self.dtype = mean, stddev, dtype
-
-    def __call__(self, shape, dtype=None, **kwargs):
-        """Return a tensor initialized from the initializer.
-
-        Parameters
-        ----------
-        shape : Sequence[int]
-            The tensor shape.
-        dtype : str, optional
-            The optional data type.
-
-        Returns
-        -------
-        dragon.Tensor
-            The output tensor.
-
-        """
-        return init_ops.random_normal(
-            shape=shape,
-            mean=self.mean,
-            std=self.stddev,
-            dtype=str(self.dtype) if dtype is None else str(dtype),
-        )
-
-
 class TruncatedNormal(Initializer):
 class TruncatedNormal(Initializer):
-    r"""Fill tensors according to a truncated normal distribution.
+    r"""Fill tensor from a truncated normal distribution.
 
 
-    The **TruncatedNormal** distribution is defined as:
-
-    .. math::
-        X \sim TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+    .. math:: \text{tensor} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
     """
     """
 
 
     def __init__(self, mean=0, stddev=1, dtype='float32'):
     def __init__(self, mean=0, stddev=1, dtype='float32'):
-        """Create a ``TruncatedNormal`` initializer.
+        r"""Create a ``TruncatedNormal`` initializer.
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The mean of distribution.
+            The value to :math:`\mu`.
         stddev : number, optional, default=1
         stddev : number, optional, default=1
-            The stddev of distribution.
+            The value to :math:`\sigma`.
         dtype : str, optional, default='float32'
         dtype : str, optional, default='float32'
             The data type to set as default.
             The data type to set as default.
 
 
@@ -247,11 +222,11 @@ class TruncatedNormal(Initializer):
 
 
 
 
 class VarianceScaling(Initializer):
 class VarianceScaling(Initializer):
-    """Fill tensors with the random values adapting to shape."""
+    """Fill tensor from a scaled random distribution."""
 
 
     def __init__(
     def __init__(
         self,
         self,
-        scale=1.,
+        scale=1.0,
         mode='fan_in',
         mode='fan_in',
         distribution='normal',
         distribution='normal',
         dtype='float32',
         dtype='float32',
@@ -260,8 +235,8 @@ class VarianceScaling(Initializer):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        scale : float, optional, default=1.
-            The scale factor applied to distribution.
+        scale : float, optional, default=1
+            The scale factor to distribution.
         mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
         mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
             The mode for adapting to shape.
             The mode for adapting to shape.
         distribution : {'normal', 'uniform'}, optional
         distribution : {'normal', 'uniform'}, optional
@@ -271,15 +246,13 @@ class VarianceScaling(Initializer):
 
 
         """
         """
         if scale <= 0.:
         if scale <= 0.:
-            raise ValueError("`scale` must be positive float.")
-
-        if mode not in {"fan_in", "fan_out", "fan_avg"}:
-            raise ValueError("Invalid `mode` argument:", mode)
-
+            raise ValueError('<scale> must be positive float.')
+        mode = mode.lower()
+        if mode not in {'fan_in', 'fan_out', 'fan_avg'}:
+            raise ValueError('Invalid <mode> argument:', mode)
         distribution = distribution.lower()
         distribution = distribution.lower()
-        if distribution not in {"normal", "uniform"}:
+        if distribution not in {'normal', 'uniform'}:
             raise ValueError("Invalid `distribution` argument:", distribution)
             raise ValueError("Invalid `distribution` argument:", distribution)
-
         self.scale = scale
         self.scale = scale
         self.mode = mode
         self.mode = mode
         self.distribution = distribution
         self.distribution = distribution
@@ -304,21 +277,25 @@ class VarianceScaling(Initializer):
         if self.distribution == 'normal':
         if self.distribution == 'normal':
             return init_ops.glorot_normal(
             return init_ops.glorot_normal(
                 shape=shape,
                 shape=shape,
-                scale=self.scale * 2.,
                 mode=self.mode,
                 mode=self.mode,
+                scale=self.scale * 2.0,
                 dtype=str(self.dtype) if dtype is None else str(dtype)
                 dtype=str(self.dtype) if dtype is None else str(dtype)
             )
             )
         else:
         else:
             return init_ops.glorot_uniform(
             return init_ops.glorot_uniform(
                 shape=shape,
                 shape=shape,
-                scale=self.scale * 3.,
                 mode=self.mode,
                 mode=self.mode,
+                scale=self.scale * 3.0,
                 dtype=str(self.dtype) if dtype is None else str(dtype)
                 dtype=str(self.dtype) if dtype is None else str(dtype)
             )
             )
 
 
 
 
 class GlorotNormal(VarianceScaling):
 class GlorotNormal(VarianceScaling):
-    """Fill tensors according to a glorot normal distribution."""
+    r"""Fill tensor from a glorot normal distribution.
+
+    .. math:: \text{tensor} \sim \mathcal{N}(0, \sqrt{\frac{2}{\text{fan\_avg}}})
+
+    """
 
 
     def __init__(self, dtype='float32'):
     def __init__(self, dtype='float32'):
         """Create a ``GlorotNormal`` initializer.
         """Create a ``GlorotNormal`` initializer.
@@ -330,7 +307,7 @@ class GlorotNormal(VarianceScaling):
 
 
         """
         """
         super(GlorotNormal, self).__init__(
         super(GlorotNormal, self).__init__(
-            scale=1.,
+            scale=1.0,
             mode='fan_avg',
             mode='fan_avg',
             distribution='normal',
             distribution='normal',
             dtype=dtype,
             dtype=dtype,
@@ -338,7 +315,12 @@ class GlorotNormal(VarianceScaling):
 
 
 
 
 class GlorotUniform(VarianceScaling):
 class GlorotUniform(VarianceScaling):
-    """Fill tensors according to a glorot uniform distribution."""
+    r"""Fill tensor from a glorot uniform distribution.
+
+    .. math:: \text{tensor} \sim \mathcal{U}(-\sqrt{\frac{3}{\text{fan\_avg}}},
+                                              \sqrt{\frac{3}{\text{fan\_avg}}})
+
+    """
 
 
     def __init__(self, dtype='float32'):
     def __init__(self, dtype='float32'):
         """Create a ``GlorotUniform`` initializer.
         """Create a ``GlorotUniform`` initializer.
@@ -357,8 +339,50 @@ class GlorotUniform(VarianceScaling):
         )
         )
 
 
 
 
+class Ones(Initializer):
+    r"""Fill tensor with ones.
+
+    .. math:: \text{tensor} \leftarrow 1
+
+    """
+
+    def __init__(self, dtype='float32'):
+        """Create a ``Ones`` initializer.
+
+        Parameters
+        ----------
+        dtype : str, optional, default='float32'
+            The data type to set as default.
+
+        """
+        self.dtype = dtype
+
+    def __call__(self, shape, dtype=None, **kwargs):
+        """Return a tensor initialized from the initializer.
+
+        Parameters
+        ----------
+        shape : Sequence[int]
+            The tensor shape.
+        dtype : str, optional
+            The optional data type.
+
+        Returns
+        -------
+        dragon.Tensor
+            The output tensor.
+
+        """
+        dtype = str(self.dtype) if dtype is None else str(dtype)
+        return init_ops.fill(shape, value=1, dtype=str(dtype))
+
+
 class Zeros(Initializer):
 class Zeros(Initializer):
-    """Fill tensors with zeros."""
+    r"""Fill tensor with zeros.
+
+    .. math:: \text{tensor} \leftarrow 0
+
+    """
 
 
     def __init__(self, dtype='float32'):
     def __init__(self, dtype='float32'):
         """Create a ``Zeros`` initializer.
         """Create a ``Zeros`` initializer.

tensorflow/core/ops/math_ops.py

@@ -846,7 +846,7 @@ def pow(x, y, name=None):
 
 
 
 
 def range(start, limit=None, delta=1, dtype='int64', name=None):
 def range(start, limit=None, delta=1, dtype='int64', name=None):
-    r"""Return a tensor with evenly spaced values within a interval.
+    r"""Return a tensor of evenly spaced values within a interval.
 
 
     Specify ``start`` and ``limit`` to determine an interval:
     Specify ``start`` and ``limit`` to determine an interval:
 
 

tensorflow/core/ops/nn_impl.py

@@ -116,7 +116,7 @@ def l2_normalize(x, axis=None, epsilon=1e-12, name=None):
     axis : Union[int, Sequence[int]], optional
     axis : Union[int, Sequence[int]], optional
         The axis to compute norm.
         The axis to compute norm.
     epsilon : float, optional, default=1e-5
     epsilon : float, optional, default=1e-5
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
     name : str, optional
     name : str, optional
         A optional name for the operation.
         A optional name for the operation.
 
 

tensorflow/core/ops/nn_ops.py

@@ -493,7 +493,7 @@ def elu(features, alpha=1., name=None, **kwargs):
     features : dragon.Tensor
     features : dragon.Tensor
         The tensor :math:`x`.
         The tensor :math:`x`.
     alpha : float, optional, default=1.
     alpha : float, optional, default=1.
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     name : str, optional
     name : str, optional
         A optional name for the operation.
         A optional name for the operation.
 
 
@@ -527,7 +527,7 @@ def leaky_relu(features, alpha=0.2, name=None, **kwargs):
     features : dragon.Tensor
     features : dragon.Tensor
         The input tensor.
         The input tensor.
     alpha : number, optional, default=0.2
     alpha : number, optional, default=0.2
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     name : str, optional
     name : str, optional
         A optional name for the operation.
         A optional name for the operation.
 
 

tensorflow/core/ops/random_ops.py

@@ -35,9 +35,9 @@ def random_normal(
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
         The shape of the tensor.
         The shape of the tensor.
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     stddev : number, optional, default=1
     stddev : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
     dtype : str, optional
     dtype : str, optional
         The optional data type.
         The optional data type.
     seed : int, optional
     seed : int, optional
@@ -74,9 +74,9 @@ def random_uniform(
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
         The shape of the tensor.
         The shape of the tensor.
     minval : number, optional, default=0
     minval : number, optional, default=0
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     maxval : number, optional, default=1
     maxval : number, optional, default=1
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
     dtype : str, optional
     dtype : str, optional
         The optional data type.
         The optional data type.
     seed : int, optional
     seed : int, optional
@@ -114,9 +114,9 @@ def truncated_normal(
     shape : Sequence[Union[int, dragon.Tensor]]
     shape : Sequence[Union[int, dragon.Tensor]]
         The shape of the tensor.
         The shape of the tensor.
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     stddev : number, optional, default=1
     stddev : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
     dtype : str, optional
     dtype : str, optional
         The optional data type.
         The optional data type.
     seed : int, optional
     seed : int, optional

tensorlayer/core/activations.py

@@ -46,7 +46,7 @@ def leaky_relu(x, alpha=0.2, name="leaky_relu", **kwargs):
     x : dragon.Tensor
     x : dragon.Tensor
         The input tensor.
         The input tensor.
     alpha : float, optional, default=0.2
     alpha : float, optional, default=0.2
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     name : str, optional
     name : str, optional
         The optional operator name.
         The optional operator name.
 
 

tensorlayer/core/initializers.py

@@ -50,9 +50,9 @@ class Initializer(object):
 
 
 
 
 class Constant(Initializer):
 class Constant(Initializer):
-    r"""Fill tensors with a scalar value.
+    r"""Fill tensor with a scalar value.
 
 
-    .. math:: y \leftarrow \text{Value}
+    .. math:: \text{tensor} \leftarrow \text{value}
 
 
     """
     """
 
 
@@ -78,32 +78,30 @@ class Constant(Initializer):
 
 
 
 
 class GlorotNormal(Initializer):
 class GlorotNormal(Initializer):
-    r"""Fill tensors according to a glorot normal distribution.
+    r"""Fill tensor from a glorot normal distribution.
 
 
-    The **GlorotNormal** distribution is defined as:
-
-    .. math:: X \sim N(0, \sqrt{\frac{\text{scale}}{\text{FAN}}})
+    .. math:: \text{tensor} \sim \mathcal{N}(0, \sqrt{\frac{\text{scale}}{\text{fan}}})
 
 
     """
     """
 
 
-    def __init__(self, scale=2., mode='FAN_IN'):
+    def __init__(self, mode='fan_in', scale=2.0):
         """Create a ``GlorotNormal`` initializer.
         """Create a ``GlorotNormal`` initializer.
 
 
         Parameters
         Parameters
         ----------
         ----------
-        scale : float, optional, default=2.
-            The scale factor of distribution.
-        mode : {'FAN_IN', 'FAN_OUT', 'FAN_AVG'}, optional
-            The mode to compute the normalizer.
+        mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
+            The mode to compute the fans.
+        scale : float, optional, default=2.0
+            The scale factor to distribution.
 
 
         """
         """
-        self.scale, self.mode = scale, mode
+        self.mode, self.scale = mode, scale
 
 
     def __call__(self, shape, dtype='float32', **kwargs):
     def __call__(self, shape, dtype='float32', **kwargs):
         return self._getter(
         return self._getter(
             init_ops.glorot_normal,
             init_ops.glorot_normal,
-            scale=self.scale,
             mode=self.mode,
             mode=self.mode,
+            scale=self.scale,
             shape=shape,
             shape=shape,
             dtype=dtype,
             dtype=dtype,
             **kwargs
             **kwargs
@@ -111,37 +109,32 @@ class GlorotNormal(Initializer):
 
 
 
 
 class GlorotUniform(Initializer):
 class GlorotUniform(Initializer):
-    r"""Fill tensors according to a glorot uniform distribution.
+    r"""Fill tensor from a glorot uniform distribution.
 
 
-    The **GlorotUniform** distribution is defined as:
-
-    .. math::
-        X \sim U(
-            -\sqrt{\frac{\text{scale}}{\text{FAN}}},
-             \sqrt{\frac{\text{scale}}{\text{FAN}}}
-            )
+    .. math:: \text{tensor} \sim \mathcal{U}(-\sqrt{\frac{\text{scale}}{\text{fan}}},
+                                              \sqrt{\frac{\text{scale}}{\text{fan}}})
 
 
     """
     """
 
 
-    def __init__(self, scale=3., mode='FAN_IN'):
+    def __init__(self, mode='fan_in', scale=3.0):
         """Create a ``GlorotUniform`` initializer.
         """Create a ``GlorotUniform`` initializer.
 
 
         Parameters
         Parameters
         ----------
         ----------
-        scale : float, optional, default=3.
-            The scale factor of distribution.
-        mode : {'FAN_IN', 'FAN_OUT', 'FAN_AVG'}, optional
-            The mode to compute the normalizer.
+        mode : {'fan_in', 'fan_out', 'fan_avg'}, optional
+            The mode to compute the fans.
+        scale : float, optional, default=3.0
+            The scale factor to distribution.
 
 
         """
         """
         super(GlorotUniform, self).__init__()
         super(GlorotUniform, self).__init__()
-        self.scale, self.mode = scale, mode
+        self.mode, self.scale = mode, scale
 
 
     def __call__(self, shape, dtype='float32', **kwargs):
     def __call__(self, shape, dtype='float32', **kwargs):
         return self._getter(
         return self._getter(
             init_ops.glorot_uniform,
             init_ops.glorot_uniform,
-            scale=self.scale,
             mode=self.mode,
             mode=self.mode,
+            scale=self.scale,
             shape=shape,
             shape=shape,
             dtype=dtype,
             dtype=dtype,
             **kwargs
             **kwargs
@@ -149,14 +142,14 @@ class GlorotUniform(Initializer):
 
 
 
 
 class Ones(Initializer):
 class Ones(Initializer):
-    r"""Fill tensors with ones.
+    r"""Fill tensor with ones.
 
 
-    .. math:: y \leftarrow 0
+    .. math:: \text{tensor} \leftarrow 1
 
 
     """
     """
 
 
     def __init__(self):
     def __init__(self):
-        """Create a ``Zeros`` initializer."""
+        """Create a ``Ones`` initializer."""
         super(Ones, self).__init__()
         super(Ones, self).__init__()
 
 
     def __call__(self, shape, dtype='float32', **kwargs):
     def __call__(self, shape, dtype='float32', **kwargs):
@@ -170,11 +163,9 @@ class Ones(Initializer):
 
 
 
 
 class RandomNormal(Initializer):
 class RandomNormal(Initializer):
-    r"""Fill tensors according to a random normal distribution.
-
-    The **RandomNormal** distribution is defined as:
+    r"""Fill tensor from a normal distribution.
 
 
-    .. math:: X \sim N(\mu, \sigma)
+    .. math:: \text{tensor} \sim \mathcal{N}(\mu, \sigma)
 
 
     """
     """
 
 
@@ -184,9 +175,9 @@ class RandomNormal(Initializer):
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0.
         mean : number, optional, default=0.
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         stddev : number, optional, default=0.05
         stddev : number, optional, default=0.05
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         """
         """
         self.mean, self.stddev = mean, stddev
         self.mean, self.stddev = mean, stddev
@@ -203,11 +194,9 @@ class RandomNormal(Initializer):
 
 
 
 
 class RandomUniform(Initializer):
 class RandomUniform(Initializer):
-    r"""Fill tensors according to a random uniform distribution.
+    r"""Fill tensors from an uniform distribution.
 
 
-    The **RandomUniform** distribution is defined as:
-
-    .. math:: X \sim U(\alpha, \beta)
+    .. math:: \text{tensor} \sim \mathcal{U}(\alpha, \beta)
 
 
     """
     """
 
 
@@ -217,9 +206,9 @@ class RandomUniform(Initializer):
         Parameters
         Parameters
         ----------
         ----------
         minval : number, optional, default=-0.05
         minval : number, optional, default=-0.05
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         maxval : number, optional, default=0.05
         maxval : number, optional, default=0.05
-            The value of :math:`\beta`.
+            The value to :math:`\beta`.
 
 
         """
         """
         self.minval, self.maxval = minval, maxval
         self.minval, self.maxval = minval, maxval
@@ -236,12 +225,9 @@ class RandomUniform(Initializer):
 
 
 
 
 class TruncatedNormal(Initializer):
 class TruncatedNormal(Initializer):
-    r"""Fill tensors according to a truncated normal distribution.
-
-    The **TruncatedNormal** distribution is defined as:
+    r"""Fill tensor from a truncated normal distribution.
 
 
-    .. math::
-        X \sim TN(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
+    .. math:: \text{tensor} \sim \mathcal{TN}(\mu, \sigma, \mu - 2\sigma, \mu + 2\sigma)
 
 
     """
     """
 
 
@@ -251,9 +237,9 @@ class TruncatedNormal(Initializer):
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0.
         mean : number, optional, default=0.
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         stddev : number, optional, default=0.05
         stddev : number, optional, default=0.05
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         """
         """
         self.mean, self.stddev = mean, stddev
         self.mean, self.stddev = mean, stddev
@@ -270,9 +256,9 @@ class TruncatedNormal(Initializer):
 
 
 
 
 class Zeros(Initializer):
 class Zeros(Initializer):
-    r"""Fill tensors with zeros.
+    r"""Fill tensor with zeros.
 
 
-    .. math:: y \leftarrow 1
+    .. math:: \text{tensor} \leftarrow 0
 
 
     """
     """
 
 

test/dragon/core/test_framework.py

@@ -46,7 +46,7 @@ class TestTensor(unittest.TestCase):
         self.assertNotEqual(a.__hash__(), b.__hash__())
         self.assertNotEqual(a.__hash__(), b.__hash__())
         self.assertNotEqual(a.__repr__(), b.__repr__())
         self.assertNotEqual(a.__repr__(), b.__repr__())
         self.assertNotEqual(b.__repr__(), dragon.EagerTensor([2]).__repr__())
         self.assertNotEqual(b.__repr__(), dragon.EagerTensor([2]).__repr__())
-        self.assertEqual(int(a.variable().placeholder().set_value(1)), 1)
+        self.assertEqual(int(a.constant().set_value(1)), 1)
         self.assertEqual(float(dragon.Tensor.convert_to(1)), 1.)
         self.assertEqual(float(dragon.Tensor.convert_to(1)), 1.)
         self.assertEqual(int(b.set_value(1)), 1)
         self.assertEqual(int(b.set_value(1)), 1)
         self.assertEqual(float(b), 1.)
         self.assertEqual(float(b), 1.)
@@ -73,6 +73,8 @@ class TestTensor(unittest.TestCase):
         with dragon.name_scope(''):
         with dragon.name_scope(''):
             b.name = 'b'
             b.name = 'b'
             self.assertEqual(b.name, 'b')
             self.assertEqual(b.name, 'b')
+        b.requires_grad = True
+        self.assertEqual(b.requires_grad, True)
 
 
     def test_dlpack_converter(self):
     def test_dlpack_converter(self):
         data = np.array([0., 1., 2.], 'float32')
         data = np.array([0., 1., 2.], 'float32')
@@ -101,14 +103,56 @@ class TestTensor(unittest.TestCase):
 class TestWorkspace(unittest.TestCase):
 class TestWorkspace(unittest.TestCase):
     """Test the workspace class."""
     """Test the workspace class."""
 
 
+    def test_clear(self):
+        w = dragon.Workspace()
+        with w.as_default():
+            x = dragon.EagerTensor(1)
+        self.assertEqual(x.size, 1)
+        w.clear()
+        self.assertEqual(x.size, 0)
+
+    def test_feed_tensor(self):
+        w = dragon.Workspace()
+        with w.as_default():
+            v1, v2 = dragon.EagerTensor(1), np.array(2)
+            x = dragon.Tensor('test_feed_tensor/x')
+            w.feed_tensor(x, v1)
+            self.assertEqual(int(x), 1)
+            w.feed_tensor(x, v2)
+            self.assertEqual(int(x), 2)
+
     def test_merge_form(self):
     def test_merge_form(self):
         w1, w2 = dragon.Workspace(), dragon.Workspace()
         w1, w2 = dragon.Workspace(), dragon.Workspace()
         with w1.as_default():
         with w1.as_default():
-            x = dragon.Tensor(str(id(w1))).set_value(0)
+            x = dragon.Tensor('test_merge_from/x').set_value(0)
         w2.merge_from(w1)
         w2.merge_from(w1)
         with w2.as_default():
         with w2.as_default():
             self.assertEqual(int(x), 0)
             self.assertEqual(int(x), 0)
 
 
+    def test_register_alias(self):
+        w = dragon.Workspace()
+        with w.as_default():
+            x = dragon.EagerTensor(1)
+            w.register_alias(x.id, 'test_register_alias/y')
+            self.assertEqual(int(w.fetch_tensor('test_register_alias/y')), 1)
+
+    def test_reset_tensor(self):
+        w = dragon.Workspace()
+        with w.as_default():
+            x = dragon.EagerTensor(1)
+            self.assertEqual(x.size, 1)
+            w.reset_tensor(x)
+            self.assertEqual(x.size, 0)
+
+    def test_reset_workspace(self):
+        w = dragon.Workspace()
+        with w.as_default():
+            try:
+                dragon.reset_workspace()
+            except AssertionError:
+                pass
+        dragon.reset_workspace()
+
 
 
 if __name__ == '__main__':
 if __name__ == '__main__':
     run_tests()
     run_tests()

test/dragon/core/test_ops.py

@@ -2706,7 +2706,7 @@ class TestTensorOps(OpTestCase):
                 for a_shape, b_shape in self.binary_test_shapes:
                 for a_shape, b_shape in self.binary_test_shapes:
                     data1, data2 = arange(a_shape, 1), arange(b_shape)
                     data1, data2 = arange(a_shape, 1), arange(b_shape)
                     a, b = new_tensor(data1), new_tensor(data2)
                     a, b = new_tensor(data1), new_tensor(data2)
-                    self.assertEqual(a.__rdiv__(b), data2 / data1)
+                    self.assertEqual(a.__rtruediv__(b), data2 / data1)
 
 
     def test_reshape(self):
     def test_reshape(self):
         entries = [(0, 0), (0, -1)]
         entries = [(0, 0), (0, -1)]

torch/__init__.py

@@ -43,7 +43,6 @@ from dragon.vm.torch.ops import tensorbind as _
 from dragon.vm.torch.ops.array.functional import argmax
 from dragon.vm.torch.ops.array.functional import argmax
 from dragon.vm.torch.ops.array.functional import argmin
 from dragon.vm.torch.ops.array.functional import argmin
 from dragon.vm.torch.ops.array.functional import assign
 from dragon.vm.torch.ops.array.functional import assign
-from dragon.vm.torch.ops.array.functional import cast
 from dragon.vm.torch.ops.array.functional import cat
 from dragon.vm.torch.ops.array.functional import cat
 from dragon.vm.torch.ops.array.functional import channel_normalize
 from dragon.vm.torch.ops.array.functional import channel_normalize
 from dragon.vm.torch.ops.array.functional import channel_shuffle
 from dragon.vm.torch.ops.array.functional import channel_shuffle
@@ -72,12 +71,10 @@ from dragon.vm.torch.ops.array.functional import unsqueeze
 from dragon.vm.torch.ops.array.functional import where
 from dragon.vm.torch.ops.array.functional import where
 from dragon.vm.torch.ops.init.functional import arange
 from dragon.vm.torch.ops.init.functional import arange
 from dragon.vm.torch.ops.init.functional import eye
 from dragon.vm.torch.ops.init.functional import eye
-from dragon.vm.torch.ops.init.functional import normal
 from dragon.vm.torch.ops.init.functional import ones
 from dragon.vm.torch.ops.init.functional import ones
 from dragon.vm.torch.ops.init.functional import ones_like
 from dragon.vm.torch.ops.init.functional import ones_like
 from dragon.vm.torch.ops.init.functional import rand
 from dragon.vm.torch.ops.init.functional import rand
 from dragon.vm.torch.ops.init.functional import randn
 from dragon.vm.torch.ops.init.functional import randn
-from dragon.vm.torch.ops.init.functional import uniform
 from dragon.vm.torch.ops.init.functional import zeros
 from dragon.vm.torch.ops.init.functional import zeros
 from dragon.vm.torch.ops.init.functional import zeros_like
 from dragon.vm.torch.ops.init.functional import zeros_like
 from dragon.vm.torch.ops.math.functional import abs
 from dragon.vm.torch.ops.math.functional import abs

torch/jit/tape.py

@@ -77,7 +77,7 @@ class Tape(object):
         self._sources.add(tensor_id)
         self._sources.add(tensor_id)
 
 
     def merge_from(self, other):
     def merge_from(self, other):
-        """Merge operations from another."""
+        """Merge operations from the other."""
         if other is not None:
         if other is not None:
             self._operations = {**self._operations, **other._operations}
             self._operations = {**self._operations, **other._operations}
             self._sources = self._sources.union(other._sources)
             self._sources = self._sources.union(other._sources)

torch/nn/functional.py

@@ -1043,7 +1043,7 @@ def normalize(input, p=2, dim=1, eps=1e-12, out=None):
     dim : int, optional, default=1
     dim : int, optional, default=1
         The dimension to reduce.
         The dimension to reduce.
     eps : float, optional, default=1e-12
     eps : float, optional, default=1e-12
-        The value of :math:`\epsilon`.
+        The value to :math:`\epsilon`.
     out : dragon.vm.torch.Tensor, optional
     out : dragon.vm.torch.Tensor, optional
         The optional output tensor.
         The optional output tensor.
 
 

torch/nn/modules/activation.py

@@ -53,7 +53,7 @@ class ELU(Module):
         Parameters
         Parameters
         ----------
         ----------
         alpha : float, optional, default=1.
         alpha : float, optional, default=1.
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         inplace : bool, optional, default=False
         inplace : bool, optional, default=False
             Whether to do the operation in-place.
             Whether to do the operation in-place.
 
 

torch/ops/array/functional.py

@@ -106,7 +106,7 @@ def assign(out, starts, sizes, input):
 
 
 
 
 def cast(input, dtype='float32', inplace=False):
 def cast(input, dtype='float32', inplace=False):
-    """Cast the data type of input to another.
+    """Cast the data type of input.
 
 
     Parameters
     Parameters
     ----------
     ----------

torch/ops/init/functional.py

@@ -27,7 +27,7 @@ def arange(
     device=None,
     device=None,
     requires_grad=False,
     requires_grad=False,
 ):
 ):
-    r"""Return a tensor with evenly spaced values within a interval.
+    """Return a tensor of evenly spaced values within a interval.
 
 
     Specify ``start`` and ``end`` to determine an interval:
     Specify ``start`` and ``end`` to determine an interval:
 
 
@@ -79,7 +79,7 @@ def arange(
             device if device else cpp.device(),
             device if device else cpp.device(),
             num_args=len(slice_args),
             num_args=len(slice_args),
             dtype=dtype if dtype else 'int64',
             dtype=dtype if dtype else 'int64',
-        ).apply(slice_args)
+        ).apply(slice_args, out)
     out.requires_grad = requires_grad
     out.requires_grad = requires_grad
     return out
     return out
 
 
@@ -145,51 +145,21 @@ def fill_like(out, shape_like, value):
         .apply(out, [], shape_like)
         .apply(out, [], shape_like)
 
 
 
 
-def normal(*size, **kwargs):
-    """Return a tensor with a normal distribution.
-
-    Parameters
-    ----------
-    size : int...
-        The size(s) indicating the out shape.
-    mean : number, optional, default=0
-        The mean of distribution.
-    std : number, optional, default=1
-        The stddev of distribution.
-    out : dragon.vm.torch.Tensor, optional
-        The optional output tensor.
-    dtype : str, optional, default='float32'
-        The optional data type.
-    device : dragon.vm.torch.device, optional
-        The optional device of returned tensor.
-    requires_grad : bool, optional, default=False
-        **True** to record gradient for returned tensor.
-
-    Returns
-    -------
-    dragon.vm.torch.Tensor
-        The output tensor.
-
-    """
-    out = kwargs.get('out', utils.new_leaf(size, kwargs))
-    return normal_fill(out, kwargs.get('mean', 0), kwargs.get('std', 1))
-
-
-def normal_fill(out, mean=0, std=1):
-    """Fill a tensor with a normal distribution."""
-    shape = out.shape
+def normal_fill(input, mean=0, std=1):
+    """Fill input from the normal distribution."""
+    shape = input.shape
     return _functions.RandomNormal \
     return _functions.RandomNormal \
         .instantiate(
         .instantiate(
-            out.device,
+            input.device,
             ndim=len(shape),
             ndim=len(shape),
             mean=float(mean),
             mean=float(mean),
             std=float(std),
             std=float(std),
-            dtype=out.dtype,
-        ).apply(out, shape)
+            dtype=input.dtype,
+        ).apply(input, shape)
 
 
 
 
 def ones(*size, **kwargs):
 def ones(*size, **kwargs):
-    r"""Return a tensor with value **1** filled.
+    r"""Return a tensor filled with ones.
 
 
     .. math:: \text{out} \leftarrow 1
     .. math:: \text{out} \leftarrow 1
 
 
@@ -217,7 +187,7 @@ def ones(*size, **kwargs):
 
 
 
 
 def ones_like(input, **kwargs):
 def ones_like(input, **kwargs):
-    r"""Return a tensor with value **1** filled, shape as input.
+    r"""Return a tensor of ones with shape as the other.
 
 
     .. math:: \text{out} \leftarrow 1
     .. math:: \text{out} \leftarrow 1
 
 
@@ -245,7 +215,7 @@ def ones_like(input, **kwargs):
 
 
 
 
 def rand(*size, **kwargs):
 def rand(*size, **kwargs):
-    """Return a float tensor with a uniform distribution of U(0, 1).
+    """Return a tensor from the uniform distribution of U(0, 1).
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -271,7 +241,7 @@ def rand(*size, **kwargs):
 
 
 
 
 def randn(*size, **kwargs):
 def randn(*size, **kwargs):
-    """Return a float tensor with a normal distribution of N(0, 1).
+    """Return a tensor from the normal distribution of N(0, 1).
 
 
     Parameters
     Parameters
     ----------
     ----------
@@ -296,51 +266,21 @@ def randn(*size, **kwargs):
     return normal_fill(out, 0, 1)
     return normal_fill(out, 0, 1)
 
 
 
 
-def uniform(*size, **kwargs):
-    """Return a tensor with a normal distribution.
-
-    Parameters
-    ----------
-    size : int...
-        The size(s) indicating the out shape.
-    low : number, optional, default=0
-        The low bound of distribution.
-    high : number, optional, default=1
-        The high bound of distribution.
-    out : dragon.vm.torch.Tensor, optional
-        The optional output tensor.
-    dtype : str, optional, default='float32'
-        The optional data type.
-    device : dragon.vm.torch.device, optional
-        The optional device of returned tensor.
-    requires_grad : bool, optional, default=False
-        **True** to record gradient for returned tensor.
-
-    Returns
-    -------
-    dragon.vm.torch.Tensor
-        The output tensor.
-
-    """
-    out = kwargs.get('out', utils.new_leaf(size, kwargs))
-    return uniform_fill(out, kwargs.get('low', 0), kwargs.get('high', 1))
-
-
-def uniform_fill(out, low=0, high=1):
-    """Fill a tensor with a uniform distribution."""
-    shape = out.shape
+def uniform_fill(input, low=0, high=1):
+    """Fill input from the uniform distribution."""
+    shape = input.shape
     return _functions.RandomUniform \
     return _functions.RandomUniform \
         .instantiate(
         .instantiate(
-            out.device,
+            input.device,
             ndim=len(shape),
             ndim=len(shape),
             low=float(low),
             low=float(low),
             high=float(high),
             high=float(high),
-            dtype=out.dtype,
-        ).apply(out, shape)
+            dtype=input.dtype,
+        ).apply(input, shape)
 
 
 
 
 def zeros(*size, **kwargs):
 def zeros(*size, **kwargs):
-    r"""Return a tensor with value **0** filled.
+    r"""Return a tensor filled with zeros.
 
 
     .. math:: \text{out} \leftarrow 0
     .. math:: \text{out} \leftarrow 0
 
 
@@ -368,7 +308,7 @@ def zeros(*size, **kwargs):
 
 
 
 
 def zeros_like(input, **kwargs):
 def zeros_like(input, **kwargs):
-    r"""Return a tensor with value **0** filled, shape as input.
+    r"""Return a tensor of zeros with shape as the other.
 
 
     .. math:: \text{out} \leftarrow 0
     .. math:: \text{out} \leftarrow 0
 
 

torch/ops/math/functional.py

@@ -54,9 +54,9 @@ def axpby(input, alpha=1., beta=1., out=None):
     input : dragon.vm.torch.Tensor
     input : dragon.vm.torch.Tensor
         The input tensor.
         The input tensor.
     alpha : float, optional, default=1.
     alpha : float, optional, default=1.
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     beta : float, optional, default=1.
     beta : float, optional, default=1.
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
     out : dragon.vm.torch.Tensor, optional
     out : dragon.vm.torch.Tensor, optional
         The optional output tensor.
         The optional output tensor.
 
 

torch/ops/tensorbind.py

@@ -40,14 +40,14 @@ def abs(self):
     return math_funcs.abs(self)
     return math_funcs.abs(self)
 
 
 
 
-def add(self, value):
+def add(self, other):
     r"""Compute the element-wise addition.
     r"""Compute the element-wise addition.
 
 
-    .. math:: \text{out} = \text{self} + \text{value}
+    .. math:: \text{out} = \text{self} + \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to add.
         The value to add.
 
 
     Returns
     Returns
@@ -60,17 +60,17 @@ def add(self, value):
     `torch.add(...)`_ : Compute the element-wise addition.
     `torch.add(...)`_ : Compute the element-wise addition.
 
 
     """
     """
-    return math_funcs.add(self, value)
+    return math_funcs.add(self, other)
 
 
 
 
-def add_(self, value):
+def add_(self, other):
     r"""Compute the element-wise addition.
     r"""Compute the element-wise addition.
 
 
-    .. math:: \text{self} \mathrel{+}= \text{value}
+    .. math:: \text{self} \mathrel{+}= \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to add.
         The value to add.
 
 
     Returns
     Returns
@@ -83,7 +83,7 @@ def add_(self, value):
     `torch.add(...)`_ : Compute the element-wise addition.
     `torch.add(...)`_ : Compute the element-wise addition.
 
 
     """
     """
-    return math_funcs.add(self, value, self)
+    return math_funcs.add(self, other, self)
 
 
 
 
 def backward(self, gradient=None, retain_graph=False):
 def backward(self, gradient=None, retain_graph=False):
@@ -398,14 +398,14 @@ def cumsum(self, dim):
     return array_funcs.cumsum(self, dim)
     return array_funcs.cumsum(self, dim)
 
 
 
 
-def div(self, value):
+def div(self, other):
     r"""Compute the element-wise division.
     r"""Compute the element-wise division.
 
 
-    .. math:: \text{out} = \text{self} \div \text{value}
+    .. math:: \text{out} = \text{self} \div \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to divide.
         The value to divide.
 
 
     Returns
     Returns
@@ -418,17 +418,17 @@ def div(self, value):
     `torch.div(...)`_ : Compute the element-wise division.
     `torch.div(...)`_ : Compute the element-wise division.
 
 
     """
     """
-    return math_funcs.div(self, value)
+    return math_funcs.div(self, other)
 
 
 
 
-def div_(self, value):
+def div_(self, other):
     r"""Compute the element-wise division.
     r"""Compute the element-wise division.
 
 
-    .. math:: \text{self} \mathrel{\div}= \text{value}
+    .. math:: \text{self} \mathrel{\div}= \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to be divided.
         The value to be divided.
 
 
     Returns
     Returns
@@ -441,7 +441,7 @@ def div_(self, value):
     `torch.div(...)`_ : Compute the element-wise division.
     `torch.div(...)`_ : Compute the element-wise division.
 
 
     """
     """
-    return math_funcs.div(self, value, self)
+    return math_funcs.div(self, other, self)
 
 
 
 
 def double(self):
 def double(self):
@@ -531,14 +531,14 @@ def expand(self, *sizes):
 
 
 
 
 def fill_(self, value):
 def fill_(self, value):
-    r"""Fill with the given constant value.
+    r"""Fill self with a scalar value.
 
 
     .. math:: \text{self} \leftarrow \text{value}
     .. math:: \text{self} \leftarrow \text{value}
 
 
     Parameters
     Parameters
     ----------
     ----------
     value : number
     value : number
-        The constant value.
+        The value to fill.
 
 
     Returns
     Returns
     -------
     -------
@@ -633,6 +633,19 @@ def ge(self, other):
 
 
 
 
 def getitem(self, item):
 def getitem(self, item):
+    """Select elements at the specific index.
+
+    Parameters
+    ----------
+    item : Union[int, slice, dragon.vm.torch.Tensor]
+        The index.
+
+    Returns
+    -------
+    dragon.vm.torch.Tensor
+        The output tensor.
+
+    """
     if isinstance(item, Tensor):
     if isinstance(item, Tensor):
         return self.masked_select(item)
         return self.masked_select(item)
     else:
     else:
@@ -935,14 +948,14 @@ def min(self, dim=None, keepdim=False):
     return array_funcs.min(self, dim, keepdim)
     return array_funcs.min(self, dim, keepdim)
 
 
 
 
-def mul(self, value):
+def mul(self, other):
     r"""Compute the element-wise multiplication.
     r"""Compute the element-wise multiplication.
 
 
-    .. math:: \text{out} = \text{self} \times \text{value}
+    .. math:: \text{out} = \text{self} \times \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to multiply.
         The value to multiply.
 
 
     Returns
     Returns
@@ -955,17 +968,17 @@ def mul(self, value):
     `torch.mul(...)`_ : Compute the element-wise multiplication.
     `torch.mul(...)`_ : Compute the element-wise multiplication.
 
 
     """
     """
-    return math_funcs.mul(self, value)
+    return math_funcs.mul(self, other)
 
 
 
 
-def mul_(self, value):
+def mul_(self, other):
     r"""Compute the element-wise multiplication.
     r"""Compute the element-wise multiplication.
 
 
-    .. math:: \text{self} \mathrel{\times}= \text{value}
+    .. math:: \text{self} \mathrel{\times}= \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to multiply.
         The value to multiply.
 
 
     Returns
     Returns
@@ -978,7 +991,7 @@ def mul_(self, value):
     `torch.mul(...)`_ : Compute the element-wise multiplication.
     `torch.mul(...)`_ : Compute the element-wise multiplication.
 
 
     """
     """
-    return math_funcs.mul(self, value, self)
+    return math_funcs.mul(self, other, self)
 
 
 
 
 def multinomial(self, num_samples, eps=0.):
 def multinomial(self, num_samples, eps=0.):
@@ -1076,16 +1089,16 @@ def nonzero(self):
 
 
 
 
 def normal_(self, mean=0, std=1):
 def normal_(self, mean=0, std=1):
-    r"""Fill self with a normal distribution.
+    r"""Fill self from a normal distribution.
 
 
-    .. math:: \text{self} \leftarrow N(\mu, \sigma)
+    .. math:: \text{self} \sim \mathcal{N}(\mu, \sigma)
 
 
     Parameters
     Parameters
     ----------
     ----------
     mean : number, optional, default=0
     mean : number, optional, default=0
-        The value of :math:`\mu`.
+        The value to :math:`\mu`.
     std : number, optional, default=1
     std : number, optional, default=1
-        The value of :math:`\sigma`.
+        The value to :math:`\sigma`.
 
 
     Returns
     Returns
     -------
     -------
@@ -1302,6 +1315,16 @@ def rsqrt_(self):
 
 
 
 
 def setitem(self, key, value):
 def setitem(self, key, value):
+    """Set elements at the specific index.
+
+    Parameters
+    ----------
+    key : Union[int, slice, dragon.vm.torch.Tensor]
+        The index.
+    value : Union[dragon.vm.torch.Tensor, number]
+        The value to set.
+
+    """
     if isinstance(key, Tensor):
     if isinstance(key, Tensor):
         return self.masked_fill_(key, value)
         return self.masked_fill_(key, value)
     else:
     else:
@@ -1464,14 +1487,14 @@ def sum(self, dim=None, keepdim=False):
     return array_funcs.sum(self, dim, keepdim)
     return array_funcs.sum(self, dim, keepdim)
 
 
 
 
-def sub(self, value):
+def sub(self, other):
     r"""Compute the element-wise subtraction.
     r"""Compute the element-wise subtraction.
 
 
-    .. math:: \text{out} = \text{self} - \text{value}
+    .. math:: \text{out} = \text{self} - \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to subtract.
         The value to subtract.
 
 
     Returns
     Returns
@@ -1484,17 +1507,17 @@ def sub(self, value):
     `torch.sub(...)`_ : Compute the element-wise subtraction.
     `torch.sub(...)`_ : Compute the element-wise subtraction.
 
 
     """
     """
-    return math_funcs.sub(self, value)
+    return math_funcs.sub(self, other)
 
 
 
 
-def sub_(self, value):
+def sub_(self, other):
     r"""Compute the element-wise subtraction.
     r"""Compute the element-wise subtraction.
 
 
-    .. math:: \text{self} \mathrel{-}= \text{value}
+    .. math:: \text{self} \mathrel{-}= \text{other}
 
 
     Parameters
     Parameters
     ----------
     ----------
-    value : Union[dragon.vm.torch.Tensor, number]
+    other : Union[dragon.vm.torch.Tensor, number]
         The value to be subtracted.
         The value to be subtracted.
 
 
     Returns
     Returns
@@ -1507,7 +1530,7 @@ def sub_(self, value):
     `torch.sub(...)`_ : Compute the element-wise subtraction.
     `torch.sub(...)`_ : Compute the element-wise subtraction.
 
 
     """
     """
-    return math_funcs.sub(self, value, self)
+    return math_funcs.sub(self, other, self)
 
 
 
 
 def type(self, dtype=None):
 def type(self, dtype=None):
@@ -1532,16 +1555,16 @@ def type(self, dtype=None):
 
 
 
 
 def uniform_(self, low=0, high=1):
 def uniform_(self, low=0, high=1):
-    r"""Fill self with a uniform distribution.
+    r"""Fill self from a uniform distribution.
 
 
-    .. math:: \text{self} \leftarrow U(\alpha, \beta)
+    .. math:: \text{self} \sim \mathcal{U}(\alpha, \beta)
 
 
     Parameters
     Parameters
     ----------
     ----------
     low : number, optional, default=0
     low : number, optional, default=0
-        The value of :math:`\alpha`.
+        The value to :math:`\alpha`.
     high : number, optional, default=1
     high : number, optional, default=1
-        The value of :math:`\beta`.
+        The value to :math:`\beta`.
 
 
     Returns
     Returns
     -------
     -------
@@ -1741,7 +1764,6 @@ Tensor.unsqueeze_ = unsqueeze_
 Tensor.where = where
 Tensor.where = where
 Tensor.__getitem__ = getitem
 Tensor.__getitem__ = getitem
 Tensor.__radd__ = lambda self, value: math_funcs._binary_func(value, self, 'Add')
 Tensor.__radd__ = lambda self, value: math_funcs._binary_func(value, self, 'Add')
-Tensor.__rdiv__ = lambda self, value: math_funcs._binary_func(value, self, 'Div')
 Tensor.__rmul__ = lambda self, value: math_funcs._binary_func(value, self, 'Mul')
 Tensor.__rmul__ = lambda self, value: math_funcs._binary_func(value, self, 'Mul')
 Tensor.__rsub__ = lambda self, value: math_funcs._binary_func(value, self, 'Sub')
 Tensor.__rsub__ = lambda self, value: math_funcs._binary_func(value, self, 'Sub')
 Tensor.__rtruediv__ = lambda self, value: math_funcs._binary_func(value, self, 'Div')
 Tensor.__rtruediv__ = lambda self, value: math_funcs._binary_func(value, self, 'Div')

torch/optim/adam.py

@@ -21,7 +21,7 @@ from dragon.vm.torch.optim.optimizer import Optimizer
 
 
 
 
 class Adam(Optimizer):
 class Adam(Optimizer):
-    r"""The optimizer which implements Adam algorithm.
+    r"""The optimizer to apply Adam algorithm.
     `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
     `[Kingma & Ba, 2014] <https://arxiv.org/abs/1412.6980>`_.
 
 
     The **Adam** update is defined as:
     The **Adam** update is defined as:

torch/optim/rmsprop.py

@@ -21,7 +21,7 @@ from dragon.vm.torch.optim.optimizer import Optimizer
 
 
 
 
 class RMSprop(Optimizer):
 class RMSprop(Optimizer):
-    r"""The optimizer which implements RMSprop algorithm.
+    r"""The optimizer to apply RMSprop algorithm.
     `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
     `[Hinton et.al, 2013] <http://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/lectures/lec6.pdf>`_.
 
 
     The **RMSprop** update is defined as:
     The **RMSprop** update is defined as:

torch/optim/sgd.py

@@ -22,7 +22,7 @@ from dragon.vm.torch.optim.optimizer import required
 
 
 
 
 class SGD(Optimizer):
 class SGD(Optimizer):
-    r"""The optimizer which implements SGD algorithm.
+    r"""The optimizer to apply SGD algorithm.
 
 
     Following SGD algorithms are supported:
     Following SGD algorithms are supported:
 
 

torch/tensor.py

@@ -171,7 +171,7 @@ class Tensor(object):
 
 
     @property
     @property
     def requires_grad(self):
     def requires_grad(self):
-        """Return a bool report whether the grad is required.
+        """Return whether the grad is required.
 
 
         Returns
         Returns
         -------
         -------
@@ -199,6 +199,14 @@ class Tensor(object):
 
 
     @property
     @property
     def volatile(self):
     def volatile(self):
+        """Return whether this tensor is volatile.
+
+        Returns
+        -------
+        bool
+            **True** if volatile otherwise **False**.
+
+        """
         warnings.warn('Attribute ``volatile`` was removed (always False).', stacklevel=2)
         warnings.warn('Attribute ``volatile`` was removed (always False).', stacklevel=2)
         return False
         return False
 
 
@@ -217,16 +225,15 @@ class Tensor(object):
         `torch.abs(...)`_ : Compute the absolute value of input.
         `torch.abs(...)`_ : Compute the absolute value of input.
 
 
         """
         """
-        pass
 
 
-    def add(self, value):
+    def add(self, other):
         r"""Compute the element-wise addition.
         r"""Compute the element-wise addition.
 
 
-        .. math:: \text{out} = \text{self} + \text{value}
+        .. math:: \text{out} = \text{self} + \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to add.
             The value to add.
 
 
         Returns
         Returns
@@ -239,16 +246,15 @@ class Tensor(object):
         `torch.add(...)`_ : Compute the element-wise addition.
         `torch.add(...)`_ : Compute the element-wise addition.
 
 
         """
         """
-        pass
 
 
-    def add_(self, value):
+    def add_(self, other):
         r"""Compute the element-wise addition.
         r"""Compute the element-wise addition.
 
 
-        .. math:: \text{self} \mathrel{+}= \text{value}
+        .. math:: \text{self} \mathrel{+}= \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to add.
             The value to add.
 
 
         Returns
         Returns
@@ -261,7 +267,6 @@ class Tensor(object):
         `torch.add(...)`_ : Compute the element-wise addition.
         `torch.add(...)`_ : Compute the element-wise addition.
 
 
         """
         """
-        pass
 
 
     def backward(self, gradient=None, retain_graph=False):
     def backward(self, gradient=None, retain_graph=False):
         """Compute the derivatives of this tensor w.r.t. graph leaves.
         """Compute the derivatives of this tensor w.r.t. graph leaves.
@@ -290,7 +295,6 @@ class Tensor(object):
         `torch.bitwise_not(...)`_ : Compute the element-wise NOT bitwise operation.
         `torch.bitwise_not(...)`_ : Compute the element-wise NOT bitwise operation.
 
 
         """
         """
-        pass
 
 
     def bitwise_not_(self):
     def bitwise_not_(self):
         r"""Compute the element-wise NOT bitwise operation.
         r"""Compute the element-wise NOT bitwise operation.
@@ -307,7 +311,6 @@ class Tensor(object):
         `torch.bitwise_not(...)`_ : Compute the element-wise NOT bitwise operation.
         `torch.bitwise_not(...)`_ : Compute the element-wise NOT bitwise operation.
 
 
         """
         """
-        pass
 
 
     def bitwise_xor(self, other):
     def bitwise_xor(self, other):
         r"""Compute the element-wise XOR bitwise operation.
         r"""Compute the element-wise XOR bitwise operation.
@@ -329,7 +332,6 @@ class Tensor(object):
         `torch.bitwise_xor(...)`_ : Compute the element-wise XOR bitwise operation.
         `torch.bitwise_xor(...)`_ : Compute the element-wise XOR bitwise operation.
 
 
         """
         """
-        pass
 
 
     def bitwise_xor_(self, other):
     def bitwise_xor_(self, other):
         r"""Compute the element-wise XOR bitwise operation.
         r"""Compute the element-wise XOR bitwise operation.
@@ -351,7 +353,6 @@ class Tensor(object):
         `torch.bitwise_xor(...)`_ : Compute the element-wise XOR bitwise operation.
         `torch.bitwise_xor(...)`_ : Compute the element-wise XOR bitwise operation.
 
 
         """
         """
-        pass
 
 
     def bool(self):
     def bool(self):
         """Return a bool tensor with the same data.
         """Return a bool tensor with the same data.
@@ -362,7 +363,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def bool_(self):
     def bool_(self):
         """Cast to a bool tensor.
         """Cast to a bool tensor.
@@ -373,7 +373,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def byte(self):
     def byte(self):
         """Return an uint8 tensor with the same data.
         """Return an uint8 tensor with the same data.
@@ -384,7 +383,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def byte_(self):
     def byte_(self):
         """Cast to an uint8 tensor.
         """Cast to an uint8 tensor.
@@ -395,7 +393,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def ceil(self):
     def ceil(self):
         r"""Return a tensor taken the ceil of elements.
         r"""Return a tensor taken the ceil of elements.
@@ -412,7 +409,6 @@ class Tensor(object):
         `torch.ceil(...)`_ : Compute the smallest integer not less than input.
         `torch.ceil(...)`_ : Compute the smallest integer not less than input.
 
 
         """
         """
-        pass
 
 
     def ceil_(self):
     def ceil_(self):
         r"""Set to the ceil of elements.
         r"""Set to the ceil of elements.
@@ -429,7 +425,6 @@ class Tensor(object):
         `torch.ceil(...)`_ : Compute the smallest integer not less than input.
         `torch.ceil(...)`_ : Compute the smallest integer not less than input.
 
 
         """
         """
-        pass
 
 
     def char(self):
     def char(self):
         """Return an int8 tensor with the same data.
         """Return an int8 tensor with the same data.
@@ -440,7 +435,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def char_(self):
     def char_(self):
         """Cast to an int8 tensor.
         """Cast to an int8 tensor.
@@ -451,7 +445,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def chunk(self, chunks, dim=0):
     def chunk(self, chunks, dim=0):
         """Split self into several parts along the given dim.
         """Split self into several parts along the given dim.
@@ -469,7 +462,6 @@ class Tensor(object):
             The output chunks.
             The output chunks.
 
 
         """
         """
-        pass
 
 
     def clamp(self, min=None, max=None):
     def clamp(self, min=None, max=None):
         """Return a tensor with elements clamped into a range.
         """Return a tensor with elements clamped into a range.
@@ -491,7 +483,6 @@ class Tensor(object):
         `torch.clamp(...)`_ : Compute the clipped input according to the given bounds.
         `torch.clamp(...)`_ : Compute the clipped input according to the given bounds.
 
 
         """
         """
-        pass
 
 
     def clamp_(self, min=None, max=None):
     def clamp_(self, min=None, max=None):
         """Clamp elements into the a range.
         """Clamp elements into the a range.
@@ -513,7 +504,6 @@ class Tensor(object):
         `torch.clamp(...)`_ : Compute the clipped input according to the given bounds.
         `torch.clamp(...)`_ : Compute the clipped input according to the given bounds.
 
 
         """
         """
-        pass
 
 
     def copy_(self, src):
     def copy_(self, src):
         """Copy the elements into this tensor.
         """Copy the elements into this tensor.
@@ -557,7 +547,6 @@ class Tensor(object):
         `torch.cos(...)`_ : Compute the cos of input.
         `torch.cos(...)`_ : Compute the cos of input.
 
 
         """
         """
-        pass
 
 
     def cpu(self):
     def cpu(self):
         """Switch the internal storage on cpu memory.
         """Switch the internal storage on cpu memory.
@@ -611,7 +600,6 @@ class Tensor(object):
         `torch.cumsum(...)`_ : Compute the cumulative sum of elements along the given axis.
         `torch.cumsum(...)`_ : Compute the cumulative sum of elements along the given axis.
 
 
         """
         """
-        pass
 
 
     def detach(self):
     def detach(self):
         """Return a data reference detaching the grad.
         """Return a data reference detaching the grad.
@@ -635,14 +623,14 @@ class Tensor(object):
         """
         """
         return self._impl.ndim
         return self._impl.ndim
 
 
-    def div(self, value):
+    def div(self, other):
         r"""Compute the element-wise division.
         r"""Compute the element-wise division.
 
 
-        .. math:: \text{out} = \text{self} \div \text{value}
+        .. math:: \text{out} = \text{self} \div \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to divide.
             The value to divide.
 
 
         Returns
         Returns
@@ -655,16 +643,15 @@ class Tensor(object):
         `torch.div(...)`_ : Compute the element-wise division.
         `torch.div(...)`_ : Compute the element-wise division.
 
 
         """
         """
-        pass
 
 
-    def div_(self, value):
+    def div_(self, other):
         r"""Compute the element-wise division.
         r"""Compute the element-wise division.
 
 
-        .. math:: \text{self} \mathrel{\div}= \text{value}
+        .. math:: \text{self} \mathrel{\div}= \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to be divided.
             The value to be divided.
 
 
         Returns
         Returns
@@ -677,7 +664,6 @@ class Tensor(object):
         `torch.div(...)`_ : Compute the element-wise division.
         `torch.div(...)`_ : Compute the element-wise division.
 
 
         """
         """
-        pass
 
 
     def double(self):
     def double(self):
         """Return a float64 tensor with the same data.
         """Return a float64 tensor with the same data.
@@ -688,7 +674,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def double_(self):
     def double_(self):
         """Cast to a float64 tensor.
         """Cast to a float64 tensor.
@@ -699,7 +684,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def eq(self, other):
     def eq(self, other):
         r"""Compute the element-wise equal comparison.
         r"""Compute the element-wise equal comparison.
@@ -721,7 +705,6 @@ class Tensor(object):
         `torch.eq(...)`_ : Compute the element-wise equal comparison.
         `torch.eq(...)`_ : Compute the element-wise equal comparison.
 
 
         """
         """
-        pass
 
 
     def exp(self):
     def exp(self):
         r"""Compute the exponential.
         r"""Compute the exponential.
@@ -738,7 +721,6 @@ class Tensor(object):
         `torch.exp(...)`_ : Compute the exponential of input.
         `torch.exp(...)`_ : Compute the exponential of input.
 
 
         """
         """
-        pass
 
 
     def expand(self, *sizes):
     def expand(self, *sizes):
         """Return a tensor with elements broadcast.
         """Return a tensor with elements broadcast.
@@ -758,10 +740,9 @@ class Tensor(object):
         `torch.expand(...)`_ : Broadcast input according to given sizes.
         `torch.expand(...)`_ : Broadcast input according to given sizes.
 
 
         """
         """
-        pass
 
 
     def expand_as(self, other):
     def expand_as(self, other):
-        """Return a tensor with elements broadcast like another.
+        """Return a tensor with elements broadcast like the other.
 
 
         Parameters
         Parameters
         ----------
         ----------
@@ -781,14 +762,14 @@ class Tensor(object):
         return self.expand(*other.size())
         return self.expand(*other.size())
 
 
     def fill_(self, value):
     def fill_(self, value):
-        r"""Fill with the given constant value.
+        r"""Fill self with a scalar value.
 
 
         .. math:: \text{self} \leftarrow \text{value}
         .. math:: \text{self} \leftarrow \text{value}
 
 
         Parameters
         Parameters
         ----------
         ----------
         value : number
         value : number
-            The constant value.
+            The value to fill.
 
 
         Returns
         Returns
         -------
         -------
@@ -796,7 +777,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def float(self):
     def float(self):
         """Return a float32 tensor with the same data.
         """Return a float32 tensor with the same data.
@@ -807,7 +787,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def float_(self):
     def float_(self):
         """Cast to a float32 tensor.
         """Cast to a float32 tensor.
@@ -818,7 +797,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def floor(self):
     def floor(self):
         r"""Return a tensor taken the floor of elements.
         r"""Return a tensor taken the floor of elements.
@@ -835,7 +813,6 @@ class Tensor(object):
         `torch.floor(...)`_ : Compute the largest integer not greater than input.
         `torch.floor(...)`_ : Compute the largest integer not greater than input.
 
 
         """
         """
-        pass
 
 
     def floor_(self):
     def floor_(self):
         r"""Set to the floor of elements.
         r"""Set to the floor of elements.
@@ -852,7 +829,6 @@ class Tensor(object):
         `torch.floor(...)`_ : Compute the largest integer not greater than input.
         `torch.floor(...)`_ : Compute the largest integer not greater than input.
 
 
         """
         """
-        pass
 
 
     def ge(self, other):
     def ge(self, other):
         r"""Compute the element-wise greater-equal comparison.
         r"""Compute the element-wise greater-equal comparison.
@@ -874,7 +850,6 @@ class Tensor(object):
         `torch.ge(...)`_ : Compute the element-wise greater-equal comparison.
         `torch.ge(...)`_ : Compute the element-wise greater-equal comparison.
 
 
         """
         """
-        pass
 
 
     def gt(self, other):
     def gt(self, other):
         r"""Compute the element-wise greater comparison.
         r"""Compute the element-wise greater comparison.
@@ -896,7 +871,6 @@ class Tensor(object):
         `torch.gt(...)`_ : Compute the element-wise greater comparison.
         `torch.gt(...)`_ : Compute the element-wise greater comparison.
 
 
         """
         """
-        pass
 
 
     def half(self):
     def half(self):
         """Return a float16 tensor with the same data.
         """Return a float16 tensor with the same data.
@@ -907,7 +881,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def half_(self):
     def half_(self):
         """Cast to a float16 tensor.
         """Cast to a float16 tensor.
@@ -918,7 +891,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def index_select(self, dim, index):
     def index_select(self, dim, index):
         """Select the elements along the given dim using index.
         """Select the elements along the given dim using index.
@@ -936,7 +908,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def int(self):
     def int(self):
         """Return an int32 tensor with the same data.
         """Return an int32 tensor with the same data.
@@ -947,7 +918,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def int_(self):
     def int_(self):
         """Cast to an int32 tensor.
         """Cast to an int32 tensor.
@@ -958,10 +928,9 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def is_floating_point(self):
     def is_floating_point(self):
-        """Whether the data type is floating.
+        """Return whether the data type is floating.
 
 
         Floating types contains: (*float16*, *float32*, *float64*)
         Floating types contains: (*float16*, *float32*, *float64*)
 
 
@@ -993,7 +962,6 @@ class Tensor(object):
         `torch.le(...)`_ : Compute the element-wise less-equal comparison.
         `torch.le(...)`_ : Compute the element-wise less-equal comparison.
 
 
         """
         """
-        pass
 
 
     def log(self):
     def log(self):
         r"""Compute the natural logarithm.
         r"""Compute the natural logarithm.
@@ -1006,7 +974,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def logsumexp(self, dim, keepdim=False):
     def logsumexp(self, dim, keepdim=False):
         r"""Apply the composite of log, sum, and exp.
         r"""Apply the composite of log, sum, and exp.
@@ -1026,7 +993,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def long(self):
     def long(self):
         """Return an int64 tensor with the same data.
         """Return an int64 tensor with the same data.
@@ -1037,7 +1003,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def long_(self):
     def long_(self):
         """Cast to an int64 tensor.
         """Cast to an int64 tensor.
@@ -1048,7 +1013,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def lt(self, other):
     def lt(self, other):
         r"""Compute the element-wise less comparison.
         r"""Compute the element-wise less comparison.
@@ -1070,7 +1034,6 @@ class Tensor(object):
         `torch.lt(...)`_ : Compute the element-wise less comparison.
         `torch.lt(...)`_ : Compute the element-wise less comparison.
 
 
         """
         """
-        pass
 
 
     def masked_fill_(self, mask, value):
     def masked_fill_(self, mask, value):
         r"""Fill self with the given value where ``mask`` is **1**.
         r"""Fill self with the given value where ``mask`` is **1**.
@@ -1095,7 +1058,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def max(self, dim=None, keepdim=False):
     def max(self, dim=None, keepdim=False):
         """Compute the max value of elements along the given axis.
         """Compute the max value of elements along the given axis.
@@ -1113,7 +1075,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def masked_select(self, mask):
     def masked_select(self, mask):
         """Select the elements where mask is **1**.
         """Select the elements where mask is **1**.
@@ -1129,7 +1090,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def mean(self, dim=None, keepdim=False):
     def mean(self, dim=None, keepdim=False):
         """Compute the mean value of elements along the given axis.
         """Compute the mean value of elements along the given axis.
@@ -1147,7 +1107,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def min(self, dim=None, keepdim=False):
     def min(self, dim=None, keepdim=False):
         """Compute the min value of elements along the given axis.
         """Compute the min value of elements along the given axis.
@@ -1165,16 +1124,15 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
-    def mul(self, value):
+    def mul(self, other):
         r"""Compute the element-wise multiplication.
         r"""Compute the element-wise multiplication.
 
 
-        .. math:: \text{out} = \text{self} \times \text{value}
+        .. math:: \text{out} = \text{self} \times \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to multiply.
             The value to multiply.
 
 
         Returns
         Returns
@@ -1187,16 +1145,15 @@ class Tensor(object):
         `torch.mul(...)`_ : Compute the element-wise multiplication.
         `torch.mul(...)`_ : Compute the element-wise multiplication.
 
 
         """
         """
-        pass
 
 
-    def mul_(self, value):
+    def mul_(self, other):
         r"""Compute the element-wise multiplication.
         r"""Compute the element-wise multiplication.
 
 
-        .. math:: \text{self} \mathrel{\times}= \text{value}
+        .. math:: \text{self} \mathrel{\times}= \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to multiply.
             The value to multiply.
 
 
         Returns
         Returns
@@ -1209,7 +1166,6 @@ class Tensor(object):
         `torch.mul(...)`_ : Compute the element-wise multiplication.
         `torch.mul(...)`_ : Compute the element-wise multiplication.
 
 
         """
         """
-        pass
 
 
     def multinomial(self, num_samples, eps=0.):
     def multinomial(self, num_samples, eps=0.):
         """Return a tensor where each row contains ``num_samples``,
         """Return a tensor where each row contains ``num_samples``,
@@ -1228,7 +1184,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def narrow(self, dimension, start, length):
     def narrow(self, dimension, start, length):
         """Return a new tensor that is a narrowed version of input tensor.
         """Return a new tensor that is a narrowed version of input tensor.
@@ -1248,7 +1203,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def ndimension(self):
     def ndimension(self):
         """Alias for ``Tensor.dim()``.
         """Alias for ``Tensor.dim()``.
@@ -1281,7 +1235,6 @@ class Tensor(object):
         `torch.ne(...)`_ : Compute the element-wise not-equal comparison.
         `torch.ne(...)`_ : Compute the element-wise not-equal comparison.
 
 
         """
         """
-        pass
 
 
     def neg(self):
     def neg(self):
         r"""Compute the element-wise negative.
         r"""Compute the element-wise negative.
@@ -1298,7 +1251,6 @@ class Tensor(object):
         `torch.neg(...)`_ : Compute the element-wise negative.
         `torch.neg(...)`_ : Compute the element-wise negative.
 
 
         """
         """
-        pass
 
 
     def nonzero(self):
     def nonzero(self):
         """Return the indices of non-zero elements.
         """Return the indices of non-zero elements.
@@ -1309,19 +1261,18 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def normal_(self, mean=0, std=1):
     def normal_(self, mean=0, std=1):
-        r"""Fill self with a normal distribution.
+        r"""Fill self from a normal distribution.
 
 
-        .. math:: \text{self} \leftarrow N(\mu, \sigma)
+        .. math:: \text{self} \sim \mathcal{N}(\mu, \sigma)
 
 
         Parameters
         Parameters
         ----------
         ----------
         mean : number, optional, default=0
         mean : number, optional, default=0
-            The value of :math:`\mu`.
+            The value to :math:`\mu`.
         std : number, optional, default=1
         std : number, optional, default=1
-            The value of :math:`\sigma`.
+            The value to :math:`\sigma`.
 
 
         Returns
         Returns
         -------
         -------
@@ -1329,7 +1280,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def numel(self):
     def numel(self):
         """Return the total number of elements.
         """Return the total number of elements.
@@ -1359,7 +1309,7 @@ class Tensor(object):
         return self._impl.ToNumpy(readonly)
         return self._impl.ToNumpy(readonly)
 
 
     def one_(self):
     def one_(self):
-        r"""Fill with constant 1.
+        r"""Fill self with ones.
 
 
         .. math:: \text{self} \leftarrow 1
         .. math:: \text{self} \leftarrow 1
 
 
@@ -1369,7 +1319,7 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        self.fill_(1)
+        return self.fill_(1)
 
 
     def permute(self, *dims):
     def permute(self, *dims):
         """Return a new tensor with the specific order of dimensions.
         """Return a new tensor with the specific order of dimensions.
@@ -1385,7 +1335,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def pow(self, exponent):
     def pow(self, exponent):
         """Compute the power.
         """Compute the power.
@@ -1405,7 +1354,6 @@ class Tensor(object):
         `torch.pow(...)`_ : Compute the power of input.
         `torch.pow(...)`_ : Compute the power of input.
 
 
         """
         """
-        pass
 
 
     def reciprocal(self):
     def reciprocal(self):
         r"""Compute the reciprocal.
         r"""Compute the reciprocal.
@@ -1422,7 +1370,6 @@ class Tensor(object):
         `torch.reciprocal(...)`_ : Compute the reciprocal of input.
         `torch.reciprocal(...)`_ : Compute the reciprocal of input.
 
 
         """
         """
-        pass
 
 
     def reciprocal_(self):
     def reciprocal_(self):
         r"""Compute the reciprocal.
         r"""Compute the reciprocal.
@@ -1439,7 +1386,6 @@ class Tensor(object):
         `torch.reciprocal(...)`_ : Compute the reciprocal of input.
         `torch.reciprocal(...)`_ : Compute the reciprocal of input.
 
 
         """
         """
-        pass
 
 
     def repeat(self, *sizes):
     def repeat(self, *sizes):
         """Repeat elements along the specified dimensions.
         """Repeat elements along the specified dimensions.
@@ -1455,7 +1401,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def reshape(self, shape):
     def reshape(self, shape):
         """Return a tensor with the same data but a different shape.
         """Return a tensor with the same data but a different shape.
@@ -1475,7 +1420,6 @@ class Tensor(object):
         `torch.reshape(...)`_ : Change the shape of input.
         `torch.reshape(...)`_ : Change the shape of input.
 
 
         """
         """
-        pass
 
 
     def reshape_(self, shape):
     def reshape_(self, shape):
         """Change into a new shape with the same data.
         """Change into a new shape with the same data.
@@ -1495,7 +1439,6 @@ class Tensor(object):
         `torch.reshape(...)`_ : Change the shape of input.
         `torch.reshape(...)`_ : Change the shape of input.
 
 
         """
         """
-        pass
 
 
     def retain_grad(self):
     def retain_grad(self):
         """Retain grad for the non-leaf tensor."""
         """Retain grad for the non-leaf tensor."""
@@ -1517,7 +1460,6 @@ class Tensor(object):
         `torch.round(...)`_ : Compute the nearest integer of input.
         `torch.round(...)`_ : Compute the nearest integer of input.
 
 
         """
         """
-        pass
 
 
     def round_(self):
     def round_(self):
         r"""Set to the round of elements.
         r"""Set to the round of elements.
@@ -1534,7 +1476,6 @@ class Tensor(object):
         `torch.round(...)`_ : Compute the nearest integer of input.
         `torch.round(...)`_ : Compute the nearest integer of input.
 
 
         """
         """
-        pass
 
 
     def rsqrt(self):
     def rsqrt(self):
         r"""Compute the reciprocal square root.
         r"""Compute the reciprocal square root.
@@ -1551,7 +1492,6 @@ class Tensor(object):
         `torch.rsqrt(...)`_ : Compute the square root of input.
         `torch.rsqrt(...)`_ : Compute the square root of input.
 
 
         """
         """
-        pass
 
 
     def rsqrt_(self):
     def rsqrt_(self):
         r"""Compute the reciprocal square root.
         r"""Compute the reciprocal square root.
@@ -1568,7 +1508,6 @@ class Tensor(object):
         `torch.rsqrt(...)`_ : Compute the square root of input.
         `torch.rsqrt(...)`_ : Compute the square root of input.
 
 
         """
         """
-        pass
 
 
     def sign(self):
     def sign(self):
         r"""Return a tensor taken the sign indication of elements.
         r"""Return a tensor taken the sign indication of elements.
@@ -1591,7 +1530,6 @@ class Tensor(object):
         `torch.sign(...)`_ : Compute the sign indication of input.
         `torch.sign(...)`_ : Compute the sign indication of input.
 
 
         """
         """
-        pass
 
 
     def sign_(self):
     def sign_(self):
         r"""Set to the sign indication of elements.
         r"""Set to the sign indication of elements.
@@ -1614,7 +1552,6 @@ class Tensor(object):
         `torch.sign(...)`_ : Compute the sign indication of input.
         `torch.sign(...)`_ : Compute the sign indication of input.
 
 
         """
         """
-        pass
 
 
     def sin(self):
     def sin(self):
         r"""Compute the sin.
         r"""Compute the sin.
@@ -1631,7 +1568,6 @@ class Tensor(object):
         `torch.sin(...)`_ : Compute the sin of input.
         `torch.sin(...)`_ : Compute the sin of input.
 
 
         """
         """
-        pass
 
 
     def size(self, axis=None):
     def size(self, axis=None):
         """Return the size of this tensor.
         """Return the size of this tensor.
@@ -1665,7 +1601,6 @@ class Tensor(object):
         `torch.sqrt(...)`_ : Compute the square root of input.
         `torch.sqrt(...)`_ : Compute the square root of input.
 
 
         """
         """
-        pass
 
 
     def sqrt_(self):
     def sqrt_(self):
         r"""Compute the square root.
         r"""Compute the square root.
@@ -1682,7 +1617,6 @@ class Tensor(object):
         `torch.sqrt(...)`_ : Compute the square root of input.
         `torch.sqrt(...)`_ : Compute the square root of input.
 
 
         """
         """
-        pass
 
 
     def squeeze(self, dim=None):
     def squeeze(self, dim=None):
         """Return a tensor with dimensions of size 1 removed.
         """Return a tensor with dimensions of size 1 removed.
@@ -1698,7 +1632,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def squeeze_(self, dim=None):
     def squeeze_(self, dim=None):
         """Inplace version of ``Tensor.squeeze()``.
         """Inplace version of ``Tensor.squeeze()``.
@@ -1714,7 +1647,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def sum(self, dim=None, keepdim=False):
     def sum(self, dim=None, keepdim=False):
         """Compute the sum value of elements along the given axis.
         """Compute the sum value of elements along the given axis.
@@ -1732,16 +1664,15 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
-    def sub(self, value):
+    def sub(self, other):
         r"""Compute the element-wise subtraction.
         r"""Compute the element-wise subtraction.
 
 
-        .. math:: \text{out} = \text{self} - \text{value}
+        .. math:: \text{out} = \text{self} - \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to subtract.
             The value to subtract.
 
 
         Returns
         Returns
@@ -1754,16 +1685,15 @@ class Tensor(object):
         `torch.sub(...)`_ : Compute the element-wise subtraction.
         `torch.sub(...)`_ : Compute the element-wise subtraction.
 
 
         """
         """
-        pass
 
 
-    def sub_(self, value):
+    def sub_(self, other):
         r"""Compute the element-wise subtraction.
         r"""Compute the element-wise subtraction.
 
 
-        .. math:: \text{self} \mathrel{-}= \text{value}
+        .. math:: \text{self} \mathrel{-}= \text{other}
 
 
         Parameters
         Parameters
         ----------
         ----------
-        value : Union[dragon.vm.torch.Tensor, number]
+        other : Union[dragon.vm.torch.Tensor, number]
             The value to be subtracted.
             The value to be subtracted.
 
 
         Returns
         Returns
@@ -1776,7 +1706,6 @@ class Tensor(object):
         `torch.sub(...)`_ : Compute the element-wise subtraction.
         `torch.sub(...)`_ : Compute the element-wise subtraction.
 
 
         """
         """
-        pass
 
 
     def type(self, dtype=None):
     def type(self, dtype=None):
         """Return the data type.
         """Return the data type.
@@ -1794,19 +1723,18 @@ class Tensor(object):
             The data type or new tensor.
             The data type or new tensor.
 
 
         """
         """
-        pass
 
 
     def uniform_(self, low=0, high=1):
     def uniform_(self, low=0, high=1):
-        r"""Fill self with a uniform distribution.
+        r"""Fill self from a uniform distribution.
 
 
-        .. math:: \text{self} \leftarrow U(\alpha, \beta)
+        .. math:: \text{self} \sim \mathcal{U}(\alpha, \beta)
 
 
         Parameters
         Parameters
         ----------
         ----------
         low : number, optional, default=0
         low : number, optional, default=0
-            The value of :math:`\alpha`.
+            The value to :math:`\alpha`.
         high : number, optional, default=1
         high : number, optional, default=1
-            The value of :math:`\beta`.
+            The value to :math:`\beta`.
 
 
         Returns
         Returns
         -------
         -------
@@ -1814,7 +1742,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def unsqueeze(self, dim):
     def unsqueeze(self, dim):
         """Return a tensor with dimensions of size 1 inserted.
         """Return a tensor with dimensions of size 1 inserted.
@@ -1830,7 +1757,6 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def unsqueeze_(self, dim):
     def unsqueeze_(self, dim):
         """In-place version of ``Tensor.unsqueeze()``.
         """In-place version of ``Tensor.unsqueeze()``.
@@ -1846,7 +1772,6 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        pass
 
 
     def view(self, *shape):
     def view(self, *shape):
         """Return a tensor with the same data but a different shape.
         """Return a tensor with the same data but a different shape.
@@ -1928,10 +1853,9 @@ class Tensor(object):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        pass
 
 
     def zero_(self):
     def zero_(self):
-        r"""Fill self with constant 0.
+        r"""Fill self with zeros.
 
 
         .. math:: \text{self} \leftarrow 0
         .. math:: \text{self} \leftarrow 0
 
 
@@ -1941,7 +1865,7 @@ class Tensor(object):
             The self.
             The self.
 
 
         """
         """
-        self.fill_(0)
+        return self.fill_(0)
 
 
     def _from_numpy(self, array, copy):
     def _from_numpy(self, array, copy):
         """Create impl from the numpy array."""
         """Create impl from the numpy array."""
@@ -1963,6 +1887,19 @@ class Tensor(object):
         return mapping.TENSOR_TYPE_TO_TORCH_TENSOR[self.dtype]
         return mapping.TENSOR_TYPE_TO_TORCH_TENSOR[self.dtype]
 
 
     def __add__(self, other):
     def __add__(self, other):
+        """Compute the element-wise addition.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to add.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.add(other)
         return self.add(other)
 
 
     def __del__(self):
     def __del__(self):
@@ -1971,63 +1908,215 @@ class Tensor(object):
             # PyGC will detect them automatically.
             # PyGC will detect them automatically.
             self._gc.collect(self.id)
             self._gc.collect(self.id)
 
 
-    def __div__(self, other):
-        return self.div(other)
-
     def __float__(self):
     def __float__(self):
-        """Return a float python scalar."""
-        if self.numel() == 1:
+        """Return a float python scalar.
+
+        Returns
+        -------
+        float
+            The float value.
+
+        """
         return float(self.numpy())
         return float(self.numpy())
-        raise TypeError('Only size-1 array can be converted to Python scalars.')
 
 
     def __ge__(self, other):
     def __ge__(self, other):
+        """Compute the element-wise greater-equal comparison.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to compare.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.ge(other)
         return self.ge(other)
 
 
     def __getitem__(self, item):
     def __getitem__(self, item):
-        pass
+        """Select elements at the specific index.
+
+        Parameters
+        ----------
+        item : Union[int, slice, dragon.vm.torch.Tensor]
+            The index.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
 
 
     def __gt__(self, other):
     def __gt__(self, other):
+        """Compute the element-wise greater comparison.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to compare.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.gt(other)
         return self.gt(other)
 
 
     def __hash__(self):
     def __hash__(self):
         return id(self)
         return id(self)
 
 
     def __iadd__(self, other):
     def __iadd__(self, other):
-        return self.add_(other)
+        """Compute the element-wise addition.
 
 
-    def __idiv__(self, other):
-        return self.div_(other)
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to add.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The self.
+
+        """
+        return self.add_(other)
 
 
     def __imul__(self, other):
     def __imul__(self, other):
+        """Compute the element-wise multiplication.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to multiply.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The self.
+
+        """
         return self.mul_(other)
         return self.mul_(other)
 
 
     def __int__(self):
     def __int__(self):
-        """Return a int python scalar."""
+        """Return an integer python scalar.
+
+        Returns
+        -------
+        int
+            The integer value.
+
+        """
         return int(self.__float__())
         return int(self.__float__())
 
 
     def __isub__(self, other):
     def __isub__(self, other):
+        """Compute the element-wise subtraction.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to be subtracted.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The self.
+
+        """
         return self.sub_(other)
         return self.sub_(other)
 
 
     def __itruediv__(self, other):
     def __itruediv__(self, other):
+        """Compute the element-wise division.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to be divided.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The self.
+
+        """
         return self.div_(other)
         return self.div_(other)
 
 
     def __le__(self, other):
     def __le__(self, other):
+        """Compute the element-wise less-equal comparison.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to compare.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.le(other)
         return self.le(other)
 
 
     def __lt__(self, other):
     def __lt__(self, other):
+        """Compute the element-wise less comparison.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to compare.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.lt(other)
         return self.lt(other)
 
 
     def __mul__(self, other):
     def __mul__(self, other):
+        """Compute the element-wise multiplication.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to multiply.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.mul(other)
         return self.mul(other)
 
 
     def __neg__(self):
     def __neg__(self):
+        """Compute the element-wise negative.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.neg()
         return self.neg()
 
 
     def __radd__(self, other):
     def __radd__(self, other):
-        pass
+        """Compute the element-wise addition.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to add.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
 
 
-    def __rdiv__(self, other):
-        pass
+        """
 
 
     def __repr__(self):
     def __repr__(self):
         np_data = self.numpy()
         np_data = self.numpy()
@@ -2044,61 +2133,146 @@ class Tensor(object):
         return format_str + meta_info
         return format_str + meta_info
 
 
     def __rmul__(self, other):
     def __rmul__(self, other):
-        pass
+        """Compute the element-wise multiplication.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to multiply.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
 
 
     def __rsub__(self, other):
     def __rsub__(self, other):
-        pass
+        """Compute the element-wise subtraction.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to be subtracted.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
 
 
     def __rtruediv__(self, other):
     def __rtruediv__(self, other):
-        pass
+        """Compute the element-wise division.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to be divided.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
 
 
     def __truediv__(self, other):
     def __truediv__(self, other):
+        """Compute the element-wise division.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to divide.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.div(other)
         return self.div(other)
 
 
     def __setitem__(self, key, value):
     def __setitem__(self, key, value):
-        pass
+        """Set elements at the specific index.
+
+        Parameters
+        ----------
+        key : Union[int, slice, dragon.vm.torch.Tensor]
+            The index.
+        value : Union[dragon.vm.torch.Tensor, number]
+            The value to set.
+
+        """
 
 
     def __sub__(self, other):
     def __sub__(self, other):
+        """Compute the element-wise subtraction.
+
+        Parameters
+        ----------
+        other : Union[dragon.vm.torch.Tensor, number]
+            The value to subtract.
+
+        Returns
+        -------
+        dragon.vm.torch.Tensor
+            The output tensor.
+
+        """
         return self.sub(other)
         return self.sub(other)
 
 
 
 
 class BoolTensor(object):
 class BoolTensor(object):
+    """The bool tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'bool'
         kwargs['dtype'] = 'bool'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class ByteTensor(object):
 class ByteTensor(object):
+    """The uint8 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'uint8'
         kwargs['dtype'] = 'uint8'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class CharTensor(object):
 class CharTensor(object):
+    """The int8 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'int8'
         kwargs['dtype'] = 'int8'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class DoubleTensor(object):
 class DoubleTensor(object):
+    """The float64 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'float64'
         kwargs['dtype'] = 'float64'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class FloatTensor(object):
 class FloatTensor(object):
+    """The float32 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'float32'
         kwargs['dtype'] = 'float32'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class HalfTensor(object):
 class HalfTensor(object):
+    """The float16 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'float16'
         kwargs['dtype'] = 'float16'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)
 
 
 
 
 class IntTensor(object):
 class IntTensor(object):
+    """The int32 tensor."""
+
     def __new__(cls, *args, **kwargs):
     def __new__(cls, *args, **kwargs):
         kwargs['dtype'] = 'int32'
         kwargs['dtype'] = 'int32'
         return Tensor(*args, **kwargs)
         return Tensor(*args, **kwargs)