!114 fix doc/comments issue merge from r0.1

Merge pull request !114 from 万万没想到/merge_from_r0.1

mindspore/nn/layer/activation.py

@@ -40,7 +40,7 @@ class Softmax(Cell):
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
     Args:
-        axis (Union[int, tuple[int]]): The axis to apply Softmax operation. Default: -1, means the last dimension.
+        axis (Union[int, tuple[int]]): The axis to apply Softmax operation, -1 means the last dimension. Default: -1.
 
     Inputs:
         - **x** (Tensor) - The input of Softmax.
@@ -70,7 +70,7 @@ class LogSoftmax(Cell):
     where :math:`x_{i}` is the :math:`i`-th slice along the given dim of the input Tensor.
 
     Args:
-        axis (int): The axis to apply LogSoftmax operation. Default: -1, means the last dimension.
+        axis (int): The axis to apply LogSoftmax operation, -1 means the last dimension. Default: -1.
 
     Inputs:
         - **x** (Tensor) - The input of LogSoftmax.

mindspore/nn/layer/basic.py

@@ -32,13 +32,13 @@ class Dropout(Cell):
     r"""
     Dropout layer for the input.
 
-    Randomly set some elements of the input tensor to zero with probability :math:`1 - keep_prob` during training
+    Randomly set some elements of the input tensor to zero with probability :math:`1 - keep\_prob` during training
     using samples from a Bernoulli distribution.
 
     Note:
         Each channel will be zeroed out independently on every construct call.
 
-        The outputs are scaled by a factor of :math:`\frac{1}{keep_prob}` during training so
+        The outputs are scaled by a factor of :math:`\frac{1}{keep\_prob}` during training so
         that the output layer remains at a similar scale. During inference, this
         layer returns the same tensor as the input.
 

mindspore/nn/layer/conv.py

@@ -241,7 +241,7 @@ class Conv2dTranspose(_Conv):
         in_channels (int): The number of channels in the input space.
         out_channels (int): The number of channels in the output space.
         kernel_size (Union[int, tuple]): int or tuple with 2 integers, which specifies the  height
-            and width of the 2D convolution window.Single int means the value if for both height and width of
+            and width of the 2D convolution window. Single int means the value is for both height and width of
             the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
             width of the kernel.
         stride (int): Specifies the same value for all spatial dimensions. Default: 1.

mindspore/nn/metrics/fbeta.py

@@ -26,8 +26,8 @@ class Fbeta(Metric):
     Fbeta score is a weighted mean of precison and recall.
 
     .. math::
-        F_\beta=\frac{(1+\beta^2) \cdot true positive}
+        F_\beta=\frac{(1+\beta^2) \cdot true\_positive}
-                {(1+\beta^2) \cdot true positive +\beta^2 \cdot false negative + false positive}
+                {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive}
 
     Args:
         beta (float): The weight of precision.
@@ -123,7 +123,7 @@ class F1(Fbeta):
     Refer to class `Fbeta` for more details.
 
     .. math::
-        F_\beta=\frac{2\cdot true positive}{2\cdot true positive + false negative + false positive}
+        F_\beta=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
 
     Examples:
         >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))

mindspore/ops/operations/array_ops.py

@@ -881,7 +881,7 @@ class ScalarToTensor(PrimitiveWithInfer):
     Inputs:
         - **input_x** (Union[int, float]) - The input is a scalar. Only constant value is allowed.
         - **dtype** (mindspore.dtype) - The target data type. Default: mindspore.float32. Only
-                    constant value is allowed.
+          constant value is allowed.
 
     Outputs:
         Tensor. 0-D Tensor and the content is the input.
@@ -1458,7 +1458,10 @@ class Select(PrimitiveWithInfer):
 
     Examples:
         >>> select = Select()
-        >>> select([True, False],[2,3],[1,2])
+        >>> input_x = Tensor([True, False])
+        >>> input_y = Tensor([2,3], mindspore.float32)
+        >>> input_z = Tensor([1,2], mindspore.float32)
+        >>> select(input_x, input_y, input_z)
     """
 
     @prim_attr_register

mindspore/ops/operations/comm_ops.py

@@ -66,11 +66,12 @@ class AllReduce(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.allreduce_sum = AllReduce(ReduceOp.SUM, group="nccl_world_group")
+        >>>         self.allreduce_sum = P.AllReduce(ReduceOp.SUM, group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.allreduce_sum(x)
@@ -130,11 +131,12 @@ class AllGather(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.allgather = AllGather(group="nccl_world_group")
+        >>>         self.allgather = P.AllGather(group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.allgather(x)
@@ -184,11 +186,12 @@ class ReduceScatter(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.reducescatter = ReduceScatter(ReduceOp.SUM, group="nccl_world_group")
+        >>>         self.reducescatter = P.ReduceScatter(ReduceOp.SUM, group="nccl_world_group")
         >>>
         >>>     def construct(self, x):
         >>>         return self.reducescatter(x)
@@ -246,11 +249,12 @@ class Broadcast(PrimitiveWithInfer):
 
     Examples:
         >>> from mindspore.communication.management import init
+        >>> import mindspore.ops.operations as P
         >>> init('nccl')
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.broadcast = Broadcast(1)
+        >>>         self.broadcast = P.Broadcast(1)
         >>>
         >>>     def construct(self, x):
         >>>         return self.broadcast((x,))

mindspore/ops/operations/control_ops.py

@@ -150,7 +150,6 @@ class Merge(PrimitiveWithInfer):
         raise NotImplementedError
 
     def infer_shape(self, inputs):
-        """merge select one input as its output"""
         return (inputs[0], [1])
 
     def infer_dtype(self, inputs):

mindspore/ops/operations/math_ops.py

@@ -1319,7 +1319,6 @@ class EqualCount(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
     def infer_shape(self, x_shape, w_shape):
-        """Infer shape."""
         output_shape = (1,)
         return output_shape
 

mindspore/ops/operations/nn_ops.py

@@ -1309,6 +1309,9 @@ class SGD(PrimitiveWithInfer):
     Nesterov momentum is based on the formula from On the importance of
     initialization and momentum in deep learning.
 
+    Note:
+        For details, please refer to `nn.SGD` source code.
+
     Args:
         dampening (float): The dampening for momentum. Default: 0.0.
         weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
@@ -1320,16 +1323,10 @@ class SGD(PrimitiveWithInfer):
         - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
         - **accum** (Tensor) - Accum(velocity) to be update.
         - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
-        - **stat** (Tensor) - States to be updated with the same shape as gradient. Default: 1.0.
+        - **stat** (Tensor) - States to be updated with the same shape as gradient.
 
     Outputs:
         Tensor, parameters to be update.
-
-    Examples:
-        >>> net = ResNet50()
-        >>> loss = SoftmaxCrossEntropyWithLogits()
-        >>> opt = SGD(params=net.trainable_params(), learning_rate=lr, momentum=0.9)
-        >>> model = Model(net, loss, opt)
     """
 
     @prim_attr_register
@@ -1919,7 +1916,7 @@ class LSTM(PrimitiveWithInfer):
     """
     Performs the long short term memory(LSTM) on the input.
 
-    Detailed information, please refer to `nn.layer.LSTM`.
+    Detailed information, please refer to `nn.LSTM`.
     """
 
     @prim_attr_register

mindspore/train/amp.py

@@ -91,13 +91,12 @@ def build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs):
         loss_fn (Union[None, Cell]): Definition of the loss_fn. If None, the `network` should have the loss inside.
             Default: None.
         optimizer (Optimizer): Optimizer to update the Parameter.
-        level (str): Supports [O0, O2].
+        level (str): Supports [O0, O2]. Default: "O0".
 
             - O0: Do not change.
             - O2: Cast network to float16, keep batchnorm and `loss_fn` (if set) run in float32,
               using dynamic loss scale.
 
-            Default: "O0"
         cast_model_type (:class:`mindspore.dtype`): Supports `mstype.float16` or `mstype.float32`.
             If set to `mstype.float16`, use `float16` mode to train. If set, overwrite the level setting.
         keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting.