!126 resolve some issues in nn comments

Merge pull request !126 from zhongligeng/master

mindspore/nn/layer/basic.py

@@ -65,7 +65,7 @@ class Dropout(Cell):
         Tensor, output tensor with the same shape as the input.
 
     Examples:
-        >>> x = mindspore.Tensor(np.ones([20, 16, 50]), mindspore.float32)
+        >>> x = Tensor(np.ones([20, 16, 50]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
         >>> net(x)
     """
@@ -111,7 +111,7 @@ class Flatten(Cell):
 
     Examples:
         >>> net = nn.Flatten()
-        >>> input = mindspore.Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
+        >>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32)
         >>> input.shape()
         (2, 2, 2)
         >>> net(input)
@@ -149,9 +149,6 @@ class Dense(Cell):
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
         activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
 
-    Returns:
-        Tensor, output tensor.
-
     Raises:
         ValueError: If weight_init or bias_init shape is incorrect.
 
@@ -163,7 +160,7 @@ class Dense(Cell):
 
     Examples:
         >>> net = nn.Dense(3, 4)
-        >>> input = mindspore.Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
         >>> net(input)
         [[ 2.5246444   2.2738023   0.5711005  -3.9399147 ]
          [ 1.0739875   4.0155234   0.94188046 -5.459526  ]]
@@ -243,8 +240,8 @@ class ClipByNorm(Cell):
 
     Examples:
         >>> net = nn.ClipByNorm()
-        >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
-        >>> clip_norm = mindspore.Tensor(np.array([100]).astype(np.float32))
+        >>> clip_norm = Tensor(np.array([100]).astype(np.float32))
         >>> net(input, clip_norm)
 
     """
@@ -290,9 +287,6 @@ class Norm(Cell):
         keep_dims (bool): If True, the axis indicated in `axis` are kept with size 1. Otherwise,
                    the dimensions in `axis` are removed from the output shape. Default: False.
 
-    Returns:
-        Tensor, a Tensor of the same type as input, containing the vector or matrix norms.
-
     Inputs:
         - **input** (Tensor) - Tensor which is not empty.
 
@@ -302,7 +296,7 @@ class Norm(Cell):
 
     Examples:
         >>> net = nn.Norm(axis=0)
-        >>> input = mindspore.Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
         >>> net(input)
     """
     def __init__(self, axis=(), keep_dims=False):
@@ -344,7 +338,8 @@ class OneHot(Cell):
                           when indices[j] = i. Default: 1.0.
         off_value (float): A scalar defining the value to fill in output[i][j]
                            when indices[j] != i. Default: 0.0.
-        dtype (:class:`mindspore.dtype`): Default: mindspore.float32.
+        dtype (:class:`mindspore.dtype`): Data type of 'on_value' and 'off_value', not the
+                                          data type of indices. Default: mindspore.float32.
 
     Inputs:
         - **indices** (Tensor) - A tensor of indices of data type mindspore.int32 and arbitrary shape.
@@ -355,7 +350,7 @@ class OneHot(Cell):
 
     Examples:
         >>> net = nn.OneHot(depth=4, axis=1)
-        >>> indices = mindspore.Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
+        >>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32)
         >>> net(indices)
         [[[0. 0.]
           [1. 0.]

mindspore/nn/layer/container.py

@@ -86,7 +86,7 @@ class SequentialCell(Cell):
         >>> relu = nn.ReLU()
         >>> seq = nn.SequentialCell([conv, bn, relu])
         >>>
-        >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
+        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
         >>> seq(x)
         [[[[0.02531557 0.        ]
            [0.04933941 0.04880078]]
@@ -138,7 +138,6 @@ class SequentialCell(Cell):
         return len(self._cells)
 
     def construct(self, input_data):
-        """Processes the input with the defined sequence of Cells."""
         for cell in self.cell_list:
             input_data = cell(input_data)
         return input_data
@@ -161,7 +160,7 @@ class CellList(_CellListBase, Cell):
         >>> cell_ls = nn.CellList([bn])
         >>> cell_ls.insert(0, conv)
         >>> cell_ls.append(relu)
-        >>> x = mindspore.Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
+        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
         >>> # not same as nn.SequentialCell, `cell_ls(x)` is not correct
         >>> cell_ls
         CellList< (0): Conv2d<input_channels=100, ..., bias_init=None>

mindspore/nn/layer/conv.py

@@ -146,9 +146,6 @@ class Conv2d(_Conv):
             Initializer and string are the same as 'weight_init'. Refer to the values of
             Initializer for more details. Default: 'zeros'.
 
-    Returns:
-        Tensor, output tensor.
-
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
 
@@ -157,7 +154,7 @@ class Conv2d(_Conv):
 
     Examples:
         >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
-        >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
+        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
         >>> net(input).shape()
         (1, 240, 1024, 640)
     """
@@ -277,7 +274,7 @@ class Conv2dTranspose(_Conv):
 
     Examples:
         >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal')
-        >>> input = Tensor(np.ones([1, 3, 16, 50]), mstype.float32)
+        >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
         >>> net(input)
         """
     def __init__(self,

mindspore/nn/layer/embedding.py

@@ -50,7 +50,7 @@ class Embedding(Cell):
 
     Examples:
         >>> net = nn.Embedding(20000, 768,  True)
-        >>> input_data = mindspore.Tensor(np.ones([8, 128]), mindspore.int32)
+        >>> input_data = Tensor(np.ones([8, 128]), mindspore.int32)
         >>>
         >>> # Maps the input word IDs to word embedding.
         >>> output = net(input_data)

mindspore/nn/layer/lstm.py

@@ -96,9 +96,9 @@ class LSTM(Cell):
         >>>         return self.lstm(inp, (h0, c0))
         >>>
         >>> net = LstmNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False)
-        >>> input = mindspore.Tensor(np.ones([3, 5, 10]).astype(np.float32))
+        >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32))
-        >>> h0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
+        >>> h0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
-        >>> c0 = mindspore.Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
+        >>> c0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
         >>> output, (hn, cn) = net(input, h0, c0)
     """
     def __init__(self,

mindspore/nn/layer/normalization.py

@@ -159,7 +159,7 @@ class BatchNorm1d(_BatchNorm):
 
     Examples:
         >>> net = nn.BatchNorm1d(num_features=16)
-        >>> input = mindspore.Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32)
+        >>> input = Tensor(np.random.randint(0, 255, [3, 16]), mindspore.float32)
         >>> net(input)
     """
     def _check_data_dim(self, x):
@@ -258,7 +258,7 @@ class LayerNorm(Cell):
     Examples:
         >>> x = Tensor(np.ones([20, 5, 10, 10], np.float32))
         >>> shape1 = x.shape()[1:]
-        >>> m = LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
+        >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
         >>> m(x)
     """
     def __init__(self,

mindspore/nn/layer/pooling.py

@@ -63,8 +63,8 @@ class MaxPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over.
+        kernel_size (int): Size of the window to take a max over. Default 1.
-        stride (int): Stride size of the window. Default: None.
+        stride (int): Stride size of the window. Default: 1.
         pad_mode (str): Select the mode of the pad. The optional values are
             "same" and "valid". Default: "valid".
 
@@ -75,7 +75,7 @@ class MaxPool2d(_PoolNd):
 
             - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
               without padding. Extra pixels will be discarded.
-        padding (int): Now is not supported, mplicit zero padding to be added on both sides. Default: 0.
+        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -85,7 +85,7 @@ class MaxPool2d(_PoolNd):
 
     Examples:
         >>> pool = MaxPool2d(kernel_size=3, stride=1)
-        >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[1. 5. 5. 1.]
            [0. 3. 4. 8.]
            [4. 2. 7. 6.]
@@ -149,8 +149,8 @@ class AvgPool2d(_PoolNd):
         pad_mode for training only supports "same" and "valid".
 
     Args:
-        kernel_size (int): Size of the window to take a max over.
+        kernel_size (int): Size of the window to take a max over. Default: 1.
-        stride (int): Stride size of the window. Default: None.
+        stride (int): Stride size of the window. Default: 1.
         pad_mode (str): Select the mode of the pad. The optional values are
             "same", "valid". Default: "valid".
 
@@ -161,7 +161,7 @@ class AvgPool2d(_PoolNd):
 
             - valid: Adopts the way of discarding. The possibly largest height and width of output will be return
               without padding. Extra pixels will be discarded.
-        padding (int): Now is not supported, implicit zero padding to be added on both sides. Default: 0.
+        padding (int): Implicit zero padding to be added on both sides. Default: 0.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
@@ -171,7 +171,7 @@ class AvgPool2d(_PoolNd):
 
     Examples:
         >>> pool = AvgPool2d(kernel_size=3, stride=1)
-        >>> x = mindspore.Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
+        >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]
             [2. 7. 1. 2.]

mindspore/nn/loss/loss.py

@@ -86,9 +86,9 @@ class L1Loss(_Loss):
         Tensor, loss float tensor.
 
     Examples:
-        >>> loss = L1Loss()
+        >>> loss = nn.L1Loss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def __init__(self, reduction='mean'):
@@ -126,9 +126,9 @@ class MSELoss(_Loss):
         Tensor, weighted loss float tensor.
 
     Examples:
-        >>> loss = MSELoss()
+        >>> loss = nn.MSELoss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def construct(self, base, target):
@@ -171,9 +171,9 @@ class SmoothL1Loss(_Loss):
         Tensor, loss float tensor.
 
     Examples:
-        >>> loss = SmoothL1Loss()
+        >>> loss = nn.SmoothL1Loss()
-        >>> input_data = Tensor(np.array([1, 2, 3]), mstype.float32)
+        >>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
-        >>> target_data = Tensor(np.array([1, 2, 2]), mstype.float32)
+        >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
     def __init__(self, sigma=1.0):
@@ -219,17 +219,16 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
     Inputs:
         - **logits** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
         - **labels** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`. If `sparse` is True, The type of
-          `labels` is mstype.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`.
+          `labels` is mindspore.int32. If `sparse` is False, the type of `labels` is same as the type of `logits`.
 
     Outputs:
         Tensor, a tensor of the same shape as logits with the component-wise
         logistic losses.
 
     Examples:
-        >>> loss = SoftmaxCrossEntropyWithLogits(sparse=True)
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
-        >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mstype.float32)
+        >>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32)
-        >>> labels_np = np.zeros([1, 10]).astype(np.int32)
+        >>> labels_np = np.ones([1,]).astype(np.int32)
-        >>> labels_np[0][0] = 1
         >>> labels = Tensor(labels_np)
         >>> loss(logits, labels)
     """
@@ -286,8 +285,8 @@ class SoftmaxCrossEntropyExpand(Cell):
 
     Examples:
         >>> loss = SoftmaxCrossEntropyExpand(sparse=True)
-        >>> input_data = Tensor(np.ones([64, 512]), dtype=mstype.float32)
+        >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
-        >>> label = Tensor(np.ones([64]), dtype=mstype.int32)
+        >>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
         >>> loss(input_data, label)
     """
     def __init__(self, sparse=False):

mindspore/nn/metrics/accuracy.py

@@ -35,8 +35,8 @@ class Accuracy(EvaluationBase):
             Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
         >>> metric = nn.Accuracy('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
@@ -58,13 +58,14 @@ class Accuracy(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
-                index is used in 'classification' evaluation type.
+                are both :math:`(N, C)`.
 
         Raises:
             ValueError: If the number of the input is not 2.

mindspore/nn/metrics/error.py

@@ -33,8 +33,8 @@ class MAE(Metric):
         The method `update` must be called with the form `update(y_pred, y)`.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
+        >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
+        >>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
         >>> error = nn.MAE()
         >>> error.clear()
         >>> error.update(x, y)
@@ -95,8 +95,8 @@ class MSE(Metric):
     where :math:`n` is batch size.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
+        >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
+        >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
         >>> error = MSE()
         >>> error.clear()
         >>> error.update(x, y)

mindspore/nn/metrics/fbeta.py

@@ -33,12 +33,11 @@ class Fbeta(Metric):
         beta (float): The weight of precision.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Fbeta(1)
         >>> metric.update(x, y)
         >>> fbeta = metric.eval()
-        [0.66666667 0.66666667]
     """
     def __init__(self, beta):
         super(Fbeta, self).__init__()
@@ -64,7 +63,7 @@ class Fbeta(Metric):
                 `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
                 is the number of categories. y contains values of integers. The shape is :math:`(N, C)`
-                if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used.
+                if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used.
         """
         if len(inputs) != 2:
             raise ValueError('Fbeta need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
@@ -126,8 +125,8 @@ class F1(Fbeta):
         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]]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.F1()
         >>> metric.update(x, y)
         >>> fbeta = metric.eval()

mindspore/nn/metrics/loss.py

@@ -25,12 +25,11 @@ class Loss(Metric):
         loss = \frac{\sum_{k=1}^{n}loss_k}{n}
 
     Examples:
-        >>> x = mindspore.Tensor(np.array(0.2), mindspore.float32)
+        >>> x = Tensor(np.array(0.2), mindspore.float32)
         >>> loss = nn.Loss()
         >>> loss.clear()
         >>> loss.update(x)
         >>> result = loss.eval()
-        0.20000000298023224
     """
     def __init__(self):
         super(Loss, self).__init__()

mindspore/nn/metrics/precision.py

@@ -41,13 +41,12 @@ class Precision(EvaluationBase):
                          multilabel. Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Precision('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> precision = metric.eval()
-        [0.5 1. ]
     """
     def __init__(self, eval_type='classification'):
         super(Precision, self).__init__(eval_type)
@@ -72,13 +71,14 @@ class Precision(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
-                index is used in 'classification' evaluation type.
+                are both :math:`(N, C)`.
 
         Raises:
             ValueError: If the number of input is not 2.

mindspore/nn/metrics/recall.py

@@ -41,13 +41,12 @@ class Recall(EvaluationBase):
                          multilabel. Default: 'classification'.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+        >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
-        >>> y = mindspore.Tensor(np.array([1, 0, 1]))
+        >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Recall('classification')
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> recall = metric.eval()
-        [1.  0.5]
     """
     def __init__(self, eval_type='classification'):
         super(Recall, self).__init__(eval_type)
@@ -72,13 +71,14 @@ class Recall(EvaluationBase):
 
         Args:
             inputs: Input `y_pred` and `y`. `y_pred` and `y` are a `Tensor`, a list or an array.
-                `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
+                For 'classification' evaluation type, `y_pred` is in most cases (not strictly) a list
+                of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
-                is the number of categories. For 'multilabel' evaluation type, `y_pred` can only be one-hot
+                is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
-                encoding with values 0 or 1. Indices with 1 indicate positive category. `y` contains values
+                encoding is used or the shape is :math:`(N,)` with integer values if index of category is used.
-                of integers. The shape is :math:`(N, C)` if one-hot encoding is used. One-hot encoding
+                For 'multilabel' evaluation type, `y_pred` and `y` can only be one-hot encoding with
-                should be used when 'eval_type' is 'multilabel'. Shape can also be :math:`(N, 1)` if category
+                values 0 or 1. Indices with 1 indicate positive category. The shape of `y_pred` and `y`
-                index is used in 'classification' evaluation type.
+                are both :math:`(N, C)`.
 
 
         Raises:

mindspore/nn/metrics/topk.py

@@ -33,14 +33,13 @@ class TopKCategoricalAccuracy(Metric):
         ValueError: If `k` is less than 1.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.TopKCategoricalAccuracy(3)
         >>> topk.clear()
         >>> topk.update(x, y)
         >>> result = topk.eval()
-        0.6666666666666666
     """
     def __init__(self, k):
         super(TopKCategoricalAccuracy, self).__init__()
@@ -65,7 +64,7 @@ class TopKCategoricalAccuracy(Metric):
                 y_pred is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
                 is the number of categories. y contains values of integers. The shape is :math:`(N, C)`
-                if one-hot encoding is used. Shape can also be :math:`(N, 1)` if category index is used.
+                if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used.
         """
         if len(inputs) != 2:
             raise ValueError('Topk need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
@@ -98,9 +97,9 @@ class Top1CategoricalAccuracy(TopKCategoricalAccuracy):
     Refer to class 'TopKCategoricalAccuracy' for more details.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.Top1CategoricalAccuracy()
         >>> topk.clear()
         >>> topk.update(x, y)
@@ -116,9 +115,9 @@ class Top5CategoricalAccuracy(TopKCategoricalAccuracy):
     Refer to class 'TopKCategoricalAccuracy' for more details.
 
     Examples:
-        >>> x = mindspore.Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
+        >>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
         >>>            [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
-        >>> y = mindspore.Tensor(np.array([2, 0, 1]), mindspore.float32)
+        >>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
         >>> topk = nn.Top5CategoricalAccuracy()
         >>> topk.clear()
         >>> topk.update(x, y)

mindspore/nn/optim/adam.py

@@ -161,7 +161,7 @@ class Adam(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Adam(params=net.trainable_params())
+        >>> optim = nn.Adam(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
 
@@ -252,7 +252,7 @@ class AdamWeightDecay(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = AdamWeightDecay(params=net.trainable_params())
+        >>> optim = nn.AdamWeightDecay(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
    """
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
@@ -306,7 +306,7 @@ class AdamWeightDecayDynamicLR(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10)
+        >>> optim = nn.AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self,

mindspore/nn/optim/ftrl.py

@@ -87,7 +87,7 @@ class FTRL(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = FTRL(net.trainable_params())
+        >>> opt = nn.FTRL(net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
     """
     def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,

mindspore/nn/optim/lamb.py

@@ -163,7 +163,7 @@ class Lamb(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Lamb(params=net.trainable_params(), decay_steps=10)
+        >>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
 

mindspore/nn/optim/lars.py

@@ -90,8 +90,8 @@ class LARS(Cell):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = Momentum(net.trainable_params(), 0.1, 0.9)
+        >>> opt = nn.Momentum(net.trainable_params(), 0.1, 0.9)
-        >>> opt_lars = LARS(opt, epsilon=1e-08, hyperpara=0.02)
+        >>> opt_lars = nn.LARS(opt, epsilon=1e-08, hyperpara=0.02)
         >>> model = Model(net, loss_fn=loss, optimizer=opt_lars, metrics=None)
     """
 

mindspore/nn/optim/momentum.py

@@ -83,7 +83,7 @@ class Momentum(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0,

mindspore/nn/optim/rmsprop.py

@@ -132,7 +132,7 @@ class RMSProp(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
+        >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
         >>> model = Model(net, loss, opt)
     """
     def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,

mindspore/nn/optim/sgd.py

@@ -77,7 +77,7 @@ class SGD(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = SGD(params=net.trainable_params())
+        >>> optim = nn.SGD(params=net.trainable_params())
         >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
     def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,

mindspore/nn/wrap/cell_wrapper.py

@@ -50,8 +50,8 @@ class WithLossCell(Cell):
         >>> net_with_criterion = nn.WithLossCell(net, loss_fn)
         >>>
         >>> batch_size = 2
-        >>> data = mindspore.Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
+        >>> data = Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
-        >>> label = mindspore.Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32))
+        >>> label = Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32))
         >>>
         >>> net_with_criterion(data, label)
     """
@@ -62,16 +62,6 @@ class WithLossCell(Cell):
         self._loss_fn = loss_fn
 
     def construct(self, data, label):
-        """
-        Computes loss based on the wrapped loss cell.
-
-        Args:
-            data (Tensor): Tensor data to train.
-            label (Tensor): Tensor label data.
-
-        Returns:
-            Tensor, compute result.
-        """
         out = self._backbone(data)
         return self._loss_fn(out, label)
 
@@ -137,19 +127,6 @@ class WithGradCell(Cell):
         self.network_with_loss.set_train()
 
     def construct(self, data, label):
-        """
-        Computes gradients based on the wrapped gradients cell.
-
-        Note:
-            Run in PyNative mode.
-
-        Args:
-            data (Tensor): Tensor data to train.
-            label (Tensor): Tensor label data.
-
-        Returns:
-            Tensor, return compute gradients.
-        """
         weights = self.weights
         if self.sens is None:
             grads = self.grad(self.network_with_loss, weights)(data, label)
@@ -355,7 +332,7 @@ class ParameterUpdate(Cell):
         >>> param = network.parameters_dict()['learning_rate']
         >>> update = nn.ParameterUpdate(param)
         >>> update.phase = "update_param"
-        >>> lr = mindspore.Tensor(0.001, mindspore.float32)
+        >>> lr = Tensor(0.001, mindspore.float32)
         >>> update(lr)
     """
 

mindspore/nn/wrap/grad_reducer.py

@@ -120,25 +120,36 @@ class DistributedGradReducer(Cell):
         ValueError: If degree is not a int or less than 0.
 
     Examples:
-        >>> from mindspore.communication import get_group_size
+        >>> from mindspore.communication import init, get_group_size
         >>> from mindspore.ops import composite as C
         >>> from mindspore.ops import operations as P
         >>> from mindspore.ops import functional as F
         >>> from mindspore import context
+        >>> from mindspore import nn
+        >>> from mindspore import ParallelMode, ParameterTuple
+        >>>
+        >>> device_id = int(os.environ["DEVICE_ID"])
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
+        >>>                     device_id=int(device_id), enable_hccl=True)
+        >>> init()
+        >>> context.reset_auto_parallel_context()
+        >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
+        >>>
         >>>
         >>> class TrainingWrapper(nn.Cell):
         >>>     def __init__(self, network, optimizer, sens=1.0):
         >>>         super(TrainingWrapper, self).__init__(auto_prefix=False)
         >>>         self.network = network
-        >>>         self.weights = mindspore.ParameterTuple(network.trainable_params())
+        >>>         self.network.add_flags(defer_inline=True)
+        >>>         self.weights = ParameterTuple(network.trainable_params())
         >>>         self.optimizer = optimizer
         >>>         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         >>>         self.sens = sens
         >>>         self.reducer_flag = False
         >>>         self.grad_reducer = None
         >>>         self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
-        >>>         if self.parallel_mode in [mindspore.ParallelMode.DATA_PARALLEL,
+        >>>         if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
-        >>>                                            mindspore.ParallelMode.HYBRID_PARALLEL]:
+        >>>                                            ParallelMode.HYBRID_PARALLEL]:
         >>>             self.reducer_flag = True
         >>>         if self.reducer_flag:
         >>>             mean = context.get_auto_parallel_context("mirror_mean")
@@ -161,8 +172,8 @@ class DistributedGradReducer(Cell):
         >>> network = Net()
         >>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> train_cell = TrainingWrapper(network, optimizer)
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
         >>> grads = train_cell(inputs, label)
     """
 

mindspore/nn/wrap/loss_scale.py

@@ -65,9 +65,10 @@ class DynamicLossScaleUpdateCell(Cell):
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> output = train_network(inputs, label)
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> output = train_network(inputs, label, scaling_sens)
     """
 
     def __init__(self,
@@ -126,13 +127,14 @@ class FixedLossScaleUpdateCell(Cell):
     Examples:
         >>> net_with_loss = Net()
         >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
-        >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
+        >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> output = train_network(inputs, label)
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> output = train_network(inputs, label, scaling_sens)
     """
 
     def __init__(self, loss_scale_value):
@@ -181,9 +183,9 @@ class TrainOneStepWithLossScaleCell(Cell):
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
         >>> train_network.set_train()
         >>>
-        >>> inputs = mindspore.Tensor(np.ones([16, 16]).astype(np.float32))
+        >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
-        >>> label = mindspore.Tensor(np.zeros([16, 16]).astype(np.float32))
+        >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
-        >>> scaling_sens = mindspore.Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
+        >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
         >>> output = train_network(inputs, label, scaling_sens)
     """