fix bugs

mindspore/nn/layer/basic.py

@@ -364,13 +364,13 @@ class ClipByNorm(Cell):
     where :math:`L_2(X)` is the :math:`L_2`-norm of :math:`X`.
 
     Args:
-        axis (Union[None, int, tuple(int)): Compute the L2-norm along the Specific dimension.
+        axis (Union[None, int, tuple(int)]): Compute the L2-norm along the Specific dimension.
                                             Default: None, all dimensions to calculate.
 
     Inputs:
         - **input** (Tensor) - Tensor of shape N-D. The type must be float32 or float16.
         - **clip_norm** (Tensor) - A scalar Tensor of shape :math:`()` or :math:`(1)`.
-                                   Or a tensor shape can be broadcast to input shape.
+          Or a tensor shape can be broadcast to input shape.
 
     Outputs:
         Tensor, clipped tensor with the same shape as the input, whose type is float32.

mindspore/nn/layer/lstm.py

@@ -101,7 +101,7 @@ class LSTM(Cell):
           (num_directions * `num_layers`, batch_size, `hidden_size`).
 
     Supported Platforms:
-        ``GPU``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> net = nn.LSTM(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False)

mindspore/nn/learning_rate_schedule.py

@@ -87,7 +87,7 @@ class ExponentialDecayLR(LearningRateSchedule):
         >>> decay_rate = 0.9
         >>> decay_steps = 4
         >>> global_step = Tensor(2, mstype.int32)
-        >>> exponential_decay_lr = ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
+        >>> exponential_decay_lr = nn.ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
         >>> result = exponential_decay_lr(global_step)
         >>> print(result)
         0.09486833
@@ -145,7 +145,7 @@ class NaturalExpDecayLR(LearningRateSchedule):
         >>> decay_rate = 0.9
         >>> decay_steps = 4
         >>> global_step = Tensor(2, mstype.int32)
-        >>> natural_exp_decay_lr = NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
+        >>> natural_exp_decay_lr = nn.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
         >>> result = natural_exp_decay_lr(global_step)
         >>> print(result)
         0.1
@@ -204,10 +204,10 @@ class InverseDecayLR(LearningRateSchedule):
         >>> decay_rate = 0.9
         >>> decay_steps = 4
         >>> global_step = Tensor(2, mstype.int32)
-        >>> inverse_decay_lr = InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
+        >>> inverse_decay_lr = nn.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
         >>> result = inverse_decay_lr(global_step)
         >>> print(result)
-        0.06896552
+        0.1
     """
     def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
         super(InverseDecayLR, self).__init__()
@@ -252,7 +252,7 @@ class CosineDecayLR(LearningRateSchedule):
         >>> max_lr = 0.1
         >>> decay_steps = 4
         >>> global_steps = Tensor(2, mstype.int32)
-        >>> cosine_decay_lr = CosineDecayLR(min_lr, max_lr, decay_steps)
+        >>> cosine_decay_lr = nn.CosineDecayLR(min_lr, max_lr, decay_steps)
         >>> result = cosine_decay_lr(global_steps)
         >>> print(result)
         0.055
@@ -320,7 +320,7 @@ class PolynomialDecayLR(LearningRateSchedule):
         >>> decay_steps = 4
         >>> power = 0.5
         >>> global_step = Tensor(2, mstype.int32)
-        >>> polynomial_decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        >>> polynomial_decay_lr = nn.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
         >>> result = polynomial_decay_lr(global_step)
         >>> print(result)
         0.07363961
@@ -388,7 +388,7 @@ class WarmUpLR(LearningRateSchedule):
         >>> learning_rate = 0.1
         >>> warmup_steps = 2
         >>> global_step = Tensor(2, mstype.int32)
-        >>> warmup_lr = WarmUpLR(learning_rate, warmup_steps)
+        >>> warmup_lr = nn.WarmUpLR(learning_rate, warmup_steps)
         >>> result = warmup_lr(global_step)
         >>> print(result)
         0.1

mindspore/nn/loss/loss.py

@@ -303,9 +303,9 @@ class SampledSoftmaxLoss(_Loss):
         - **weights** (Tensor) - Tensor of shape (C, dim).
         - **bias** (Tensor) - Tensor of shape (C).  The class biases.
         - **labels** (Tensor) - Tensor of shape (N, num_true), type `int64, int32`. The
-            target classes.
-        - **inputs** (Tensor) - Tensor of shape (N, dim).  The forward activations of
-            the input network.
+          target classes.
+        - **inputs** (Tensor) - Tensor of shape (N, dim). The forward activations of
+          the input network.
 
     Outputs:
         Tensor, a tensor of shape (N) with the per-example sampled softmax losses.

mindspore/nn/optim/adam.py

@@ -273,7 +273,7 @@ class Adam(Optimizer):
         Tensor[bool], the value is True.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> net = Net()

mindspore/ops/operations/nn_ops.py

@@ -1295,7 +1295,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
     Examples:
         >>> input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32)
         >>> weight = Tensor(np.ones([1, 32, 3, 3]), mindspore.float32)
-        >>> depthwise_conv2d = ops.DepthwiseConv2dNative(channel_multiplier = 3, kernel_size = (3, 3))
+        >>> depthwise_conv2d = ops.DepthwiseConv2dNative(channel_multiplier=3, kernel_size=(3, 3))
         >>> output = depthwise_conv2d(input, weight)
         >>> print(output.shape)
         (10, 96, 30, 30)
@@ -2417,7 +2417,7 @@ class ApplyRMSProp(PrimitiveWithInfer):
         >>> input_x = Tensor(1., mindspore.float32)
         >>> mean_square = Tensor(2., mindspore.float32)
         >>> moment = Tensor(1., mindspore.float32)
-        >>> grad = Tensor(2., mindspore.float32 )
+        >>> grad = Tensor(2., mindspore.float32)
         >>> learning_rate = Tensor(0.9, mindspore.float32)
         >>> decay = 0.0
         >>> momentum = 1e-10
@@ -4611,7 +4611,7 @@ class ApplyAdadelta(PrimitiveWithInfer):
         >>> lr = Tensor(0.001, mstype.float32)
         >>> rho = Tensor(0.0, mstype.float32)
         >>> epsilon = Tensor(1e-6, mstype.float32)
-        >>> grad = Tensor(np.random.rand(1, 2).astype(np.float32))
+        >>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
         >>> output = net(lr, rho, epsilon, grad)
         >>> print(output)
         (Tensor(shape=[2, 2], dtype=Float32, value=
@@ -5863,7 +5863,7 @@ class SparseApplyFtrl(PrimitiveWithCheck):
         - **linear** (Tensor) - Tensor, has the same shape and data type as `linear`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> import mindspore