fix bugs

mindspore/nn/layer/basic.py

@@ -258,9 +258,8 @@ class Dense(Cell):
         >>> input = Tensor(np.array([[180, 234, 154], [244, 48, 247]]), mindspore.float32)
         >>> net = nn.Dense(3, 4)
         >>> output = net(input)
-        >>> print(output)
-        [[ 1.1199665   1.6730378   -1.383349  -1.5148697 ]
-         [ 3.0728707   0.0124917   -1.4012015 0.04354739 ]]
+        >>> print(output.shape)
+        (2, 4)
     """
 
     @cell_attr_register(attrs=['has_bias', 'activation', 'in_channels', 'out_channels'])

mindspore/nn/layer/combined.py

@@ -154,7 +154,6 @@ class DenseBnAct(Cell):
         bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
             same as input. The values of str refer to the function `initializer`. Default: 'zeros'.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
-        activation (Cell): The regularization function applied to the output of the layer, eg. 'ReLU'. Default: None.
         has_bn (bool): Specifies to use batchnorm or not. Default: False.
         momentum (float): Momentum for moving average for batchnorm, must be [0, 1]. Default:0.9
         eps (float): Term added to the denominator to improve numerical stability for batchnorm, should be greater

mindspore/nn/layer/normalization.py

@@ -441,6 +441,7 @@ class GlobalBatchNorm(_BatchNorm):
         eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
         momentum (float): A floating hyperparameter of the momentum for the
             running_mean and running_var computation. Default: 0.9.
+        affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True.
         gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
             The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
             'he_uniform', etc. Default: 'ones'.

mindspore/nn/layer/quant.py

@@ -559,6 +559,9 @@ class Conv2dBnFoldQuant(Cell):
         padding (int): Implicit paddings on both sides of the input. Default: 0.
         eps (float): Parameters for BatchNormal. Default: 1e-5.
         momentum (float): Parameters for BatchNormal op. Default: 0.997.
+        dilation (int): Specifies the dilation rate to use for dilated convolution. Default: 1.
+        group (int): Splits filter into groups, `in_ channels` and `out_channels` must be
+            divisible by the number of groups. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
             convolution kernel. Default: 'normal'.

mindspore/nn/optim/lamb.py

@@ -244,7 +244,8 @@ class Lamb(Optimizer):
         >>> optim = nn.Lamb(params=net.trainable_params(), learning_rate=0.1)
         >>>
         >>> #2) Use parameter groups and set different values
-        >>> poly_decay_lr = learning_rate_schedule.PolynomialDecayLR()
+        >>> poly_decay_lr = learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1, end_learning_rate=0.01,
+        ...                                                    decay_steps=4, power = 0.5)
         >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
         >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},

mindspore/ops/operations/nn_ops.py

@@ -3018,7 +3018,7 @@ class GetNext(PrimitiveWithInfer):
         >>> relu = P.ReLU()
         >>> result = relu(data).asnumpy()
         >>> print(result.shape)
-        >>> (32, 1, 32, 32)
+        (32, 1, 32, 32)
     """
 
     @prim_attr_register