fix bugs

mindspore/nn/layer/activation.py

@@ -408,7 +408,7 @@ class Sigmoid(Cell):
     Applies sigmoid-type activation element-wise.
 
     Sigmoid function is defined as:
-    :math:`\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)}`, where :math:`x_i` is the element of the input.
+    :math:`\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)}`,    where :math:`x_i` is the element of the input.
 
     Inputs:
         - **input_data** (Tensor) - The input of Tanh.

mindspore/nn/layer/basic.py

@@ -98,7 +98,7 @@ class Dropout(Cell):
     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/embedding.py

@@ -176,11 +176,8 @@ class EmbeddingLookup(Cell):
     Examples:
         >>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
         >>> result = nn.EmbeddingLookup(4,2)(input_indices)
-        >>> print(result)
-        [[[ 0.00856617  0.01039034]
-          [ 0.00196276 -0.00094072]]
-         [[ 0.01279703  0.00078912]
-          [ 0.00084863 -0.00742412]]]
+        >>> print(result.shape)
+        (2, 2, 2)
     """
     BATCH_SLICE = "batch_slice"
     FIELD_SLICE = "field_slice"
@@ -350,11 +347,8 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
         >>> field_ids = Tensor([[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]], mindspore.int32)
         >>> net = nn.MultiFieldEmbeddingLookup(10, 2, field_size=2, operator='SUM')
         >>> out = net(input_indices, input_values, field_ids)
-        >>> print(out)
-        [[[-0.00478983 -0.00772568]
-          [-0.00968955 -0.00064902]]
-         [[-0.01251151 -0.01251151]
-          [-0.00196387 -0.00196387]
+        >>> print(out.shape)
+        (2, 2, 2)
     """
     OPERATOR_SUM = 'SUM'
     OPERATOR_MEAN = 'MEAN'

mindspore/nn/layer/image.py

@@ -219,11 +219,12 @@ class SSIM(Cell):
 
     Examples:
         >>> net = nn.SSIM()
+        >>> np.random.seed(0)
         >>> img1 = Tensor(np.random.random((1, 3, 16, 16)), mindspore.float32)
         >>> img2 = Tensor(np.random.random((1, 3, 16, 16)), mindspore.float32)
         >>> output = net(img1, img2)
         >>> print(output)
-        [0.12174469]
+        [-0.15189075]
     """
     def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
         super(SSIM, self).__init__()

mindspore/nn/layer/math.py

@@ -315,6 +315,7 @@ class DiGamma(Cell):
         >>> input_x = Tensor(np.array([2, 3, 4]).astype(np.float32))
         >>> op = nn.DiGamma()
         >>> output = op(input_x)
+        >>> print(output)
         [0.42278463  0.92278427 1.2561178]
     """
 
@@ -661,7 +662,7 @@ class LBeta(Cell):
         >>> input_y = Tensor(np.array([2.0, 3.0, 14.0, 15.0]).astype(np.float32))
         >>> lbeta = nn.LBeta()
         >>> output = lbeta(input_y, input_x)
-        >>> print (output)
+        >>> print(output)
         [-1.7917596  -4.094345  -12.000229  -14.754799]
     """
 

mindspore/ops/operations/math_ops.py

@@ -716,6 +716,8 @@ class MatMul(PrimitiveWithInfer):
         >>> input_x2 = Tensor(np.ones(shape=[3, 4]), mindspore.float32)
         >>> matmul = ops.MatMul()
         >>> output = matmul(input_x1, input_x2)
+        >>> print(output)
+        [[3. 3. 3. 3.]]
     """
 
     @prim_attr_register
@@ -2368,6 +2370,8 @@ class Acosh(PrimitiveWithInfer):
         >>> acosh = ops.Acosh()
         >>> input_x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
         >>> output = acosh(input_x)
+        >>> print(output)
+        [0. 0.9624236 1.7627472 5.298292]
     """
 
     @prim_attr_register
@@ -2522,7 +2526,7 @@ class Equal(_LogicBinaryOp):
         >>> equal = ops.Equal()
         >>> output = equal(input_x, 2.0)
         >>> print(output)
-        Tensor(shape=[3], dtype=Bool, value= [False, True, False])
+        [False True False]
         >>>
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32)
@@ -3246,6 +3250,8 @@ class ACos(PrimitiveWithInfer):
         >>> acos = ops.ACos()
         >>> input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32)
         >>> output = acos(input_x)
+        >>> print(output)
+        [0.7377037 1.5307858 1.2661037 0.97641146]
     """
 
     @prim_attr_register
@@ -3360,12 +3366,15 @@ class NMSWithMask(PrimitiveWithInfer):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> bbox = np.random.rand(128, 5)
+        >>> bbox = np.array([[0.4, 0.2, 0.4, 0.3, 0.1], [0.4, 0.3, 0.6, 0.8, 0.7]])
         >>> bbox[:, 2] += bbox[:, 0]
         >>> bbox[:, 3] += bbox[:, 1]
         >>> inputs = Tensor(bbox, mindspore.float32)
         >>> nms = ops.NMSWithMask(0.5)
         >>> output_boxes, indices, mask = nms(inputs)
+        >>> print(output_boxes)
+        [[0.39990234 0.19995117 0.7998047  0.5        0.09997559]
+         [0.39990234 0.30004883 1.         1.0996094  0.7001953 ]]
     """
 
     @prim_attr_register
@@ -3520,6 +3529,7 @@ class Tan(PrimitiveWithInfer):
         >>> tan = ops.Tan()
         >>> input_x = Tensor(np.array([-1.0, 0.0, 1.0]), mindspore.float32)
         >>> output = tan(input_x)
+        >>> print(output)
         [-1.5574081 0. 1.5574081]
     """
 
@@ -3733,7 +3743,7 @@ class BitwiseOr(_BitwiseBinaryOp):
         >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
         >>> input_x2 = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
         >>> bitwise_or = ops.BitwiseOr()
-        >>> boutput = itwise_or(input_x1, input_x2)
+        >>> output = bitwise_or(input_x1, input_x2)
         >>> print(output)
         [ 0  1  1 -1 -1  3  3]
     """

mindspore/ops/operations/nn_ops.py

@@ -3159,16 +3159,12 @@ class LSTM(PrimitiveWithInfer):
         >>> print(output)
         [[[0.9640267  0.9640267 ]
           [0.9640267  0.9640267 ]]
-
          [[0.9950539  0.9950539 ]
           [0.9950539  0.9950539 ]]
-
          [[0.99932843 0.99932843]
           [0.99932843 0.99932843]]
-
          [[0.9999084  0.9999084 ]
           [0.9999084  0.9999084 ]]
-
          [[0.9999869  0.9999869 ]
           [0.9999869  0.9999869 ]]]
     """