Fixing some tiny faults in notes of classes' examples

mindspore/nn/graph_kernels/graph_kernels.py

@@ -366,7 +366,8 @@ class ReLU(GraphKernel):
         >>> relu = ReLU()
         >>> result = relu(input_x)
         >>> print(result)
-        [[0, 4.0, 0.0], [2.0, 0.0, 9.0]]
+        [[0. 4. 0.]
+         [2. 0. 9.]]
     """
     def __init__(self):
         super(ReLU, self).__init__()
@@ -685,7 +686,7 @@ class LogSoftmax(GraphKernel):
         >>> log_softmax = LogSoftmax()
         >>> result = log_softmax(input_x)
         >>> print(result)
-        [-4.4519143, -3.4519143, -2.4519143, -1.4519144, -0.4519144]
+        [-4.4519143 -3.4519143 -2.4519143 -1.4519144 -0.4519144]
     """
 
     def __init__(self, axis=-1):
@@ -743,7 +744,7 @@ class Tanh(GraphKernel):
         >>> tanh = Tanh()
         >>> result = tanh(input_x)
         >>> print(result)
-        [0.7615941, 0.9640276, 0.9950548, 0.9993293, 0.99990916]
+        [0.7615941  0.9640276  0.9950548  0.9993293  0.99990916]
     """
     def __init__(self):
         super(Tanh, self).__init__()

mindspore/nn/layer/activation.py

@@ -264,7 +264,7 @@ class LeakyReLU(Cell):
         >>> output = leaky_relu(input_x)
         >>> print(output)
         [[-0.2  4.  -1.6]
-         [ 2   -1.   9. ]]
+         [ 2.  -1.   9. ]]
     """
 
     def __init__(self, alpha=0.2):

mindspore/nn/layer/basic.py

@@ -748,8 +748,8 @@ class Triu(Cell):
         >>> triu = nn.Triu()
         >>> result = triu(x)
         >>> print(result)
-        [[1   2]
+        [[1 0]
-         [0   4]]
+         [3 4]]
     """
     def __init__(self):
         super(Triu, self).__init__()

mindspore/nn/layer/image.py

@@ -398,7 +398,7 @@ class PSNR(Cell):
         >>> img2 = Tensor(np.random.random((1,3,16,16)))
         >>> output = net(img1, img2)
         >>> print(output)
-        [7.7229595]
+        [7.915369]
     """
     def __init__(self, max_val=1.0):
         super(PSNR, self).__init__()

mindspore/nn/layer/pooling.py

@@ -182,7 +182,7 @@ class MaxPool1d(_PoolNd):
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4]), mindspore.float32)
         >>> output = max_pool(x)
         >>> result = output.shape
-        >>> printI(result)
+        >>> print(result)
         (1, 2, 2)
     """
 

mindspore/nn/learning_rate_schedule.py

@@ -148,7 +148,7 @@ class NaturalExpDecayLR(LearningRateSchedule):
         >>> natural_exp_decay_lr = NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
         >>> result = natural_exp_decay_lr(global_step)
         >>> print(result)
-        0.016529894
+        0.1
     """
     def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
         super(NaturalExpDecayLR, self).__init__()

mindspore/nn/loss/loss.py

@@ -599,7 +599,7 @@ class CosineEmbeddingLoss(_Loss):
         >>> cosine_embedding_loss = nn.CosineEmbeddingLoss()
         >>> output = cosine_embedding_loss(x1, x2, y)
         >>> print(output)
-        [0.0003426075]
+        0.0003426075
     """
     def __init__(self, margin=0.0, reduction="mean"):
         super(CosineEmbeddingLoss, self).__init__(reduction)

mindspore/nn/metrics/accuracy.py

@@ -42,7 +42,7 @@ class Accuracy(EvaluationBase):
         >>> metric.update(x, y)
         >>> accuracy = metric.eval()
         >>> print(accuracy)
-        0.66666666
+        0.6666666666666666
     """
     def __init__(self, eval_type='classification'):
         super(Accuracy, self).__init__(eval_type)

mindspore/ops/operations/math_ops.py

@@ -613,7 +613,7 @@ class ReduceProd(_Reduce):
         >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
         >>> op = ops.ReduceProd(keep_dims=True)
         >>> output = op(input_x, 1)
-        >>> reuslt = output.shape
+        >>> result = output.shape
         >>> print(result)
         (3, 1, 5, 6)
     """
@@ -2513,8 +2513,9 @@ class Equal(_LogicBinaryOp):
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
         >>> equal = ops.Equal()
-        >>> equal(input_x, 2.0)
+        >>> output = equal(input_x, 2.0)
-        [False, True, False]
+        >>> print(output)
+        Tensor(shape=[3], dtype=Bool, value= [False, True, False])
         >>>
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> input_y = Tensor(np.array([1, 2, 4]), mindspore.int32)

mindspore/ops/operations/other_ops.py

@@ -318,9 +318,9 @@ class IOU(PrimitiveWithInfer):
         >>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
         >>> output = iou(anchor_boxes, gt_boxes)
         >>> print(output)
-        [[65000. 65500.    -0.]
+        [[65500. 65500. 65500.]
-         [65000. 65500.    -0.]
+         [   -0.    -0.    -0.]
-         [    0.     0.     0.]]
+         [   -0.    -0.    -0.]]
 
     """
 
@@ -524,7 +524,7 @@ class ConfusionMatrix(PrimitiveWithInfer):
         >>> predictions = Tensor([1, 2, 1, 3], mindspore.int32)
         >>> output = confusion_matrix(labels, predictions)
         >>> print(output)
-        [[0 1 0 0
+        [[0 1 0 0]
          [0 1 1 0]
          [0 0 0 0]
          [0 0 0 1]]

mindspore/ops/operations/random_ops.py

@@ -420,7 +420,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         >>> print(result)
         (256, 2)
         >>> result = output_mask.shape
-        >>> print(reuslt)
+        >>> print(result)
         (256,)
     """
 
@@ -474,16 +474,16 @@ class RandomCategorical(PrimitiveWithInfer):
         >>> net = Net(8)
         >>> output = net(Tensor(x))
         >>> print(output)
-        [[0 2 1 3 4 2 0 2]
+        [[0 2 0 3 4 2 0 2]
-         [0 2 1 3 4 2 0 2]
-         [0 2 1 3 4 2 0 2]
          [0 2 1 3 4 2 0 2]
          [0 2 0 3 4 2 0 2]
-         [0 2 1 3 4 3 0 3]
          [0 2 1 3 4 2 0 2]
          [0 2 1 3 4 2 0 2]
          [0 2 1 3 4 2 0 2]
-         [0 2 0 3 4 2 0 2]]
+         [0 2 0 3 4 2 0 2]
+         [0 2 0 3 4 2 0 2]
+         [0 2 1 3 4 3 0 3]
+         [0 2 1 3 4 2 0 2]]
     """
 
     @prim_attr_register