!15075 add print function in exmple

From: @lijiaqi0612
Reviewed-by: @kingxian,@kisnwang
Signed-off-by: @kingxian

mindspore/nn/metrics/auc.py

@@ -40,6 +40,7 @@ def auc(x, y, reorder=False):
         >>> metric.update(y_pred, y)
         >>> fpr, tpr, thre = metric.eval()
         >>> output = auc(fpr, tpr)
+        >>> print(output)
         0.5357142857142857
     """
     if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):

mindspore/nn/metrics/cosine_similarity.py

@@ -32,14 +32,15 @@ class CosineSimilarity(Metric):
         If sum or mean are used, then returns (b, 1) with the reduced value for each row
 
     Example:
-        >>> test_data = np.random.randn(4, 8)
+        >>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]])
         >>> metric = CosineSimilarity()
         >>> metric.clear()
         >>> metric.update(test_data)
         >>> square_matrix = metric.eval()
-        [[0. -0.14682831 0.19102288 -0.36204537]
-         ...
-         ]
+        >>> print(square_matrix)
+        [[0.  0.94025615  0.95162452]
+         [0.94025615  0.  0.86146098]
+         [0.95162452  0.86146098  0.]]
     """
     def __init__(self, similarity='cosine', reduction='none', zero_diagonal=True):
         super().__init__()

mindspore/nn/metrics/occlusion_sensitivity.py

@@ -64,6 +64,7 @@ class OcclusionSensitivity(Metric):
         >>> metric.clear()
         >>> metric.update(model, test_data, label)
         >>> score = metric.eval()
+        >>> print(score)
         [0.29999995    0.6    1    0.9]
     """
     def __init__(self, pad_val=0.0, margin=2, n_batch=128, b_box=None):

mindspore/nn/metrics/perplexity.py

@@ -39,6 +39,7 @@ class Perplexity(Metric):
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> perplexity = metric.eval()
+        >>> print(perplexity)
         2.231443166940565
     """
 

mindspore/nn/metrics/roc.py

@@ -38,8 +38,11 @@ class ROC(Metric):
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> fpr, tpr, thresholds = metric.eval()
+        >>> print(fpr)
         [0. 0. 0.33333333 0.6666667 1.]
+        >>> print(tpr)
         [0. 1. 1. 1. 1.]
+        >>> print(thresholds)
         [5 4 3 2 1]
         >>>
         >>> # 2) multiclass classification example
@@ -50,9 +53,12 @@ class ROC(Metric):
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> fpr, tpr, thresholds = metric.eval()
+        >>> print(fpr)
         [array([0., 0., 0.33333333, 0.66666667, 1.]), array([0., 0.33333333, 0.33333333, 1.]),
         array([0., 0.33333333, 1.]), array([0., 0., 1.])]
+        >>> print(tpr)
         [array([0., 1., 1., 1., 1.]), array([0., 0., 1., 1.]), array([0., 1., 1.]), array([0., 1., 1.])]
+        print(thresholds)
         [array([1.28, 0.28, 0.2, 0.1, 0.05]), array([1.55, 0.55, 0.2, 0.05]), array([1.15, 0.15, 0.05]),
         array([1.75, 0.75, 0.05])]
     """