nn_notes

mindspore/nn/metrics/error.py

@@ -39,6 +39,8 @@ class MAE(Metric):
         >>> error.clear()
         >>> error.update(x, y)
         >>> result = error.eval()
+        >>> print(result)
+        0.037499990314245224
     """
     def __init__(self):
         super(MAE, self).__init__()

mindspore/nn/metrics/fbeta.py

@@ -36,10 +36,11 @@ class Fbeta(Metric):
         >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
         >>> y = Tensor(np.array([1, 0, 1]))
         >>> metric = nn.Fbeta(1)
+        >>> metric.clear()
         >>> metric.update(x, y)
         >>> fbeta = metric.eval()
         >>> print(fbeta)
-        [0.66666667  0.66666667]
+        [0.66666667 0.66666667]
     """
     def __init__(self, beta):
         super(Fbeta, self).__init__()
@@ -133,7 +134,7 @@ class F1(Fbeta):
         >>> metric.update(x, y)
         >>> result = metric.eval()
         >>> print(result)
-        [0.66666667  0.66666667]
+        [0.66666667 0.66666667]
     """
     def __init__(self):
         super(F1, self).__init__(1.0)

mindspore/nn/metrics/precision.py

@@ -47,6 +47,9 @@ class Precision(EvaluationBase):
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> precision = metric.eval()
+        >>> print(precision)
+        [0.5 1. ]
+
     """
     def __init__(self, eval_type='classification'):
         super(Precision, self).__init__(eval_type)

mindspore/nn/metrics/recall.py

@@ -47,6 +47,8 @@ class Recall(EvaluationBase):
         >>> metric.clear()
         >>> metric.update(x, y)
         >>> recall = metric.eval()
+        >>> print(recall)
+        [1. 0.5]
     """
     def __init__(self, eval_type='classification'):
         super(Recall, self).__init__(eval_type)

mindspore/nn/wrap/cell_wrapper.py

@@ -276,7 +276,7 @@ class GetNextSingleOp(Cell):
         >>> relu = P.ReLU()
         >>> result = relu(data).asnumpy()
         >>> print(result.shape)
-        >>> (32, 1, 32, 32)
+        (32, 1, 32, 32)
     """
 
     def __init__(self, dataset_types, dataset_shapes, queue_name):
@@ -356,6 +356,7 @@ class WithEvalCell(Cell):
     Args:
         network (Cell): The network Cell.
         loss_fn (Cell): The loss Cell.
+        add_cast_fp32 (bool): Adjust the data type to float32.
 
     Inputs:
         - **data** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
@@ -410,7 +411,7 @@ class ParameterUpdate(Cell):
         >>> param = network.parameters_dict()['weight']
         >>> update = nn.ParameterUpdate(param)
         >>> update.phase = "update_param"
-        >>> weight = Tensor(np.arrange(12).reshape((4, 3)), mindspore.float32)
+        >>> weight = Tensor(np.arange(12).reshape((4, 3)), mindspore.float32)
         >>> network_updata = update(weight)
     """