Fixing errors in classes' notes

mindspore/nn/layer/activation.py

@@ -159,7 +159,7 @@ class ELU(Cell):
         >>> elu = nn.ELU()
         >>> result = elu(input_x)
         >>> print(result)
-        [-0.63212055  -0.86466473  0  2  1]
+        [-0.63212055  -0.86466473  0.  2.  1.]
     """
 
     def __init__(self, alpha=1.0):
@@ -389,8 +389,8 @@ class FastGelu(Cell):
         >>> fast_gelu = nn.FastGelu()
         >>> output = fast_gelu(input_x)
         >>> print(output)
-        [[-1.5420423e-01  3.9955849e+00 -9.7664278e-06]
-         [ 1.9356585e+00 -1.0070159e-03  8.9999981e+00]]
+        [[-1.5420423e-01  3.9955850e+00 -9.7664279e-06]
+         [ 1.9356586e+00 -1.0070159e-03  8.9999981e+00]]
     """
 
     def __init__(self):

mindspore/nn/layer/math.py

@@ -712,7 +712,7 @@ class LBeta(Cell):
         >>> input_x = Tensor(np.array([2.0, 4.0, 6.0, 8.0]).astype(np.float32))
         >>> input_y = Tensor(np.array([2.0, 3.0, 14.0, 15.0]).astype(np.float32))
         >>> lbeta = nn.LBeta()
-        >>> output = lbeta(input_a, input_x)
+        >>> output = lbeta(input_y, input_x)
         >>> print (output)
         [-1.7917596  -4.094345  -12.000229  -14.754799]
     """

mindspore/nn/probability/bijector/gumbel_cdf.py

@@ -59,7 +59,7 @@ class GumbelCDF(Bijector):
         >>> y = Tensor([0.1, 0.2, 0.3], dtype=mindspore.float32)
         >>> ans1 = gumbel_cdf.forward(x)
         >>> print(ans1)
-        [0.36787945 0.54523915 0.6922006 ]
+        [0.36787945 0.5452392  0.69220066]
         >>> ans2 = gumbel_cdf.inverse(y)
         >>> print(ans2)
         [-0.66806495  0.04822993  0.62874645]

mindspore/nn/probability/bijector/scalar_affine.py

@@ -64,7 +64,7 @@ class ScalarAffine(Bijector):
         0.0
         >>> ans4 = scalaraffine.inverse_log_jacobian(value)
         >>> print(ans4)
-        -0.0
+        0.0
         ...
     """
 

mindspore/nn/probability/distribution/bernoulli.py

@@ -69,7 +69,7 @@ class Bernoulli(Distribution):
         >>> # Evaluate `prob` with respect to distribution b.
         >>> ans = b1.prob(value, probs_b)
         >>> print(ans)
-        [0.2 0.5 0.4]
+        [0.2 0.7 0.4]
         >>> # `probs` must be passed in during function calls.
         >>> ans = b2.prob(value, probs_a)
         >>> print(ans)

mindspore/nn/probability/distribution/cauchy.py

@@ -70,7 +70,7 @@ class Cauchy(Distribution):
         >>> # by replacing 'prob' by the name of the function
         >>> ans = cauchy1.prob(value)
         >>> print(ans)
-        [0.06366198 0.07489645 0.07957747]
+        [0.06366198 0.07489645 0.07957748]
         >>> # Evaluate with respect to distribution b.
         >>> ans = cauchy1.prob(value, loc_b, scale_b)
         >>> print(ans)

mindspore/nn/probability/distribution/exponential.py

@@ -71,7 +71,7 @@ class Exponential(Distribution):
         >>> # Evaluate with respect to distribution b.
         >>> ans = e1.prob(value, rate_b)
         >>> print(ans)
-        [0.16374613 0.18393973 0.12047768]
+        [0.16374615 0.18393973 0.12047768]
         >>> # `rate` must be passed in during function calls.
         >>> ans = e2.prob(value, rate_a)
         >>> print(ans)

mindspore/nn/probability/distribution/gumbel.py

@@ -64,7 +64,7 @@ class Gumbel(TransformedDistribution):
         >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
         >>> ans = gumbel.prob(value)
         >>> print(ans)
-        [0.07926048 0.08889321 0.09196986]
+        [0.07926048 0.08889319 0.09196986]
         >>> # Functions `mean`, `mode`, sd`, `var`, and `entropy` do not take in any argument.
         >>> ans = gumbel.mean()
         >>> print(ans)

mindspore/nn/probability/distribution/logistic.py

@@ -71,7 +71,7 @@ class Logistic(Distribution):
         >>> # by replacing 'prob' by the name of the function
         >>> ans = l1.prob(value)
         >>> print(ans)
-        [0.05875093 0.06153353 0.0625    ]
+        [0.05875093 0.06153352 0.0625    ]
         >>> # Evaluate with respect to distribution b.
         >>> ans = l1.prob(value, loc_b, scale_b)
         >>> print(ans)

mindspore/ops/composite/array_ops.py

@@ -71,10 +71,10 @@ def repeat_elements(x, rep, axis=0):
         >>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
         >>> output = C.repeat_elements(x, rep = 2, axis = 0)
         >>> print(output)
-        [[0, 1, 2],
-         [0, 1, 2],
-         [3, 4, 5],
-         [3, 4, 5]],
+        [[0 1 2]
+         [0 1 2]
+         [3 4 5]
+         [3 4 5]]
     """
     const_utils.check_valid_type(F.dtype(x), mstype.number_type, 'input x')
     rep = _check_positive_int(rep, "rep", "repeat_elements")

mindspore/ops/composite/math_ops.py

@@ -186,9 +186,9 @@ def tensor_dot(x1, x2, axes):
         >>> input_x2 = Tensor(np.ones(shape=[3, 1, 2]), mindspore.float32)
         >>> output = C.tensor_dot(input_x1, input_x2, ((0,1),(1,2)))
         >>> print(output)
-        [[2,2,2],
-         [2,2,2],
-         [2,2,2]]
+        [[2. 2. 2]
+         [2. 2. 2]
+         [2. 2. 2]]
     """
     shape_op = P.Shape()
     reshape_op = P.Reshape()