diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py
index 3c8b32b2a48..a52a0b860bb 100644
--- a/mindspore/python/mindspore/ops/function/nn_func.py
+++ b/mindspore/python/mindspore/ops/function/nn_func.py
@@ -827,7 +827,8 @@ def interpolate(x, roi=None, scales=None, sizes=None, coordinate_transformation_
         resize_bilinear_inner = _get_cache_prim(IMG.ResizeBilinearV2)(align_corners, half_pixel_centers)
         return resize_bilinear_inner(x, output_size)
 
-    raise ValueError("Input Error: For interpolate,  {} mode is not support now".format(mode))
+    raise TypeError(
+        "Input Error: For interpolate,  {} mode is not support now".format(mode))
 
 
 def softsign(x):
@@ -1688,8 +1689,8 @@ def mish(x):
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> output = ops.mish(input_x)
         >>> print(output)
-        [[-0.3034014  3.9974129 -0.00026832]
-         [ 1.9439590  -0.0033576 9.0000000]]
+        [[-3.0340147e-01  3.9974129e+00 -2.68311895e-03]
+         [ 1.9439590e+00  -3.3576239e-02 8.99999990e+00]]
     """
     return mish_(x)
 
@@ -1818,7 +1819,7 @@ def grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zero
     Examples:
         >>> input_x = Tensor(np.arange(16).reshape((2, 2, 2, 2)).astype(np.float32))
         >>> grid = Tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(np.float32))
-        >>> output = ops.grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
+        >>> output = grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
                                      align_corners=True)
         >>> print(output)
         [[[[ 1.9      ]
@@ -1942,8 +1943,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
         >>> inputs = Tensor(np.array([[[0.6, 0.4, 0.2], [0.8, 0.6, 0.3]],
         ...                           [[0.0, 0.6, 0.0], [0.5, 0.4, 0.5]]]), mindspore.float32)
         >>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
-        >>> decoded_indices, decoded_values, decoded_shape, log_probability = ops.ctc_greedy_decoder(inputs,
-                                                                                                     sequence_length)
+        >>> decoded_indices, decoded_values, decoded_shape, log_probability = ctc_greedy_decode(inputs, sequence_length)
         >>> print(decoded_indices)
         [[0 0]
          [0 1]
diff --git a/tests/st/ops/gpu/test_matrix_determinant_op.py b/tests/st/ops/gpu/test_matrix_determinant_op.py
index 0c763b5993d..5112fffefd6 100644
--- a/tests/st/ops/gpu/test_matrix_determinant_op.py
+++ b/tests/st/ops/gpu/test_matrix_determinant_op.py
@@ -141,6 +141,8 @@ def test_matrix_determinant_dy_shape():
     input_shape = (4, 4)
     data_type = np.float32
     ms_data_type = ms_type.float32
+    if data_type in (np.float32, np.complex64):
+        loss = 1e-3
     input_x_np = np.random.random(input_shape).astype(data_type)
     benchmark_output = matrix_determinant_scipy_benchmark(input_x_np)
 
@@ -169,6 +171,8 @@ def test_log_matrix_determinant_dy_shape():
     input_shape = (4, 4)
     data_type = np.float32
     ms_data_type = ms_type.float32
+    if data_type in (np.float32, np.complex64):
+        loss = 1e-3
     input_x_np = np.random.random(input_shape).astype(data_type)
     benchmark_output = log_matrix_determinant_np_benchmark(input_x_np)
 
@@ -195,6 +199,8 @@ def test_matrix_determinant_vmap():
     context.set_context(mode=context.GRAPH_MODE)
     loss = 1e-6
     data_type = np.float32
+    if data_type in (np.float32, np.complex64):
+        loss = 1e-3
     # Case : in_axes input_x batch remains 0
     input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
                                [[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))
@@ -219,6 +225,8 @@ def test_log_matrix_determinant_vmap():
     context.set_context(mode=context.GRAPH_MODE)
     loss = 1e-6
     data_type = np.float32
+    if data_type in (np.float32, np.complex64):
+        loss = 1e-3
     # Case : in_axes input_x batch remains 0
     input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
                                [[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))