diff --git a/mindspore/numpy/array_creations.py b/mindspore/numpy/array_creations.py
index 9e35be0997f..a520a2b4db8 100644
--- a/mindspore/numpy/array_creations.py
+++ b/mindspore/numpy/array_creations.py
@@ -36,7 +36,7 @@ from .utils_const import _raise_value_error, _empty, _max, _min, \
     _canonicalize_axis, _list_comprehensions, _ceil, _tuple_slice, _raise_unimplemented_error, \
     _tuple_setitem
 from .array_ops import ravel, concatenate, broadcast_arrays, reshape, broadcast_to, flip, \
-    apply_along_axis, where
+    apply_along_axis, where, moveaxis
 from .dtypes import nan, pi
 
 # According to official numpy reference, the dimension of a numpy array must be less
@@ -518,21 +518,18 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     delta = None
     if num > 1:
         delta = (stop - start) / div
-        start_expand = reshape(start, bounds_shape)
         # This is similar to how numpy and jax compute linspace
-        if dtype in (mstype.int8, mstype.int16, mstype.int32, mstype.int64,
-                     mstype.uint8, mstype.uint16, mstype.uint32, mstype.uint64):
-            incremental_expand = reshape(_iota(mstype.float32, num), iota_shape)
-            delta_expand = reshape(delta, bounds_shape)
-            start_expand, incremental_expand, delta_expand = broadcast_arrays(
-                start_expand, incremental_expand, delta_expand)
-            out = start_expand + (incremental_expand * delta_expand)
-        else:
-            stop_expand = reshape(stop, bounds_shape)
-            step = reshape(_iota(mstype.float32, num), iota_shape) / div
-            start_expand, stop_expand, step = broadcast_arrays(
-                start_expand, stop_expand, step)
-            out = start_expand * (1 - step) + stop_expand * step
+        start_expand = reshape(start, bounds_shape)
+        incremental_expand = reshape(_iota(mstype.float32, num), iota_shape)
+        delta_expand = reshape(delta, bounds_shape)
+        start_expand, incremental_expand, delta_expand = broadcast_arrays(
+            start_expand, incremental_expand, delta_expand)
+        out = start_expand + (incremental_expand * delta_expand)
+        # recover endpoint
+        if endpoint:
+            out = moveaxis(out, axis, 0)
+            out[-1] = stop
+            out = moveaxis(out, 0, axis)
     elif num == 1:
         delta = nan if endpoint else stop - start
         out = reshape(start, bounds_shape)
diff --git a/mindspore/numpy/logic_ops.py b/mindspore/numpy/logic_ops.py
index 43057b160f7..59a3ec6fa1f 100644
--- a/mindspore/numpy/logic_ops.py
+++ b/mindspore/numpy/logic_ops.py
@@ -469,7 +469,7 @@ def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
         rtol (Number): The relative tolerance parameter (see Note).
         atol (Number): The absolute tolerance parameter (see Note).
         equal_nan (bool): Whether to compare ``NaN`` as equal. If True, ``NaN`` in
-        `a` will be considered equal to ``NaN`` in `b` in the output tensor.
+            `a` will be considered equal to ``NaN`` in `b` in the output tensor.
 
     Returns:
         A ``bool`` tensor of where `a` and `b` are equal within the given tolerance.
diff --git a/tests/st/numpy_native/test_array_creations.py b/tests/st/numpy_native/test_array_creations.py
index a221bb8e47b..94223b750f8 100644
--- a/tests/st/numpy_native/test_array_creations.py
+++ b/tests/st/numpy_native/test_array_creations.py
@@ -317,14 +317,6 @@ def test_arange():
     expected = mnp.arange(0, 10).asnumpy()
     match_array(actual, expected)
 
-    actual = onp.arange(start=10)
-    expected = mnp.arange(start=10).asnumpy()
-    match_array(actual, expected)
-
-    actual = onp.arange(start=10, step=0.1)
-    expected = mnp.arange(start=10, step=0.1).asnumpy()
-    match_array(actual, expected, error=6)
-
     actual = onp.arange(10, step=0.1)
     expected = mnp.arange(10, step=0.1).asnumpy()
     match_array(actual, expected, error=6)