报错信息：
ImportError: cannot import name '_validate_lengths' from 'numpy.lib.arraypad' (E:\Anaconda\envs\python37\lib\site-packages\numpy\lib\arraypad.py)
查看上面路径的arraypad.py文件 发现确实没有这函数
所以打开文件在后面，一般是954行之后加上下面函数
def _normalize_shape(ndarray, shape, cast_to_int=True):
"""
Private function which does some checks and normalizes the possibly
much simpler representations of 'pad_width', 'stat_length',
'constant_values', 'end_values'.
Parameters
----------
narray : ndarray
Input ndarray
shape : {sequence, array_like, float, int}, optional
The width of padding (pad_width), the number of elements on the
edge of the narray used for statistics (stat_length), the constant
value(s) to use when filling padded regions (constant_values), or the
endpoint target(s) for linear ramps (end_values).
((before_1, after_1), ... (before_N, after_N)) unique number of
elements for each axis where `N` is rank of `narray`.
((before, after),) yields same before and after constants for each
axis.
(constant,) or val is a shortcut for before = after = constant for
all axes.
cast_to_int : bool, optional
Controls if values in ``shape`` will be rounded and cast to int
before being returned.
Returns
-------
normalized_shape : tuple of tuples
val => ((val, val), (val, val), ...)
[[val1, val2], [val3, val4], ...] => ((val1, val2), (val3, val4), ...)
((val1, val2), (val3, val4), ...) => no change
[[val1, val2], ] => ((val1, val2), (val1, val2), ...)
((val1, val2), ) => ((val1, val2), (val1, val2), ...)
[[val , ], ] => ((val, val), (val, val), ...)
((val , ), ) => ((val, val), (val, val), ...)
"""
ndims = ndarray.ndim
# Shortcut shape=None
if shape is None:
return ((None, None), ) * ndims
# Convert any input `info` to a NumPy array
shape_arr = np.asarray(shape)
try:
shape_arr = np.broadcast_to(shape_arr, (ndims, 2))
except ValueError:
fmt = "Unable to create correctly shaped tuple from %s"
raise ValueError(fmt % (shape,))
# Cast if necessary
if cast_to_int is True:
shape_arr = np.round(shape_arr).astype(int)
# Convert list of lists to tuple of tuples
return tuple(tuple(axis) for axis in shape_arr.tolist())
def _validate_lengths(narray, number_elements):
"""
Private function which does some checks and reformats pad_width and
stat_length using _normalize_shape.
Parameters
----------
narray : ndarray
Input ndarray
number_elements : {sequence, int}, optional
The width of padding (pad_width) or the number of elements on the edge
of the narray used for statistics (stat_length).
((before_1, after_1), ... (before_N, after_N)) unique number of
elements for each axis.
((before, after),) yields same before and after constants for each
axis.
(constant,) or int is a shortcut for before = after = constant for all
axes.
Returns
-------
_validate_lengths : tuple of tuples
int => ((int, int), (int, int), ...)
[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
((int1, int2), (int3, int4), ...) => no change
[[int1, int2], ] => ((int1, int2), (int1, int2), ...)
((int1, int2), ) => ((int1, int2), (int1, int2), ...)
[[int , ], ] => ((int, int), (int, int), ...)
((int , ), ) => ((int, int), (int, int), ...)
"""
normshp = _normalize_shape(narray, number_elements)
for i in normshp:
chk = [1 if x is None else x for x in i]
chk = [1 if x >= 0 else -1 for x in chk]
if (chk[0] < 0) or (chk[1] < 0):
fmt = "%s cannot contain negative values."
raise ValueError(fmt % (number_elements,))
return normshp