几个TensorFlow、Keras、Python的报错处理

2019/6/16 23:20 下午 posted in  舶来 comments

介绍一下ImportError: cannot import name '_obtain_input_shape' from kerasImportError: No module named skimage.iocannot import name ‘_validate_lengths’ from ‘numpy.lib.arraypad’等报错问题的处理。

ImportError: cannot import name '_obtain_input_shape' from keras

You don't have to downgrade Keras 2.2.2.
In Keras 2.2.2 there is no _obtain_input_shape method in the keras.applications.imagenet_utils module. You can find it under keras-applications with the modul name keras_applications (underscore).

So you don't have to downgrade your Keras to 2.2.0 just change:

from keras.applications.imagenet_utils import _obtain_input_shape

to

from keras_applications.imagenet_utils import _obtain_input_shape

ImportError: No module named skimage.io

在需要安装的环境中执行(例如conda在安装时需要确认环境是否为需要安装的环境):

pip install -U scikit-image

cannot import name ‘_validate_lengths’ from ‘numpy.lib.arraypad’

找到:Anaconda3/lib/python3.6/site-packages/numpy/lib/arraypad.py 954行,添加下面两个函数保存,重新加载即可消除错误:

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
###############################################################################
# Public functions​

参考文献

  1. https://stackoverflow.com/questions/49113140/importerror-cannot-import-name-obtain-input-shape-from-keras
  2. https://blog.csdn.net/hdmjdp/article/details/65628685
  3. http://www.bubuko.com/infodetail-2921218.html