TensorFlow安装

TensorFlow成功安装

首先声明本次安装的是基于Python 3.7 tensorflow-cpu的安装，其他版本安装参考网上其他资料。

以前使用的python3.6的，安装过程没遇到什么错误。这次是在python3.7上安装的，遇到了不同的问题，故记录下安装过程中的问题及解决办法，希望对大家有所帮助。我是在windows上安装的，个人觉得用anaconda进行安装非常方便。anaconda安装后有一个类似windows命令窗口的Anaconda Prompt，如图：

打开上述窗口后执行

1.pip install tensorflow

出现了这个问题 wisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.，

解决方法 ：pip install PyHamcrest

同时还会出现 ： ERROR:Cannot uninstall 'wrap’问题

解决方案： pip install -U --ignore-installed wrapt enum34 simplejson netaddr

参考：ERROR:Cannot uninstall ‘wrap’

同时还会出现 ： tensorboard 1.14.0 has requirement setuptools>=41.0.0, but you’ll have setuptools 40.2.0 which is incompatible.问题

解决方案： pip install setuptools==41.0.0

2.这个时候看起来已经成功安装，但是还会出现问题。

当我们导入包时： import tensorflow as tf

又出现问题： ModuleNotFoundError: No module named ‘numpy.core._multiarray_umath’ 错误

解决方案：升级numpy pip install --upgrade numpy

参考：更新numpy版本

这个时候基本就安装成功了。

补充 ：

后面会遇到在引入skimage 包时报错，错误提示from numpy.lib.arraypad import _validate_lengths，找不到_validate_lengths函数。

找到：Anaconda3/envs/your dirb/python3.7/site-packages/numpyb/arraypad.py的954行，添加下面两个函数保存，重新加载即可消除错误。参考：解决方法来源

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