使用卷积神经网络CNN对手势进行识别

step 1

python train.py

step 2

python test.py

#### 内有中文注释,训练迭代数可自行调大
如遇到以下报错
``` cannot import name '_validate_lengths' from 'numpy.lib.arraypad' ``` 原因：
这是在解决skimage0.15版本后出现的问题。找不到_validate_lengths函数，在arraypad.py文件中确实找不到对应的函数，所以找到以前配置过的环境中对应的文件，拷贝这个缺失的函数。
python3.7/site-packages/numpy/lib/arraypad.py,打开文件后，在954后添加以下代码，保存退出，问题解决。

def _normalize_shape(ndarray, shape, cast_to_int=True):

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):

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