我正在尝试使用tf.image.crop_and_resize裁剪图像，但是出现了一个我不明白的错误，下面将进一步说明。
我试图将我的sample_excerpt重塑为不同的形状，因为这是我的错误抱怨的地方，特别是与tf.image.crop_and_resize希望图像为密集的形状张量这一事实有关[1] 。这让我感到困惑，因为在文档中它必须是[batch，height，width，depth]形状。
import matplotlib.pyplot as plt
from keras.preprocessing import image
import numpy as np
# grab slice or numpy arrays from dataset
feature = hdf5_file['val_features'][0, ...]
sample_excerpt = feature[:,1400:1515]
scales = list(np.arange(0.8, 1.0, 0.01))
# make a list for box dimensions
boxes = np.zeros((len(scales), 4))
# fill the boxes array with sequentially changing dimensions
for i, scale in enumerate(scales):
x1 = y1 = 0.5 - (0.5 * scale)
x2 = y2 = 0.5 + (0.5 * scale)
boxes[i] = [x1, y1, x2, y2]
excerpt_reshaped = sample_excerpt.reshape((1,sample_excerpt.shape[0], sample_excerpt.shape[1],1))
crops = tf.image.crop_and_resize([excerpt_reshaped], boxes=boxes, box_ind=np.zeros(len(scales)), crop_size=(80, 115))
我希望将numpy形状转换为（批处理，高度，宽度，深度）将使其与tf.image.crop_and_resize兼容，但是却出现以下错误：
ValueError Traceback (most recent call last)
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
510 as_ref=input_arg.is_ref,
--> 511 preferred_dtype=default_dtype)
512 except TypeError as err:
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_symbolic_tensors)
1174 if ret is None:
-> 1175 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1176
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
303 _ = as_ref
--> 304 return constant(v, dtype=dtype, name=name)
305
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
244 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 245 allow_broadcast=True)
246
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
282 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 283 allow_broadcast=allow_broadcast))
284 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
474 (values, list(nparray.shape),
--> 475 _GetDenseDimensions(values)))
476
ValueError: Argument must be a dense tensor: [array([[[-0.2746672 , -0.2746672 , -0.2746672 , ..., -0.2746672 ,
-0.2746672 , -0.2746672 ],
[-0.2746672 , -0.2746672 , -0.2746672 , ..., -0.2746672 ,
-0.2746672 , -0.2746672 ],
[-0.2746672 , -0.2746672 , -0.2746672 , ..., -0.2746672 ,
-0.2746672 , -0.2746672 ],
...,
[ 4.01187315, 4.28077045, 3.79459085, ..., 2.70409744,
3.30734571, 3.01116682],
[ 2.82011643, 3.15533266, 3.1251727 , ..., 2.45012212,
2.20143238, 1.92358158],
[ 0.0115974 , -0.18517987, -0.13355623, ..., -0.2746672 ,
-0.2746672 , -0.2746672 ]]])] - got shape [1, 1, 80, 115], but wanted [1].