Problem description

When an array is sent to tensorflow training, the following errors are reported:

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray)

The array element is an array, and the shape of each array element is inconsistent. An example is as follows:

cropImg[0].shape = (13, 13, 3)
cropImg[1].shape = (14, 13, 3)
cropImg[2].shape = (12, 13, 3)

environment

python 3.7. 9

tensorflow 2.6. 0

keras 2.6. 0

Solution:

There are many similar error reports on stackoverflow, which roughly means that the data type is wrong. The converted data type is not the data type in brackets, such as:

Unsupported object type numpy.Ndarray means that the cropimg array element is not numpy Ndarray type

Bloggers were puzzled and tried many methods. They all showed that the data type of the cropimg array element was numpy Ndarray, but the error always exists

Later, I suddenly realized that when generating the cropimg array, there was a warning:

VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  cropImg_ar = np.array(img_list)

The cropimg array element is an array with inconsistent shapes, which indicates that the cropimg array element type is actually object. Is it caused by tensorflow not accepting object type data

After converting the cropimg array elements to shape consistency, the problem is solved