如何解决comvertind pandas数据框时，无法将NumPy数组转换为张量？

我正在尝试将pandas数据框转换为tf数据集，但我经常遇到此问题：

Traceback (most recent call last):
  File "/home/arch_poppin/dev/AI/reviews/rev.py",line 36,in <module>
    dataset = tf.data.Dataset.from_tensor_slices((df.values,label.values))
  File "/usr/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py",line 689,in from_tensor_slices
    return TensorSliceDataset(tensors)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py",line 3066,in __init__
    element = structure.normalize_element(element)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/data/util/structure.py",line 129,in normalize_element
    ops.convert_to_tensor(t,name="component_%d" % i,dtype=dtype))
  File "/usr/lib/python3.8/site-packages/tensorflow/python/profiler/trace.py",line 163,in wrapped
    return func(*args,**kwargs)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/ops.py",line 1535,in convert_to_tensor
    ret = conversion_func(value,dtype=dtype,name=name,as_ref=as_ref)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/tensor_conversion_registry.py",line 52,in _default_conversion_function
    return constant_op.constant(value,dtype,name=name)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py",line 264,in constant
    return _constant_impl(value,shape,name,verify_shape=False,File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py",line 276,in _constant_impl
    return _constant_eager_impl(ctx,value,verify_shape)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py",line 301,in _constant_eager_impl
    t = convert_to_eager_tensor(value,ctx,dtype)
  File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py",line 98,in convert_to_eager_tensor
    return ops.EagerTensor(value,ctx.device_name,dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).

这是我的代码，如下所示：

import os
os.environ[''TF_CPP_MIN_LOG_LEVEL''] = ''3''
import io
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import numpy as np


# In[0] open file
df = pd.read_csv(r''PATH'')
df = df.sample(frac=1).reset_index(drop=True)


# In[1] make comma separated integers into list

objectColumnList = list(df.select_dtypes(include=[''object'']).columns)

for column in objectColumnList:
    colArr = []
    for row in df[column]:
        arr = np.asarray(row.split('','')).astype(np.float32)
        colArr.append(arr)
    df[column] = colArr

# In[2] mnake datatset

label = df.pop(''MYLABELS'')

dataset = tf.data.Dataset.from_tensor_slices((df.values,label.values))

如果要重现该错误，请现在指向我正在使用的csv文件的链接： https://mega.nz/file/uOwiwK5K#FVG7K0glMh2mGa53UDWQiG6iKgNFn5972Kdjb-gmAV4由于隐私原因，我必须删除列名

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

小编邮箱:dio#foxmail.com (将#修改为@）

>>> df = pd.DataFrame([[1,1,1],[2,2,2],[3,3,3]],columns=["col1","col2","col3","col4"])
>>> df
   col1  col2  col3  col4
0     1     1     1     1
1     2     2     2     2
2     3     3     3     3

>>> df.mean(axis=1)
0    1
1    2
2    3

>>> df.drop("col4",axis=1)
   col1  col2  col3
0     1     1     1
1     2     2     2
2     3     3     3

import pandas as pd
import numpy as np

df_train = pandas.read_csv("training.csv")

def convert_to_np_arr(im_as_str):
    im = [int(i) for i in im_as_str.split()]
    im = np.asarray(im)
    im = im.reshape((96,96))
    return im

df_train['Im_as_np'] = df_train.Image.apply(convert_to_np_arr)

im = df_train.Im_as_np[0]
plt.imshow(im,cmap = cm.Greys_r)
plt.show()

import pandas as pd
import numpy as np

df_train = pandas.read_csv("training.csv")

im = df_train.Image[0]
im = [int(i) for i in im.split()]
im = np.asarray(im)
im = im.reshape((96,96))

plt.imshow(im,cmap = cm.Greys_r)
plt.show()

im=df_train.ix[0,'Im_as_np']

import numpy as np

arr = np.array([[1,['a','b','c']],[2,'c']]],dtype=object)
arr = arr[:,1]

print(arr)
# [['a','c'] ['a','c']]

type(arr)
# numpy.ndarray
type(arr[0])
# list

arr.shape
# (2,)

arr.astype(str)
# ValueError: setting an array element with a sequence

arr_2 = np.array(list(arr))

type(arr_2)
# numpy.ndarray
type(arr_2[0])
# numpy.ndarray

arr_2.shape
# (2,3)

In [1617]: timeit np.array(arr[:,1].tolist())
...
100000 loops,best of 3: 11.5 µs per loop
In [1618]: timeit np.vstack(arr[:,1])
...
10000 loops,best of 3: 54.1 µs per loop

np.concatenate([np.atleast_2d(a) for a in arr[:,1]],axis=0)

In [1627]: timeit np.array([a for a in arr[:,1]])
100000 loops,best of 3: 18.6 µs per loop
In [1629]: timeit np.stack(arr[:,1],axis=0)
10000 loops,best of 3: 60.2 µs per loop