ValueError:找不到可以处理输入的数据适配器:

Question

我正在尝试将以下训练数据拟合到我的模型中：

data = np.array(data, dtype="float32") / 255.0
for i in coords_list:
# print(" 'i' is: ", i)
for j in i:
# print(" 'j' is: ", j)
np.array(j, dtype="float32")
split = train_test_split(data, coords_list, files, test_size=0.10, random_state=42)
(trainImages, testImages) = split[:2]
(trainTargets, testTargets) = split[2:4]
(trainFilenames, testFilenames) = split[4:]

其中，data是数组格式的图像列表，coords_list是包含浮点数的元组的列表，而files是字符串列表。在将输入数据(data和coords_list(转换为numpy数组后，我将它们划分为训练集、验证集和测试集。然后将它们拟合到模型中，运行无法处理coords_list类型的ValueError。

[…]

_ = model.fit(
trainImages, trainTargets,
validation_data=(testImages, testTargets),
batch_size=6,
epochs=5,           # 50
steps_per_epoch=1,  # 500
verbose=1
)
Traceback (most recent call last):
File "C:/Users/Alexandros.Oikonomid/OneDrive - Priva/Desktop/Truss_Detection/Trusses_Detection.py", line 210, in <module>
_ = model.fit(
File "C:UsersAlexandros.OikonomidPycharmProjectspythonProject1libsite-packagestensorflowpythonkerasenginetraining.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "C:UsersAlexandros.OikonomidPycharmProjectspythonProject1libsite-packagestensorflowpythonkerasenginetraining.py", line 1049, in fit
data_handler = data_adapter.DataHandler(
File "C:UsersAlexandros.OikonomidPycharmProjectspythonProject1libsite-packagestensorflowpythonkerasenginedata_adapter.py", line 1104, in __init__
adapter_cls = select_data_adapter(x, y)
File "C:UsersAlexandros.OikonomidPycharmProjectspythonProject1libsite-packagestensorflowpythonkerasenginedata_adapter.py", line 968, in select_data_adapter
raise ValueError(
ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {'(<class 'list'> containing values of types {'(<class \'tuple\'> containing values of types {"<class \'float\'>"})'})'})

任何关于如何解决此错误的建议都将不胜感激！

Answer 1

嵌套循环不会将coords_list转换为numpy数组。这应该能解决问题

import numpy as np
coords_list = np.array(coords_list, dtype="float32")
split = train_test_split(data, coords_list, files, test_size=0.10, random_state=42)