来自TF的Keras:损失是NaN并且无法找到可以处理输入的数据适配器:<class \\’pandas.core.frame.DataFrame\\’>，<class \\’NoneType\\’>

Keras from TF : loss is NaN and Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>

我试图找到一些可以解决我的问题的解决方案，但目前它们都不起作用。 (如 Tensorflow ValueError: Failed to find data adapter that can handle input)

我正在通过 Keras(来自 TF)使用具有输入形状： (5000, 1) 和输出形状为 (5000, 16) 的自定义数据集进行神经网络。

输入是时间和周期数，输出是 16 个灯中每个灯的状态(0 表示关闭或 1 表示打开)。我使用 Adam 作为优化器，我的损失是"categorical_crossentropy"(也许我在使用这个时出错了……我不确定)。

所以问题是当我尝试训练我的网络时，我收到了以下错误消息：

WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>
  import pandas as pd

  import tensorflow as tf

  

  from tensorflow.keras.models import Model

  from tensorflow.keras.layers import Input, Dense

  

  # Read csv file into a pandas dataframe

  data= pd.read_excel(r'/datasetV07clear16lamps.xlsx') 

  

  #Index by time

  data.sort_values("Time") 

  print(data.isnull().any() )

  

  #split the dataset 

  train=data[0:5000]

  test=data[5000:]

  print(train.shape)

  print(test.shape)

  

  ## split the dataset into train and test dataset

  # create train dataset

  X1_train=train[['Time']]

  X2_train=train[['cycle']]

y_train=train[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']]

  

  #create test dataset

  X1_test=test[['Time']]

  X2_test=test[['cycle']] 



y_test=test[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']]



  # Define the input

  input_tensor = Input(shape=(2,))

  

  # Define the output

  output_tensor = Dense(16)(input_tensor)

  

  # Create a model

  model = Model(input_tensor, output_tensor)

  

  # Compile the model

  model.compile(optimizer='adam', loss='categorical_crossentropy')

  

  # Fit the model

  model.fit(train[['Time','cycle']], train[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']], verbose=True, batch_size=16384, epochs=100)

但通常我的输入和输出都是 <class 'pandas.core.frame.DataFrame'>

而我的损失是loss: nan。这有点令人沮丧，因为我不知道我的错误来自哪里。

如果你知道什么是错的？如果太混乱，我可以提供我的代码。

提前谢谢你！

编辑：按照要求，这是我的代码：

WARNING:tensorflow:Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>
  import pandas as pd

  import tensorflow as tf

  

  from tensorflow.keras.models import Model

  from tensorflow.keras.layers import Input, Dense

  

  # Read csv file into a pandas dataframe

  data= pd.read_excel(r'/datasetV07clear16lamps.xlsx') 

  

  #Index by time

  data.sort_values("Time") 

  print(data.isnull().any() )

  

  #split the dataset 

  train=data[0:5000]

  test=data[5000:]

  print(train.shape)

  print(test.shape)

  

  ## split the dataset into train and test dataset

  # create train dataset

  X1_train=train[['Time']]

  X2_train=train[['cycle']]

y_train=train[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']]

  

  #create test dataset

  X1_test=test[['Time']]

  X2_test=test[['cycle']] 



y_test=test[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']]



  # Define the input

  input_tensor = Input(shape=(2,))

  

  # Define the output

  output_tensor = Dense(16)(input_tensor)

  

  # Create a model

  model = Model(input_tensor, output_tensor)

  

  # Compile the model

  model.compile(optimizer='adam', loss='categorical_crossentropy')

  

  # Fit the model

  model.fit(train[['Time','cycle']], train[['L1green','L1orange','L1red','L1blink','L2green','L2orange','L2red','L2blink','L3green','L3orange','L3red','L3blink','L4green','L4orange','L4red','L4blink']], verbose=True, batch_size=16384, epochs=100)

我找到了解决方案：

首先使用 sklearn.preprocessing 中的 StandardScaler 进行特征缩放(用于输入和输出)。然后我不是为所有行做 1 nn，而是为每个行做一个 nn。

我仍然不知道为什么我收到错误消息 "Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>