关于python：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//’>

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)