我正在尝试根据我的数据集中的部分功能训练Keras模型。我已经加载了数据集并提取了这样的功能:
train_data = pd.read_csv('../input/data.csv')
X = train_data.iloc[:, 0:30]
Y = train_data.iloc[:,30]
# Code for selecting the important features automatically (removed) ...
# Selectintg important features 14,17,12,11,10,16,18,4,9,3
X = train_data.reindex(columns=['V14','V17','V12','V11','V10','V16','V18','V4','V9','V3'])
print(X.shape[1]) # -> 10
但是当我调用fit方法时:
# Fit the model
history = model.fit(X, Y, validation_split=0.33, epochs=10, batch_size=10, verbose=0, callbacks=[early_stop])
我收到以下错误:
KeyError: '[3 2 5 1 0 4] not in index'
我错过了什么?
答案 0 :(得分:10)
numpy
期望模型输入为pandas.DataFrame
个数组 - 而不是X = train_data.iloc[:, 0:30].as_matrix()
Y = train_data.iloc[:,30].as_matrix()
个。尝试:
as_matrix
由于pandas.DataFrame
方法会将numpy.array
转换为=LINEST(IF(ISNUMBER(C15:C26);C15:C26;);IF(ISNUMBER(C15:C26);CHOOSE({1;2;3};1;D15:D26;E15:E26););1;1)
。