相关疑难解决方法(0)

我正在尝试根据我的数据集中的部分特征训练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？

我想使用keras python库创建最简单的LSTM。

我有以下代码：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM

X_train = pd.DataFrame( np.array([ [1, 2], [3, 4], [5, 6], [7, 8], [5.1, 6.1], [7.1, 8.1] ]))
y_train = pd.DataFrame( np.array([1, 2, 3, 4, 3, 4]) )
X_test = pd.DataFrame( np.array([ [1.1, 2.1], [3.1, 4.1] ]) )
y_test = pd.DataFrame( np.array([1, 2]) )

model = Sequential()
model.add(LSTM( output_dim = 10, return_sequences=False, input_dim=X_train.shape[1]))
model.add(Dense(input_dim = 10, output_dim=2)) …