拟合时的Keras GRU NN KeyError:“不在索引中" [英] Keras GRU NN KeyError when fitting : "not in index"
问题描述
我目前在尝试将我的GRU模型与训练数据拟合时遇到问题. 快速浏览StackOverflow之后，我发现这篇文章与我的问题非常相似:
I'm currently facing an issue while trying to fit my GRU model with my training data. After a quick look on StackOverflow, I found this post to be quite similar to my issue :
我自己的模型如下:
nn = Sequential()
nn.add(Embedding(input_size, hidden_size))
nn.add(GRU(hidden_size_2, return_sequences=False))
nn.add(Dropout(0.2))
nn.add(Dense(output_size))
nn.add(Activation('linear'))
nn.compile(loss='mse', optimizer="rmsprop")
history = History()
nn.fit(X_train, y_train, batch_size=30, nb_epoch=200, validation_split=0.1, callbacks=[history])
错误是:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-14-e2f199af6e0c> in <module>()
1 history = History()
----> 2 nn.fit(X_train, y_train, batch_size=30, nb_epoch=200, validation_split=0.1, callbacks=[history])
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\keras\models.pyc in fit(self, X, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, show_accuracy, class_weight, sample_weight)
487 verbose=verbose, callbacks=callbacks,
488 val_f=val_f, val_ins=val_ins,
--> 489 shuffle=shuffle, metrics=metrics)
490
491 def predict(self, X, batch_size=128, verbose=0):
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\keras\models.pyc in _fit(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, metrics)
199 batch_ids = index_array[batch_start:batch_end]
200 try:
--> 201 ins_batch = slice_X(ins, batch_ids)
202 except TypeError as err:
203 raise Exception('TypeError while preparing batch. \
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\keras\models.pyc in slice_X(X, start, stop)
53 if type(X) == list:
54 if hasattr(start, '__len__'):
---> 55 return [x[start] for x in X]
56 else:
57 return [x[start:stop] for x in X]
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
1789 if isinstance(key, (Series, np.ndarray, Index, list)):
1790 # either boolean or fancy integer index
-> 1791 return self._getitem_array(key)
1792 elif isinstance(key, DataFrame):
1793 return self._getitem_frame(key)
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\pandas\core\frame.pyc in _getitem_array(self, key)
1833 return self.take(indexer, axis=0, convert=False)
1834 else:
-> 1835 indexer = self.ix._convert_to_indexer(key, axis=1)
1836 return self.take(indexer, axis=1, convert=True)
1837
C:\Users\XXXX\AppData\Local\Continuum\Anaconda\lib\site-packages\pandas\core\indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1110 mask = check == -1
1111 if mask.any():
-> 1112 raise KeyError('%s not in index' % objarr[mask])
1113
1114 return _values_from_object(indexer)
KeyError: '[ 61 13980 11357 5577 11500 12125 19673 10985 2480 5237 2519 14874\n 16003 2611 3851 10837 11865 14607 10682 5495 10220 5043 23145 11280\n 9547 4766 18323 730 6263] not in index'
有解决这个问题的主意吗? 谢谢
Any idea to solve this ? Thanks
有关数据的一些事实:
data_X = pd.read_csv("X.csv")
data_Y = pd.read_csv("Y.csv")
def train_test_split(X,Y, test_size=0.15):
# This just splits data to training and testing parts
ntrn = int(round(X.shape[0] * (1 - test_size)))
perms = np.random.permutation(X.shape[0])
X_train = X.ix[perms[0:ntrn]]
Y_train = Y.ix[perms[0:ntrn]]
X_test = X.ix[perms[ntrn:]]
Y_test = Y.ix[perms[ntrn:]]
return (X_train, Y_train), (X_test, Y_test)
X和Y是包含时间序列值的CSV文件(例如，对于每一行，X文件中有37个连续的时间序列值+ 2个时间值(被视为过去)，Y文件中有30个时间值(被视为预测来预测))
X and Y are CSV file containing time series values (e.g. for each row, there are 37 consecutive values of the time series in the X file + 2 time values (considered as past) and 30 in the Y file (considered as the forecast to predict))
print X_train[:1]
print y_train[:1]
0 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 \
1629 84 76 76 72 72 72 72 87 87 100 ... 165 165 169 169
33 34 35 36 37 38
1629 166 166 185 185 1236778440 1236789240
[1 rows x 39 columns]
0 1 2 3 4 5 6 7 8 9 ... 20 21 22 \
1629 195 195 195 195 196 196 194 194 192 192 ... 182 182 164
23 24 25 26 27 28 29
1629 164 146 146 128 128 103 103
[1 rows x 30 columns]
推荐答案
我无法将Pandas DataFrames用作输入&输出到Keras model.fit，至少不是Pandas 0.13.1，这是Ubuntu的标准软件包.
I couldn't use Pandas DataFrames as inputs & outputs to Keras model.fit, at least not Pandas 0.13.1, which is the standard package from Ubuntu.
相反，请使用np.array(X_train)和np.array(Y_train).那对我有用.
Instead, use np.array(X_train) and np.array(Y_train). That worked for me.
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