这是我创建“组合”变量的方式，首先，我对分类变量进行了热编码，然后通过创建包含所有其他列值的列表来创建新的数组变量
series = pd.read_csv('base_alarmes.csv', header=None, sep=',', encoding='latin-1')
series.shape
one_hot = pd.get_dummies(series[0])
one_hot['combined']= one_hot.values.tolist()
显示错误消息的位置，特别是第5行
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
history = model.fit(train_X, np.array(train_y), epochs=11, batch_size=72, validation_data=(validate_X, np.array(validate_y)), verbose=2, shuffle=False)
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='validation')
pyplot.legend()
pyplot.show()
为什么会收到此错误消息？我已经尝试过将其转换为“ as_type（int）”，“ pd.to_numeric（）”，但无法正常工作。
编辑1
Train on 113732 samples, validate on 37911 samples
Epoch 1/11
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-64-d01943c3ab8b> in <module>()
5 model.compile(loss='mae', optimizer='adam')
6 # fit network
----> 7 history = model.fit(train_X, np.array(train_y), epochs=11, batch_size=72, validation_data=(validate_X, np.array(validate_y)), verbose=2, shuffle=False)
8 # plot history
9 pyplot.plot(history.history['loss'], label='train')
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2653 array_vals.append(
2654 np.asarray(value,
-> 2655 dtype=tf.as_dtype(tensor.dtype).as_numpy_dtype))
2656 if self.feed_dict:
2657 for key in sorted(self.feed_dict.keys()):
/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py in asarray(a, dtype, order)
490
491 """
--> 492 return array(a, dtype, copy=False, order=order)
493
494
ValueError: setting an array element with a sequence.
可变形状
(189554,)
变量值如下
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...]
我刚刚发现列中的值可能像这样：
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...,
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dtype=object)
难道是它不起作用的原因吗？