2 
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers.recurrent import SimpleRNN
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import LabelEncoder
import numpy as np

text = open('eng.train').read().split()
words = []
tags_1 = []
tags_2 = []
for i in range(len(text)):
    if i % 4 == 0:
        words.append(text[i])
    if i % 4 == 1:
        tags_1.append(text[i])
    if i % 4 == 3:
        tags_2.append(text[i])

hashing_vectorizer = HashingVectorizer(decode_error = 'ignore', n_features = 2 **15)
X_v = hashing_vectorizer.fit_transform(words)

label_encoder = LabelEncoder()
y1 = label_encoder.fit_transform(tags_1)
y2 = label_encoder.fit_transform(tags_2)
y1 = np_utils.to_categorical(y1)
y2 = np_utils.to_categorical(y2)

import nltk
trigram_X = list(nltk.trigrams(X_v))
#trigram_X = list(trigram_X)
print(len(trigram_X))
X = numpy.array(trigram_X)
print(X.shape)

y = numpy.reshape(y1, (204567, 1, 46))
trigram_tags = list(nltk.trigrams(y))
#trigram_y = list(trigram_tags)
print (len(trigram_y))
target = numpy.array(trigram_y)
y = numpy.reshape(target, (204565, 3, 46))
X = numpy.reshape(X, (204565, 3, 1))

X_final = numpy.dstack((X, y))
print(X_final.shape)

X_input = X_final[: -1, :, :]
print(X_input.shape)

y_final = label_encoder.fit_transform(tags_1)
y_target = np_utils.to_categorical(y_final[3:])
print(y_target.shape)

from keras.layers import Dense
from keras.models import Sequential
from keras.layers.recurrent import SimpleRNN

这里使用特征 hashig。现在的问题需要给生成的散列向量和一个对应的一个热编码向量作为输入。但是 keras 程序抛出以下错误：

model = Sequential()
model.add(SimpleRNN(100,input_shape = (X_input.shape[1], X_input.shape[2])))
model.add(Dense(y_target.shape[1], activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(X_input, y_target, epochs = 20, batch_size = 200)


ValueError: setting an array element with a sequence.

请说明错误原因和可能的解决方案

编辑 1

我附上了完整的错误堆栈，如下所示

ValueError                                Traceback (most recent call last)
<ipython-input-3-4d9a4c1d9885> in <module>()
     62 model.add(Dense(y_target.shape[1], activation = 'softmax'))
     63 model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
---> 64 model.fit(X_input, y_target, epochs = 20, batch_size = 200)
     65 

/home/aditya/anaconda3/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
    843                               class_weight=class_weight,
    844                               sample_weight=sample_weight,
--> 845                               initial_epoch=initial_epoch)
    846 
    847     def evaluate(self, x, y, batch_size=32, verbose=1,

/home/aditya/anaconda3/lib/python3.6/site-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, **kwargs)
   1483                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1484                               callback_metrics=callback_metrics,
-> 1485                               initial_epoch=initial_epoch)
   1486 
   1487     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/home/aditya/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
   1138                 batch_logs['size'] = len(batch_ids)
   1139                 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1140                 outs = f(ins_batch)
   1141                 if not isinstance(outs, list):
   1142                     outs = [outs]

/home/aditya/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2071         session = get_session()
   2072         updated = session.run(self.outputs + [self.updates_op],
-> 2073                               feed_dict=feed_dict)
   2074         return updated[:len(self.outputs)]
   2075 

/home/aditya/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/aditya/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    966             feed_handles[subfeed_name] = subfeed_val
    967           else:
--> 968             np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
    969 
    970           if (not is_tensor_handle_feed and

/home/aditya/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    529 
    530     """
--> 531     return array(a, dtype, copy=False, order=order)
    532 
    533 

ValueError: setting an array element with a sequence
4

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