python 3.x - ValueError: setting an array element with a sequence in keras using nltk ngram -

from keras.utils import np_utils keras.models import sequential keras.layers import dense keras.layers.recurrent import simplernn sklearn.feature_extraction.text import hashingvectorizer sklearn.feature_extraction.text import countvectorizer sklearn.preprocessing import labelencoder import numpy np  text = open('eng.train').read().split() words = [] tags_1 = [] tags_2 = [] in range(len(text)):     if % 4 == 0:         words.append(text[i])     if % 4 == 1:         tags_1.append(text[i])     if % 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)  keras.layers import dense keras.models import sequential keras.layers.recurrent import simplernn 

feature hashig used here. problem requires give generated hashed vector corresponding 1 hot encoded vector input. keras program throwing following error:

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 array element sequence. 

please explain reason error , possible solution

edit 1

i have attached full error stack below

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 ,  /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 array element sequence