`` `ValueError：tensorflowプロジェクトを実行するときに、シーケンス` ``を使用して配列要素を設定する

 File "model_VQA.py", line 279, in train
    tf_loss, tf_image, tf_question, tf_label = model.build_model()
  File "model_VQA.py", line 53, in build_model
    state = tf.zeros([self.batch_size, self.stacked_lstm.state_size])
  File "/home/xus/.virtualenvs/vqa/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1359, in zeros
...
ValueError: setting an array element with a sequence.

`

# question-embedding
    self.embed_ques_W = tf.Variable(tf.random_uniform([self.vocabulary_size, self.input_embedding_size], -0.08, 0.08), name='embed_ques_W')#self.vocabulary_size=14470 self.input_embedding_size=200
  # encoder: RNN body
    self.lstm_1 = tf.contrib.rnn.LSTMCell(rnn_size, input_embedding_size, use_peepholes=True)#rnn_size=512,input_embedding_size=200
    self.lstm_dropout_1 =tf.contrib.rnn.DropoutWrapper(self.lstm_1, output_keep_prob = 1 - self.drop_out_rate)#self.drop_out_rate=0
    self.lstm_2 = tf.contrib.rnn.LSTMCell(rnn_size, rnn_size, use_peepholes=True)
    self.lstm_dropout_2 = tf.contrib.rnn.DropoutWrapper(self.lstm_2, output_keep_prob = 1 - self.drop_out_rate)
    self.stacked_lstm = tf.contrib.rnn.MultiRNNCell([self.lstm_dropout_1, self.lstm_dropout_2])#state_is_tuple=False)
    # state-embedding
    self.embed_state_W = tf.Variable(tf.random_uniform([2*rnn_size*rnn_layer, self.dim_hidden], -0.08,0.08),name='embed_state_W')#dim_hidden=1024
    self.embed_state_b = tf.Variable(tf.random_uniform([self.dim_hidden], -0.08, 0.08), name='embed_state_b')
    # image-embedding
    self.embed_image_W = tf.Variable(tf.random_uniform([dim_image, self.dim_hidden], -0.08, 0.08), name='embed_image_W')#dim_image=4096
    self.embed_image_b = tf.Variable(tf.random_uniform([dim_hidden], -0.08, 0.08), name='embed_image_b')
    # score-embedding
    self.embed_scor_W = tf.Variable(tf.random_uniform([dim_hidden, num_output], -0.08, 0.08), name='embed_scor_W')#num_output=1000
    self.embed_scor_b = tf.Variable(tf.random_uniform([num_output], -0.08, 0.08), name='embed_scor_b')
def build_model(self):
    image = tf.placeholder(tf.float32, [self.batch_size, self.dim_image])
    question = tf.placeholder(tf.int32, [self.batch_size, self.max_words_q])
    label = tf.placeholder(tf.int64, [self.batch_size,])
    state = tf.zeros([self.batch_size, self.stacked_lstm.state_size])#self.batch_size=500

「ありがとう」 誰もこのエラーの原因を教えてくれますか？私はVQAプロジェクトをしています、このコードはgithubからのものであり、リンクはhttps://github.com/JamesChuanggg/VQA-tensorflow/blob/master/model_VQA.py