1
我训练一个类似于在tensorflow教程，为连续数据的RNN session.run Tensorflow ValueError异常。数据是[batch_size，step，dimension]，标签是[batch_size，num_classes]。由于不同样本的序列长度不同，因此我想创建批次训练 - 每次抓取32个样本数据时，将它们填充到最长的序列大小，然后将它们送入rnn图形进行训练。上一批培训
的数据被定义为：
data = DataGenerator(data_path, label_path)
train_data, train_label, train_seqlen, test_data, test_label = data.train_test_data(0.2)
x = tf.placeholder(tf.float32, [batch_size, None, num_dim])
y = tf.placeholder(tf.float32, [batch_size, num_classes])
seqlen = tf.placeholder(tf.int32, [batch_size])
model = VariableSeqModel(x, y, seqlen)
Train_data是[batch_size时，步骤，暗淡]，train_label是[batch_size时，num_classes]。 Seqlen是[batch_size时，1]用于记录样品的实际序列长度中的每个批次。这是否正确，我将x定义为[batch_size，None，num_dim]用于变量序列长度？
限定RNN和数据结构，启动会话，因为这代码示例中后：
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
step = 1
while step*batch_size < 1000:
batch_xx, batch_y, batch_seqlen = data.next(batch_size, train_data, train_label, train_seqlen)
batch_x = data.batch_padding(batch_xx,batch_seqlen)
sess.run(model.optimize, feed_dict={x: batch_xx, y: batch_y, seqlen: batch_seqlen})
step += 1
我打于以下ValueError异常（下面堆栈跟踪）：
dynamic_rnn.py in <module>()
--> 129 sess.run(model.optimize, feed_dict={x: batch_xx, y: batch_y, seqlen: batch_seqlen})
tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
708 try:
709 result = self._run(None, fetches, feed_dict, options_ptr,
--> 710 run_metadata_ptr)
711 if run_metadata:
712 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
879 ' to a larger type (e.g. int64).')
880
--> 881 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
882
883 if not subfeed_t.get_shape().is_compatible_with(np_val.shape):
numpy/core/numeric.pyc in asarray(a, dtype, order)
480
481 """
--> 482 return array(a, dtype, copy=False, order=order)
483
484 def asanyarray(a, dtype=None, order=None):
ValueError: setting an array element with a sequence.
我在难倒这点。任何帮助感谢！