Thanks for your debug, but...

1. "max_length = tf.shape(self.outputs)[1]
   self.outputs = tf.reshape(tf.concat(1, outputs), [-1, max_length, n_hidden])", this is your new code
   but also wrong, "max_length = tf.shape(self.outputs)[1]" no self.outputs, maybe like this
   "max_length = tf.shape(outputs)[1]"

2. i can not find some DynamicRNNLayer example, the examples:

   input_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="input_seqs")
   network = tl.layers.EmbeddingInputlayer(
                inputs = input_seqs,
                vocabulary_size = vocab_size,
                embedding_size = embedding_size,
                name = 'seq_embedding')
    network = tl.layers.DynamicRNNLayer(network,
                cell_fn = tf.nn.rnn_cell.BasicLSTMCell,
                n_hidden = embedding_size,
                dropout = 0.7,
                sequence_length = tl.layers.retrieve_seq_length_op2(input_seqs),
                return_seq_2d = True,     # stack denselayer or compute cost after it
                name = 'dynamic_rnn',)
    network = tl.layers.DenseLayer(network, n_units=vocab_size,
                act=tf.identity, name="output")
   

   i can not understand "shape=[batch_size, None]" i just write None? or n_step(max)
   if my data like this: (Sentiment Analysis)

   x = [[2, 1, 8, 9, 2]
        [2, 4]
        [1, 1, 3, 5] 
        [6, 3, 2]]
   y = [1, 0, 1, 1]
   

   i padding with zero

    x = [[2, 1, 8, 9, 2]
         [2, 4, 0, 0, 0]
         [1, 1, 3, 5, 0] 
         [6, 3, 2, 0, 0]]
   

   how to use DynamicRNNLayer?