在tensorflow 0.12

Question

使用MultiRNNCell随着Tensorflow 0.12，已经出现了更改该MultiRNNCell作品，对于初学者来说，state_is_tuple现在设置为True默认的方式，此外，还有关于它的讨论：

state_is_tuple : If True , accepted and returned states are n -tuples, where n = len(cells) . If False , the states are all concatenated along the column axis. This latter behavior will soon be deprecated.

我想知道我究竟怎么可以用同GRU细胞多层RNN，这是我到目前为止的代码：

def _run_rnn(self, inputs): 
     # embedded inputs are passed in here 
     self.initial_state = tf.zeros([self._batch_size, self._hidden_size], tf.float32) 
     cell = tf.nn.rnn_cell.GRUCell(self._hidden_size) 
     cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=self._dropout_placeholder) 
     cell = tf.nn.rnn_cell.MultiRNNCell([cell] * self._num_layers, state_is_tuple=False) 

     outputs, last_state = tf.nn.dynamic_rnn(
      cell = cell, 
      inputs = inputs, 
      sequence_length = self.sequence_length, 
      initial_state = self.initial_state 
     ) 

     return outputs, last_state 

我输入查询词ID并返回相应的嵌入矢量。

ValueError: Dimension 1 in both shapes must be equal, but are 100 and 200 for 'rnn/while/Select_1' (op: 'Select') with input shapes: [?], [64,100], [64,200]

我有一个?的地方是我的占位符中：

def _add_placeholders(self): 
     self.input_placeholder = tf.placeholder(tf.int32, shape=[None, self._max_steps]) 
     self.label_placeholder = tf.placeholder(tf.int32, shape=[None, self._max_steps]) 
     self.sequence_length = tf.placeholder(tf.int32, shape=[None]) 
     self._dropout_placeholder = tf.placeholder(tf.float32) 

Answer 1

你的主要问题是，现在，上面的代码，我在下面的错误运行打招呼在initial_state的设置中。因为你的状态现在是一个元组，（更准确地说是LSTMStateTuple，你不能直接把它分配给tf.zeros，而是使用，

self.initial_state = cell.zero_state(self._batch_size, tf.float32) 

看一看的documentation更多。

---

要使用这个代码，你就需要通过这张在feed_dict。做这样的事，

state = sess.run(model.initial_state) 
for batch in batches: 
    # Logic to add input placeholder in `feed_dict` 
    feed_dict[model.initial_state] = state 
    # Note I'm re-using `state` below 
    (loss, state) = sess.run([model.loss, model.final_state], feed_dict=feed_dict)