最近在做生成电视剧本小项目，遇到以下报错
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-a2d9a7091ca5> in <module>()
10 input_data_shape = tf.shape(input_text)
11 cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
---> 12 logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)
13
14 # Probabilities for generating words
<ipython-input-13-5720cbc33483> in build_nn(cell, rnn_size, input_data, vocab_size, embed_dim)
11 # TODO: Implement Function
12 embed_inputs = get_embed(input_data,vocab_size,embed_dim)
---> 13 outputs, final_state = build_rnn(cell,embed_inputs)
14 logits = tf.contrib.layers.fully_connected(outputs,vocab_size)
15 return logits, final_state
<ipython-input-12-a2502a6f4ee0> in build_rnn(cell, inputs)
7 """
8 # TODO: Implement Function
----> 9 outputs, state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
10 final_state = tf.identity(state,name=‘final_state‘)
11 return outputs, final_state
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py in dynamic_rnn(cell, inputs, sequence_length, initial_state, dtype, parallel_iterations, swap_memory, time_major, scope)
551 swap_memory=swap_memory,
552 sequence_length=sequence_length,
--> 553 dtype=dtype)
554
555 # Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth].
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py in _dynamic_rnn_loop(cell, inputs, initial_state, parallel_iterations, swap_memory, sequence_length, dtype)
718 loop_vars=(time, output_ta, state),
719 parallel_iterations=parallel_iterations,
--> 720 swap_memory=swap_memory)
721
722 # Unpack final output if not using output tuples.
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)
2621 context = WhileContext(parallel_iterations, back_prop, swap_memory, name)
2622 ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, context)
-> 2623 result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
2624 return result
2625
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
2454 self.Enter()
2455 original_body_result, exit_vars = self._BuildLoop(
-> 2456 pred, body, original_loop_vars, loop_vars, shape_invariants)
2457 finally:
2458 self.Exit()
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
2404 structure=original_loop_vars,
2405 flat_sequence=vars_for_body_with_tensor_arrays)
-> 2406 body_result = body(*packed_vars_for_body)
2407 if not nest.is_sequence(body_result):
2408 body_result = [body_result]
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py in _time_step(time, output_ta_t, state)
703 skip_conditionals=True)
704 else:
--> 705 (output, new_state) = call_cell()
706
707 # Pack state if using state tuples
/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py in <lambda>()
689
690 input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
--> 691 call_cell = lambda: cell(input_t, state)
692
693 if sequence_length is not None:
/usr/local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py in __call__(self, inputs, state, scope)
951 state, [0, cur_state_pos], [-1, cell.state_size])
952 cur_state_pos += cell.state_size
--> 953 cur_inp, new_state = cell(cur_inp, cur_state)
954 new_states.append(new_state)
955 new_states = (tuple(new_states) if self._state_is_tuple else
/usr/local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py in __call__(self, inputs, state, scope)
711 self._recurrent_input_noise,
712 self._input_keep_prob)
--> 713 output, new_state = self._cell(inputs, state, scope)
714 if _should_dropout(self._state_keep_prob):
715 new_state = self._dropout(new_state, "state",
/usr/local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py in __call__(self, inputs, state, scope)
233 def __call__(self, inputs, state, scope=None):
234 """Long short-term memory cell (LSTM)."""
--> 235 with _checked_scope(self, scope or "basic_lstm_cell", reuse=self._reuse):
236 # Parameters of gates are concatenated into one multiply for efficiency.
237 if self._state_is_tuple:
/usr/local/lib/python3.5/contextlib.py in __enter__(self)
57 def __enter__(self):
58 try:
---> 59 return next(self.gen)
60 except StopIteration:
61 raise RuntimeError("generator didn‘t yield") from None
/usr/local/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py in _checked_scope(cell, scope, reuse, **kwargs)
75 "this error will remain until then.)"
76 % (cell, cell_scope.name, scope_name, type(cell).__name__,
---> 77 type(cell).__name__))
78 else:
79 weights_found = False
ValueError: Attempt to reuse RNNCell <tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.BasicLSTMCell object at 0x7f1a3c448390> with a different variable scope than its first use. First use of cell was with scope ‘rnn/multi_rnn_cell/cell_0/basic_lstm_cell‘, this attempt is with scope ‘rnn/multi_rnn_cell/cell_1/basic_lstm_cell‘. Please create a new instance of the cell if you would like it to use a different set of weights. If before you were using: MultiRNNCell([BasicLSTMCell(...)] * num_layers), change to: MultiRNNCell([BasicLSTMCell(...) for _ in range(num_layers)]). If before you were using the same cell instance as both the forward and reverse cell of a bidirectional RNN, simply create two instances (one for forward, one for reverse). In May 2017, we will start transitioning this cell‘s behavior to use existing stored weights, if any, when it is called with scope=None (which can lead to silent model degradation, so this error will remain until then.)
在此先给出解决方法，后续整理一下，写上原因
初始有报错代码：
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
drop = tf.contrib.rnn.DropoutWrapper(lstm)
cell = tf.contrib.rnn.MultiRNNCell([drop for _ in range(2)])
initial_state = cell.zero_state(batch_size, tf.float32)
InitailState = tf.identity(initial_state, name=‘initial_state‘)
return cell, InitailState
改进后的代码：
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
#lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
#drop = tf.contrib.rnn.DropoutWrapper(lstm)
#cell = tf.contrib.rnn.MultiRNNCell([drop for _ in range(2)])
def lstm_cell():
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size,reuse=tf.get_variable_scope().reuse)
drop = tf.contrib.rnn.DropoutWrapper(lstm)
return drop
cell = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(2)])
initial_state = cell.zero_state(batch_size, tf.float32)
InitailState = tf.identity(initial_state, name=‘initial_state‘)
return cell, InitailState
可参考：
ValueError: Attempt to reuse RNNCell with a different variable scope than its first use.
How to reuse weights in MultiRNNCell?
时间： 2024-10-19 08:50:08