当我调用函数tf.nn.bidirectional_dynamic_rnn时，它返回 变量bidirectional_rnn / fw / lstm_cell / weights已经存在，不允许。你的意思是在VarScope中设置reuse = True吗？最初定义于：
我的代码：
tf.reset_default_graph()
sess = tf.InteractiveSession()
PAD = 0
EOS = 1
sequence_size = 10 #vocal_size
input_embedding_size = 20 #length of sequence
encoder_hidden_units = 20
decoder_hidden_units = encoder_hidden_units * 2
encoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32,
name='encoder_inputs')
#length of each sequence in the batch
encoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32,
name='encoder_inputs_length')
decoder_targets = tf.placeholder(shape=(None, None), dtype=tf.int32,
name='decoder_targets')
embeddings = tf.Variable(tf.random_uniform([sequence_size,
input_embedding_size], -1.0, 1), dtype=tf.float32 )
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
encoder_inputs)
encoder_cell = tf.contrib.rnn.LSTMCell(encoder_hidden_units)
( (encoder_fw_outputs,
encoder_bw_outputs),
(encoder_fw_final_state,
encoder_bw_final_state)) = (
tf.nn.bidirectional_dynamic_rnn(cell_fw=encoder_cell,
cell_bw=encoder_cell,
inputs=encoder_inputs_embedded,
sequence_length=encoder_inputs_length,
dtype=tf.float32, time_major=False)
)
我认为这是错误：
ValueError Traceback (most recent call last)
<ipython-input-211-41a8d71d81df> in <module>()
23
inputs=encoder_inputs_embedded,
24
sequence_length=encoder_inputs_length,
---> 25 dtype=tf.float32,
time_major=False)
26 )
27
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in
bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length,
initial_state_fw, initial_state_bw, dtype, parallel_iterations,
swap_memory, time_major, scope)
348 initial_state=initial_state_fw, dtype=dtype,
349 parallel_iterations=parallel_iterations,
swap_memory=swap_memory,
--> 350 time_major=time_major, scope=fw_scope)
351
352 # Backward direction
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in dynamic_rnn(cell, inputs,
sequence_length, initial_state, dtype, parallel_iterations,
swap_memory, time_major, scope)
544 swap_memory=swap_memory,
545 sequence_length=sequence_length,
--> 546 dtype=dtype)
547
548 # Outputs of _dynamic_rnn_loop are always shaped [time, batch,
depth].
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in _dynamic_rnn_loop(cell,
inputs, initial_state, parallel_iterations, swap_memory,
sequence_length, dtype)
711 loop_vars=(time, output_ta, state),
712 parallel_iterations=parallel_iterations,
--> 713 swap_memory=swap_memory)
714
715 # Unpack final output if not using output tuples.
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/control_flow_ops.pyc in
while_loop(cond, body, loop_vars, shape_invariants,
parallel_iterations, back_prop, swap_memory, name)
2603 context = WhileContext(parallel_iterations, back_prop,
swap_memory, name)
2604 ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, context)
-> 2605 result = context.BuildLoop(cond, body, loop_vars,
shape_invariants)
2606 return result
2607
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/control_flow_ops.pyc in
BuildLoop(self, pred, body, loop_vars, shape_invariants)
2436 self.Enter()
2437 original_body_result, exit_vars = self._BuildLoop(
-> 2438 pred, body, original_loop_vars, loop_vars,
shape_invariants)
2439 finally:
2440 self.Exit()
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/control_flow_ops.pyc in
_BuildLoop(self, pred, body, original_loop_vars, loop_vars,
shape_invariants)
2386 structure=original_loop_vars,
2387 flat_sequence=vars_for_body_with_tensor_arrays)
-> 2388 body_result = body(*packed_vars_for_body)
2389 if not nest.is_sequence(body_result):
2390 body_result = [body_result]
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in _time_step(time,
output_ta_t, state)
694 call_cell=call_cell,
695 state_size=state_size,
--> 696 skip_conditionals=True)
697 else:
698 (output, new_state) = call_cell()
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in _rnn_step(time,
sequence_length, min_sequence_length, max_sequence_length,
zero_output, state, call_cell, state_size, skip_conditionals)
175 # steps. This is faster when max_seq_len is equal to the
number of unrolls
176 # (which is typical for dynamic_rnn).
--> 177 new_output, new_state = call_cell()
178 nest.assert_same_structure(state, new_state)
179 new_state = nest.flatten(new_state)
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/rnn.pyc in <lambda>()
682
683 input_t = nest.pack_sequence_as(structure=inputs,
flat_sequence=input_t)
--> 684 call_cell = lambda: cell(input_t, state)
685
686 if sequence_length is not None:
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.pyc in
__call__(self, inputs, state, scope)
336 # i = input_gate, j = new_input, f = forget_gate, o =
output_gate
337 lstm_matrix = _linear([inputs, m_prev], 4 * self._num_units,
bias=True,
--> 338 scope=scope)
339 i, j, f, o = array_ops.split(
340 value=lstm_matrix, num_or_size_splits=4, axis=1)
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.pyc in
_linear(args, output_size, bias, bias_start, scope)
745 with vs.variable_scope(scope) as outer_scope:
746 weights = vs.get_variable(
--> 747 "weights", [total_arg_size, output_size],
dtype=dtype)
748 if len(args) == 1:
749 res = math_ops.matmul(args[0], weights)
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/variable_scope.pyc in
get_variable(name, shape, dtype, initializer, regularizer, trainable,
collections, caching_device, partitioner, validate_shape,
custom_getter)
986 collections=collections, caching_device=caching_device,
987 partitioner=partitioner, validate_shape=validate_shape,
--> 988 custom_getter=custom_getter)
989 get_variable_or_local_docstring = (
990 """%s
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/variable_scope.pyc in
get_variable(self, var_store, name, shape, dtype, initializer,
regularizer, trainable, collections, caching_device, partitioner,
validate_shape, custom_getter)
888 collections=collections, caching_device=caching_device,
889 partitioner=partitioner, validate_shape=validate_shape,
--> 890 custom_getter=custom_getter)
891
892 def _get_partitioned_variable(self,
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/variable_scope.pyc in
get_variable(self, name, shape, dtype, initializer, regularizer,
reuse, trainable, collections, caching_device, partitioner,
validate_shape, custom_getter)
346 reuse=reuse, trainable=trainable,
collections=collections,
347 caching_device=caching_device, partitioner=partitioner,
--> 348 validate_shape=validate_shape)
349
350 def _get_partitioned_variable(
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/variable_scope.pyc in
_true_getter(name, shape, dtype, initializer, regularizer, reuse,
trainable, collections, caching_device, partitioner, validate_shape)
331 initializer=initializer, regularizer=regularizer,
reuse=reuse,
332 trainable=trainable, collections=collections,
--> 333 caching_device=caching_device,
validate_shape=validate_shape)
334
335 if custom_getter is not None:
/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/python/ops/variable_scope.pyc in
_get_single_variable(self, name, shape, dtype, initializer,
regularizer, partition_info, reuse, trainable, collections,
caching_device, validate_shape)
637 " Did you mean to set reuse=True in
VarScope? "
638 "Originally defined at:\n\n%s" % (
--> 639 name,
"".join(traceback.format_list(tb))))
640 found_var = self._vars[name]
641 if not shape.is_compatible_with(found_var.get_shape()):
ValueError: Variable bidirectional_rnn/fw/lstm_cell/weights already
exists, disallowed. Did you mean to set reuse=True in VarScope?
Originally defined at:
File "/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py",
line
747, in _linear
"weights", [total_arg_size, output_size], dtype=dtype)
File "/home/cesar/anaconda2/envs/tensorflow/lib/python2.7/site-
packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py",
line 338, in __call__
scope=scope)
File "<ipython-input-23-f4f28501e56f>", line 24, in <module>
time_major=True
答案 0 :(得分：0)
我会尝试按如下方式定义编码器：
with tf.variable_scope('encoder_cell_fw'):
encoder_cell_fw = LSTMCell(encoder_hidden_units)
with tf.variable_scope('encoder_cell_bw'):
encoder_cell_bw = LSTMCell(encoder_hidden_units)
( (encoder_fw_outputs,
encoder_bw_outputs),
(encoder_fw_final_state,
encoder_bw_final_state)) = (
tf.nn.bidirectional_dynamic_rnn(cell_fw=encoder_cell_fw,
cell_bw=encoder_cell_bw,
inputs=encoder_inputs_embedded,
sequence_length=encoder_inputs_length,
dtype=tf.float32, time_major=False))
它适用于我，这是因为如果它没有设法两次使用相同的名称，bw是fw，另一个选项将使用reuse = True定义，但它没有用。
答案 1 :(得分：0)
尝试这种方式： 在块的开头插入tf.reset_default_graph（）。 通过在同一图表上多次运行脚本，您可以将变量添加到图表中以进行每次运行...