版本：Python 2.7.13 和 TF 1.2.1
背景：我正在尝试创建一个 LSTM 单元并传递 N x M 的输入并输出 N x M+1。我想将输出通过 softmax 层，然后通过具有负对数似然损失函数的 Adam 优化器。
问题：如标题所述，当我尝试设置我的 training_op = optimizer.minimize(nll) 时，它会崩溃并询问变量范围。我应该怎么办？
代码：
with tf.variable_scope('lstm1', reuse=True):
LSTM_cell_1 = tf.nn.rnn_cell.LSTMCell(num_units=n_neurons, activation=tf.nn.relu)
rnn_outputs_1, states_1 = tf.nn.dynamic_rnn(LSTM_cell_1, X_1, dtype=tf.float32)
rnn_outputs_1 = tf.nn.softmax(rnn_outputs_1)
stacked_rnn_outputs_1 = tf.reshape(rnn_outputs_1, [-1, n_neurons])
stacked_outputs_1 = tf.layers.dense(stacked_rnn_outputs_1, n_outputs)
outputs_1 = tf.reshape(stacked_outputs_1, [-1, n_steps, n_outputs])
mu = tf.Variable(np.float32(1))
sigma = tf.Variable(np.float32(1))
def normal_log(X, mu, sigma, left=-np.inf, right=np.inf):
val = -tf.log(tf.constant(np.sqrt(2.0 * np.pi), dtype=tf.float32) * sigma) - \
tf.pow(X - mu, 2) / (tf.constant(2.0, dtype=tf.float32) * tf.pow(sigma, 2))
return val
nll = -tf.reduce_sum(normal_log(outputs, mu, sigma))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(nll)
错误信息：
ValueError Traceback (most recent call last)
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc in minimize(self, loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss)
323
324 return self.apply_gradients(grads_and_vars, global_step=global_step,
--> 325 name=name)
326
327 def compute_gradients(self, loss, var_list=None,
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc in apply_gradients(self, grads_and_vars, global_step, name)
444 ([str(v) for _, _, v in converted_grads_and_vars],))
445 with ops.control_dependencies(None):
--> 446 self._create_slots([_get_variable_for(v) for v in var_list])
447 update_ops = []
448 with ops.name_scope(name, self._name) as name:
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/adam.pyc in _create_slots(self, var_list)
126 # Create slots for the first and second moments.
127 for v in var_list:
--> 128 self._zeros_slot(v, "m", self._name)
129 self._zeros_slot(v, "v", self._name)
130
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc in _zeros_slot(self, var, slot_name, op_name)
764 named_slots = self._slot_dict(slot_name)
765 if _var_key(var) not in named_slots:
--> 766 named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name)
767 return named_slots[_var_key(var)]
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/slot_creator.pyc in create_zeros_slot(primary, name, dtype, colocate_with_primary)
172 return create_slot_with_initializer(
173 primary, initializer, slot_shape, dtype, name,
--> 174 colocate_with_primary=colocate_with_primary)
175 else:
176 val = array_ops.zeros(slot_shape, dtype=dtype)
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/slot_creator.pyc in create_slot_with_initializer(primary, initializer, shape, dtype, name, colocate_with_primary)
144 with ops.colocate_with(primary):
145 return _create_slot_var(primary, initializer, "", validate_shape, shape,
--> 146 dtype)
147 else:
148 return _create_slot_var(primary, initializer, "", validate_shape, shape,
/usr/local/lib/python2.7/site-packages/tensorflow/python/training/slot_creator.pyc in _create_slot_var(primary, val, scope, validate_shape, shape, dtype)
64 use_resource=_is_resource(primary),
65 shape=shape, dtype=dtype,
---> 66 validate_shape=validate_shape)
67 variable_scope.get_variable_scope().set_partitioner(current_partitioner)
68
/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)
960 collections=collections, caching_device=caching_device,
961 partitioner=partitioner, validate_shape=validate_shape,
--> 962 use_resource=use_resource, custom_getter=custom_getter)
963
964 def _get_partitioned_variable(self,
/usr/local/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, use_resource, custom_getter)
365 reuse=reuse, trainable=trainable, collections=collections,
366 caching_device=caching_device, partitioner=partitioner,
--> 367 validate_shape=validate_shape, use_resource=use_resource)
368
369 def _get_partitioned_variable(
/usr/local/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, use_resource)
350 trainable=trainable, collections=collections,
351 caching_device=caching_device, validate_shape=validate_shape,
--> 352 use_resource=use_resource)
353
354 if custom_getter is not None:
/usr/local/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, use_resource)
662 " Did you mean to set reuse=True in VarScope? "
663 "Originally defined at:\n\n%s" % (
--> 664 name, "".join(traceback.format_list(tb))))
665 found_var = self._vars[name]
666 if not shape.is_compatible_with(found_var.get_shape()):
ValueError: Variable lstm1/dense/kernel/Adam_1/ already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "<ipython-input-107-eed033b85dc0>", line 11, in <module>
training_op = optimizer.minimize(nll)
File "/usr/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/usr/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):