我正在尝试编写一个机器学习程序。最初的想法是训练一个模型（在 q_model 中定义），该模型可以使用RMSProp进行训练。我在这里报告了一个非常简化的代码版本，但它无法正常工作。
错误信息如下：
Traceback (most recent call last): File "/home/samuele/Projects/GBFQI/test/tf_test.py", line 45, in <module> print sess.run({'train':train}, feed_dict={'x:0':x[indx],'a:0':a[indx],'y:0':y[indx]}) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run run_metadata_ptr) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run feed_dict_string, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run target_list, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call raise type(e)(node_def, op, message)tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value b/RMSProp [[Node: RMSProp/update_b/ApplyRMSProp = ApplyRMSProp[T=DT_DOUBLE, _class=["loc:@b"], use_locking=false, _device="/job:localhost/replica:0/task:0/cpu:0"](b, b/RMSProp, b/RMSProp_1, RMSProp/update_b/Cast, RMSProp/update_b/Cast_1, RMSProp/update_b/Cast_2, RMSProp/update_b/Cast_3, gradients/add_grad/tuple/control_dependency_1)]]Caused by op u'RMSProp/update_b/ApplyRMSProp', defined at: File "/home/samuele/Projects/GBFQI/test/tf_test.py", line 38, in <module> train = optimizer.minimize(error) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 325, in minimize name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 456, in apply_gradients update_ops.append(processor.update_op(self, grad)) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 97, in update_op return optimizer._apply_dense(g, self._v) # pylint: disable=protected-access File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/rmsprop.py", line 140, in _apply_dense use_locking=self._use_locking).op File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/gen_training_ops.py", line 449, in apply_rms_prop use_locking=use_locking, name=name) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__ self._traceback = _extract_stack()FailedPreconditionError (see above for traceback): Attempting to use uninitialized value b/RMSProp [[Node: RMSProp/update_b/ApplyRMSProp = ApplyRMSProp[T=DT_DOUBLE, _class=["loc:@b"], use_locking=false, _device="/job:localhost/replica:0/task:0/cpu:0"](b, b/RMSProp, b/RMSProp_1, RMSProp/update_b/Cast, RMSProp/update_b/Cast_1, RMSProp/update_b/Cast_2, RMSProp/update_b/Cast_3, gradients/add_grad/tuple/control_dependency_1)]]我无法解释这个错误，因为模型已经初始化了，实际上如果我运行
print sess.run(q_model(x,a))模型按预期工作，没有引发任何错误。
编辑：
我的问题与这个问题不同。我已经知道
init = tf.initialize_all_variables()sess = tf.Session()sess.run(init)但我不知道在优化后也应该执行这些操作。
回答：
