我是tensorflow的新手，我有一个描述我的神经网络以及该网络所需的所有方法的类。我有一种特定的方法来提供神经元值。我最初将神经元声明为占位符，希望使用get_neuron方法将值提供给神经元（如果可用）。在运行时，出现运行时错误，提示RuntimeError: The Session graph is empty. Add operations to the graph before calling run()。谁能帮我解决这个问题？究竟是什么意思？我不了解添加操作的确切含义，因为我一直在使用feed_dict操作并运行会话。如果我为张量的每个计算运行会话，或者仅在具有最终输出时才在最后运行会话，这也会带来任何不利之处。我之所以这样问，是因为我想知道这是否会影响张量流的有效性，因为它在使用优化程序时遵循图进行优化。
def get_neuron(self, a, b):
#'while a being the name of the neuron and b being the value of that neuron'
with tf.Session() as sess_n:
sess_n.run(a, feed_dict={a: b})
return
编辑：
这就是我调用上述函数的方式，即
knowledge_out = knowledge.run(carollis_inp)
知识是从具有方法run的知识知识转移类创建的对象，该函数的第一行是
self.get_neuron(self.neuron_input, carollis_input)
确切显示的错误是
[ERROR] [1566241992.292524, 15.300000]: bad callback: <function joint_callback at 0x7f42221982f0>
Traceback (most recent call last):
File "/opt/ros/melodic/lib/python2.7/dist-packages/rospy/topics.py", line 748, in _invoke_callback
cb(msg, cb_args)
File "/home/microbot/catkin_ws/src/spider/spider_control/control1.py", line 60, in joint_callback
knowledge_out = knowledge.run(carollis_inp)
File "/home/microbot/catkin_ws/src/spider/spider_control/knowledge_transfer.py", line 99, in run
self.get_neuron(self.neuron_input, carollis_input)
File "/home/microbot/catkin_ws/src/spider/spider_control/knowledge_transfer.py", line 81, in get_neuron
sess_n.run(a, feed_dict={a: b})
File "/home/microbot/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/microbot/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1098, in _run
raise RuntimeError('The Session graph is empty. Add operations to the '
RuntimeError: The Session graph is empty. Add operations to the graph before calling run().
EDIT2：
我有一个名为Knowledge_transfer的神经网络，它接受8个输入，给出4个输出，然后进行软最大化，有点像多分类问题。有一个此类方法run接受大小为8的输入数组，然后返回大小为4的输出数组。在run方法内部，它将给定的输入参数输入到大小为8的输入占位符，然后对图层和权重进行一些处理，最后给出输出。
例：
考虑到第一个参数是一个占位符，并且两个参数的大小相同，我想创建一个类成员方法，该方法接受两个输入并将feed_dict第一个第二个输入参数传递给第一个参数。
Edit3： 我将输入提供给输入层，然后使用输入函数计算输入层的输出，然后将其提供给隐藏层的输入函数，然后将输入函数的输出提供给隐藏层以通过Leaky_rectified线性输出，然后将其输出传递给输出层的输入函数，方法是将其输出传递给softmax函数，然后将该softmax输出作为NN的输出。
代码如下：
self.neuron_input = tf.compat.v1.placeholder(tf.float32, shape=(self.neurons, 1))
self.weight_in = tf.get_variable(name="Weight_input", dtype=tf.float32, shape=[self.neurons, 1], initializer=self.weight_initer)
self.neuron_hid = tf.compat.v1.placeholder(tf.float32, shape=(int(self.neurons/2), 1))
self.weight_initer1 = tf.truncated_normal_initializer(mean=1.0, stddev=0.01)
self.weight_hid = tf.get_variable(name="Weight_hidden", dtype=tf.float32, shape=[self.neurons, 1], initializer=self.weight_initer1)
self.neuron_out = tf.compat.v1.placeholder(tf.float32, shape=(4, 1))
self.weight_initer2 = tf.truncated_normal_initializer(mean=2.0, stddev=0.01)
self.weight_out = tf.get_variable(name="Weight_output", dtype=tf.float32, shape=[4, 2], initializer=self.weight_initer2)
self.bias_initer =tf.truncated_normal_initializer(mean=0-1, stddev=0.01)
self.bias_in =tf.get_variable(name="Bias_input", dtype=tf.float32, shape=[self.neurons, 1], initializer=self.bias_initer)
self.bias_initer1 =tf.truncated_normal_initializer(mean=0-2, stddev=0.01)
self.bias_hid = tf.get_variable(name="Bias_hidden", dtype=tf.float32, shape=[self.neurons, 1], initializer=self.bias_initer1)
self.bias_initer2 =tf.truncated_normal_initializer(mean=0-3, stddev=0.01)
self.bias_out = tf.get_variable(name="Bias_output", dtype=tf.float32, shape=[4, 1], initializer=self.bias_initer2)
，然后run（）函数如下：
def run(self, carollis_input):
self.normalization(carollis_input)
#'finding the output of the input layer'
knowledge_input = tf.add(tf.multiply(self.neuron_input, self.weight_in), self.bias_in)
#'calculating the input for the hidden layer'
knowledge_hidden = tf.add(tf.multiply(knowledge_input, self.weight_in), self.bias_hid)
#'calculating the output of hidden layer'
knowledge_hidden_output = 3.14*(tf.add(tf.multiply(knowledge_hidden, self.weight_hid), self.bias_hid))#input function of hidden layer
knowledge_hidden_out = tf.nn.leaky_relu(self.neuron_hid, alpha=0.01, name='leaky_relu')
with tf.Session() as sess1_2:
sess1_2.run(knowledge_hidden_out, feed_dict={self.neuron_input: carollis_input, self.neuron_hid: knowledge_hidden_output})
#'calculating the input of output layer'
tf.reshape(knowledge_hidden_out, [4, 2])#for quadrant method
in_out = tf.add(tf.multiply(knowledge_hidden_out, self.weight_out), self.bias_out)
with tf.Session as s:
s.run(in_out)
#'finding the softmax output of the neurons'
softmax_output = np.array(4)
softmax_output = self.out_softmax(in_out) # this gives the softmax output and stores it in the newly created array
return softmax_output
错误如下：
sess1_2.run(knowledge_hidden_out, feed_dict={self.neuron_input: carollis_input, self.neuron_hid: knowledge_hidden_output})
File "/home/microbot/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/microbot/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1098, in _run
raise RuntimeError('The Session graph is empty. Add operations to the '
RuntimeError: The Session graph is empty. Add operations to the graph before calling run().