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當我使用tf.nn.dynamic_rnn構造LSTM時，關於name_scope的問題導致其失敗，平臺爲WINDOWS 10.我無法解決它，任何幫助將不勝感激。ValueError：變量A3C_net/basic_lstm_cell /權重不存在或未使用tf.get_variable（）創建
非常感謝！
整個犯錯是如下：
回溯（最近通話最後一個）：
File "a3c_prediction.py", line 157, in <module>
main()
File "a3c_prediction.py", line 88, in main
global_network = ACLSTMNetwork(ACTION_SIZE, device)
File "C:\Users\xjZhan\Desktop\a3c_predication\a3c_net_lstm.py", line 139, in __init__
scope = scope)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\rnn.py", line 553, in dynamic_rnn
dtype=dtype)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\rnn.py", line 720, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2623, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2456, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2406, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\rnn.py", line 703, in _time_step
skip_conditionals=True)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\rnn.py", line 177, in _rnn_step
new_output, new_state = call_cell()
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\rnn.py", line 691, in <lambda>
call_cell = lambda: cell(input_t, state)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\contrib\rnn\python\ops\core_rnn_cell_impl.py", line 241, in __call__
concat = _linear([inputs, h], 4 * self._num_units, True)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\contrib\rnn\python\ops\core_rnn_cell_impl.py", line 1044, in _linear
_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size], dtype=dtype)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 1049, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 948, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 356, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 341, in _true_getter
use_resource=use_resource)
File "C:\Program Install\Anaconda3-4.1.0\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 671, in _get_single_variable
"VarScope?" % name)
ValueError異常：可變A3C_net/basic_lstm_cell /權重不存在，或者不是用TF創建。 get_variable（）。你是否想在VarScope中設置重用=無？
scope_name = "A3C_net"
with tf.device(self._device),tf.variable_scope(scope_name) as scope:
self.W_conv1, self.b_conv1 = self._conv_variable([8, 8, 4, 16])
self.W_conv2, self.b_conv2 = self._conv_variable([4, 4, 16, 32])
self.W_fc1, self.b_fc1 = self._fc_variable([2592, 256])
# lstm
self.lstm = tf.contrib.rnn.BasicLSTMCell(256, state_is_tuple=True, reuse=True)#
# weight for policy output layer
self.W_fc2, self.b_fc2 = self._fc_variable([256, action_size])
# weight for value output layer
self.W_fc3, self.b_fc3 = self._fc_variable([256, 1])
self.s = tf.placeholder("float", [None, 84, 84, 4])
h_conv1 = tf.nn.relu(self._conv2d(self.s, self.W_conv1, 4) + self.b_conv1)
h_conv2 = tf.nn.relu(self._conv2d(h_conv1, self.W_conv2, 2) + self.b_conv2)
h_conv2_flat = tf.reshape(h_conv2, [-1, 2592])
h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, self.W_fc1) + self.b_fc1)
h_fc1_reshaped = tf.reshape(h_fc1, [1,-1,256]) # batches steps inputs
self.step_size = tf.placeholder(tf.float32, [1])
self.initial_lstm_state0 = tf.placeholder(tf.float32, [1, 256])
self.initial_lstm_state1 = tf.placeholder(tf.float32, [1, 256])
self.initial_lstm_state = tf.contrib.rnn.LSTMStateTuple(
self.initial_lstm_state0,
self.initial_lstm_state1)
lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(self.lstm,
h_fc1_reshaped,initial_state = self.initial_lstm_state,dtype= tf.float32,
sequence_length = self.step_size,
time_major = False,
scope = scope)
lstm_outputs = tf.reshape(lstm_outputs, [-1,256])
# policy (output)
self.pi = tf.nn.softmax(tf.matmul(lstm_outputs, self.W_fc2) + self.b_fc2)
# value (output)
v_ = tf.matmul(lstm_outputs, self.W_fc3) + self.b_fc3
self.v = tf.reshape(v_, [-1])
scope.reuse_variables()
self.W_lstm = tf.get_variabl("basic_lstm_cell/weights")
self.b_lstm = tf.get_variable("basic_lstm_cell/biases")
self.reset_state()