我正在使用OpenAI基线来训练RL（deepq）模型。输入是19个功能：
observation_space = spaces.Box(0, 100, (19, 1), dtype=np.float_)
，输出为：
action_space =spaces.Discrete(6)
来自以下所有模型变量：
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
就像：
Var 0: <tf.Variable 'deepq/eps:0' shape=() dtype=float32_ref>
Var 1: <tf.Variable 'deepq/q_func/mlp_fc0/w:0' shape=(19, 64) dtype=float32_ref>
Var 2: <tf.Variable 'deepq/q_func/mlp_fc0/b:0' shape=(64,) dtype=float32_ref>
Var 3: <tf.Variable 'deepq/q_func/mlp_fc1/w:0' shape=(64, 64) dtype=float32_ref>
Var 4: <tf.Variable 'deepq/q_func/mlp_fc1/b:0' shape=(64,) dtype=float32_ref>
Var 5: <tf.Variable 'deepq/q_func/action_value/fully_connected/weights:0' shape=(64, 256) dtype=float32_ref>
Var 6: <tf.Variable 'deepq/q_func/action_value/fully_connected/biases:0' shape=(256,) dtype=float32_ref>
Var 7: <tf.Variable 'deepq/q_func/action_value/fully_connected_1/weights:0' shape=(256, 6) dtype=float32_ref>
Var 8: <tf.Variable 'deepq/q_func/action_value/fully_connected_1/biases:0' shape=(6,) dtype=float32_ref>
Var 9: <tf.Variable 'deepq/q_func/state_value/fully_connected/weights:0' shape=(64, 256) dtype=float32_ref>
Var 10: <tf.Variable 'deepq/q_func/state_value/fully_connected/biases:0' shape=(256,) dtype=float32_ref>
Var 11: <tf.Variable 'deepq/q_func/state_value/fully_connected_1/weights:0' shape=(256, 1) dtype=float32_ref>
Var 12: <tf.Variable 'deepq/q_func/state_value/fully_connected_1/biases:0' shape=(1,) dtype=float32_ref>
Var 13: <tf.Variable 'deepq/target_q_func/mlp_fc0/w:0' shape=(19, 64) dtype=float32_ref>
Var 14: <tf.Variable 'deepq/target_q_func/mlp_fc0/b:0' shape=(64,) dtype=float32_ref>
Var 15: <tf.Variable 'deepq/target_q_func/mlp_fc1/w:0' shape=(64, 64) dtype=float32_ref>
Var 16: <tf.Variable 'deepq/target_q_func/mlp_fc1/b:0' shape=(64,) dtype=float32_ref>
Var 17: <tf.Variable 'deepq/target_q_func/action_value/fully_connected/weights:0' shape=(64, 256) dtype=float32_ref>
Var 18: <tf.Variable 'deepq/target_q_func/action_value/fully_connected/biases:0' shape=(256,) dtype=float32_ref>
Var 19: <tf.Variable 'deepq/target_q_func/action_value/fully_connected_1/weights:0' shape=(256, 6) dtype=float32_ref>
Var 20: <tf.Variable 'deepq/target_q_func/action_value/fully_connected_1/biases:0' shape=(6,) dtype=float32_ref>
Var 21: <tf.Variable 'deepq/target_q_func/state_value/fully_connected/weights:0' shape=(64, 256) dtype=float32_ref>
Var 22: <tf.Variable 'deepq/target_q_func/state_value/fully_connected/biases:0' shape=(256,) dtype=float32_ref>
Var 23: <tf.Variable 'deepq/target_q_func/state_value/fully_connected_1/weights:0' shape=(256, 1) dtype=float32_ref>
Var 24: <tf.Variable 'deepq/target_q_func/state_value/fully_connected_1/biases:0' shape=(1,) dtype=float32_ref>
Var 25: <tf.Variable 'deepq_1/beta1_power:0' shape=() dtype=float32_ref>
Var 26: <tf.Variable 'deepq_1/beta2_power:0' shape=() dtype=float32_ref>
Var 27: <tf.Variable 'deepq/deepq/q_func/mlp_fc0/w/Adam:0' shape=(19, 64) dtype=float32_ref>
Var 28: <tf.Variable 'deepq/deepq/q_func/mlp_fc0/w/Adam_1:0' shape=(19, 64) dtype=float32_ref>
Var 29: <tf.Variable 'deepq/deepq/q_func/mlp_fc0/b/Adam:0' shape=(64,) dtype=float32_ref>
Var 30: <tf.Variable 'deepq/deepq/q_func/mlp_fc0/b/Adam_1:0' shape=(64,) dtype=float32_ref>
Var 31: <tf.Variable 'deepq/deepq/q_func/mlp_fc1/w/Adam:0' shape=(64, 64) dtype=float32_ref>
Var 32: <tf.Variable 'deepq/deepq/q_func/mlp_fc1/w/Adam_1:0' shape=(64, 64) dtype=float32_ref>
Var 33: <tf.Variable 'deepq/deepq/q_func/mlp_fc1/b/Adam:0' shape=(64,) dtype=float32_ref>
Var 34: <tf.Variable 'deepq/deepq/q_func/mlp_fc1/b/Adam_1:0' shape=(64,) dtype=float32_ref>
Var 35: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected/weights/Adam:0' shape=(64, 256) dtype=float32_ref>
Var 36: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected/weights/Adam_1:0' shape=(64, 256) dtype=float32_ref>
Var 37: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected/biases/Adam:0' shape=(256,) dtype=float32_ref>
Var 38: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected/biases/Adam_1:0' shape=(256,) dtype=float32_ref>
Var 39: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected_1/weights/Adam:0' shape=(256, 6) dtype=float32_ref>
Var 40: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected_1/weights/Adam_1:0' shape=(256, 6) dtype=float32_ref>
Var 41: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam:0' shape=(6,) dtype=float32_ref>
Var 42: <tf.Variable 'deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam_1:0' shape=(6,) dtype=float32_ref>
Var 43: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected/weights/Adam:0' shape=(64, 256) dtype=float32_ref>
Var 44: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected/weights/Adam_1:0' shape=(64, 256) dtype=float32_ref>
Var 45: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected/biases/Adam:0' shape=(256,) dtype=float32_ref>
Var 46: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected/biases/Adam_1:0' shape=(256,) dtype=float32_ref>
Var 47: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam:0' shape=(256, 1) dtype=float32_ref>
Var 48: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam_1:0' shape=(256, 1) dtype=float32_ref>
Var 49: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam:0' shape=(1,) dtype=float32_ref>
Var 50: <tf.Variable 'deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam_1:0' shape=(1,) dtype=float32_ref>
使用以下命令保存模型的输出
tf.train.write_graph(sess.graph_def, './model', 'my_deepq.pbtxt')
作为原始buff文件。 profo buff文件如下所示。如何从此原始buff文件中识别输入节点（层）和输出节点（层）的名称？谢谢！
node {
name: "deepq/observation"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_DOUBLE
}
}
attr {
key: "shape"
value {
shape {
dim {
size: -1
}
dim {
size: 19
}
dim {
size: 1
}
}
}
}
}
node {
name: "deepq/ToFloat"
op: "Cast"
input: "deepq/observation"
attr {
key: "DstT"
value {
type: DT_FLOAT
}
}
attr {
key: "SrcT"
value {
type: DT_DOUBLE
}
}
attr {
key: "Truncate"
value {
b: false
}
}
}
:
:
node {
name: "save/Assign_49"
op: "Assign"
input: "deepq_1/beta1_power"
input: "save/RestoreV2:49"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
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key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
}
node {
name: "save/Assign_50"
op: "Assign"
input: "deepq_1/beta2_power"
input: "save/RestoreV2:50"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
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attr {
key: "use_locking"
value {
b: true
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attr {
key: "validate_shape"
value {
b: true
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:
:
node {
name: "deepq_1/group_deps_1"
op: "NoOp"
input: "^deepq_1/Adam"
}
node {
name: "deepq_1/group_deps_2"
op: "NoOp"
input: "^deepq_1/group_deps"
}
node {
name: "deepq_1/group_deps_3"
op: "NoOp"
}
node {
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op: "NoOp"
input: "^deepq/deepq/q_func/action_value/fully_connected/biases/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/weights/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/weights/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/weights/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/weights/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/b/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/b/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/w/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/w/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/b/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/b/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/w/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/w/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/biases/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/weights/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/weights/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam_1/Assign"
input: "^deepq/eps/Assign"
input: "^deepq/q_func/action_value/fully_connected/biases/Assign"
input: "^deepq/q_func/action_value/fully_connected/weights/Assign"
input: "^deepq/q_func/action_value/fully_connected_1/biases/Assign"
input: "^deepq/q_func/action_value/fully_connected_1/weights/Assign"
input: "^deepq/q_func/mlp_fc0/b/Assign"
input: "^deepq/q_func/mlp_fc0/w/Assign"
input: "^deepq/q_func/mlp_fc1/b/Assign"
input: "^deepq/q_func/mlp_fc1/w/Assign"
input: "^deepq/q_func/state_value/fully_connected/biases/Assign"
input: "^deepq/q_func/state_value/fully_connected/weights/Assign"
input: "^deepq/q_func/state_value/fully_connected_1/biases/Assign"
input: "^deepq/q_func/state_value/fully_connected_1/weights/Assign"
input: "^deepq/target_q_func/action_value/fully_connected/biases/Assign"
input: "^deepq/target_q_func/action_value/fully_connected/weights/Assign"
input: "^deepq/target_q_func/action_value/fully_connected_1/biases/Assign"
input: "^deepq/target_q_func/action_value/fully_connected_1/weights/Assign"
input: "^deepq/target_q_func/mlp_fc0/b/Assign"
input: "^deepq/target_q_func/mlp_fc0/w/Assign"
input: "^deepq/target_q_func/mlp_fc1/b/Assign"
input: "^deepq/target_q_func/mlp_fc1/w/Assign"
input: "^deepq/target_q_func/state_value/fully_connected/biases/Assign"
input: "^deepq/target_q_func/state_value/fully_connected/weights/Assign"
input: "^deepq/target_q_func/state_value/fully_connected_1/biases/Assign"
input: "^deepq/target_q_func/state_value/fully_connected_1/weights/Assign"
input: "^deepq_1/beta1_power/Assign"
input: "^deepq_1/beta2_power/Assign"
}
node {
name: "init_1"
op: "NoOp"
input: "^deepq/deepq/q_func/action_value/fully_connected/biases/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/weights/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected/weights/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/action_value/fully_connected_1/weights/Adam/Assign"
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input: "^deepq/deepq/q_func/mlp_fc0/b/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/b/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/w/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc0/w/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/b/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/b/Adam_1/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/w/Adam/Assign"
input: "^deepq/deepq/q_func/mlp_fc1/w/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/biases/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/weights/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected/weights/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/biases/Adam_1/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam/Assign"
input: "^deepq/deepq/q_func/state_value/fully_connected_1/weights/Adam_1/Assign"
input: "^deepq/eps/Assign"
input: "^deepq/q_func/action_value/fully_connected/biases/Assign"
input: "^deepq/q_func/action_value/fully_connected/weights/Assign"
input: "^deepq/q_func/action_value/fully_connected_1/biases/Assign"
input: "^deepq/q_func/action_value/fully_connected_1/weights/Assign"
input: "^deepq/q_func/mlp_fc0/b/Assign"
input: "^deepq/q_func/mlp_fc0/w/Assign"
input: "^deepq/q_func/mlp_fc1/b/Assign"
input: "^deepq/q_func/mlp_fc1/w/Assign"
input: "^deepq/q_func/state_value/fully_connected/biases/Assign"
input: "^deepq/q_func/state_value/fully_connected/weights/Assign"
input: "^deepq/q_func/state_value/fully_connected_1/biases/Assign"
input: "^deepq/q_func/state_value/fully_connected_1/weights/Assign"
input: "^deepq/target_q_func/action_value/fully_connected/biases/Assign"
input: "^deepq/target_q_func/action_value/fully_connected/weights/Assign"
input: "^deepq/target_q_func/action_value/fully_connected_1/biases/Assign"
input: "^deepq/target_q_func/action_value/fully_connected_1/weights/Assign"
input: "^deepq/target_q_func/mlp_fc0/b/Assign"
input: "^deepq/target_q_func/mlp_fc0/w/Assign"
input: "^deepq/target_q_func/mlp_fc1/b/Assign"
input: "^deepq/target_q_func/mlp_fc1/w/Assign"
input: "^deepq/target_q_func/state_value/fully_connected/biases/Assign"
input: "^deepq/target_q_func/state_value/fully_connected/weights/Assign"
input: "^deepq/target_q_func/state_value/fully_connected_1/biases/Assign"
input: "^deepq/target_q_func/state_value/fully_connected_1/weights/Assign"
input: "^deepq_1/beta1_power/Assign"
input: "^deepq_1/beta2_power/Assign"
}
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string_val: "deepq/target_q_func/state_value/fully_connected/biases"
string_val: "deepq/target_q_func/state_value/fully_connected/weights"
string_val: "deepq/target_q_func/state_value/fully_connected_1/biases"
string_val: "deepq/target_q_func/state_value/fully_connected_1/weights"
string_val: "deepq_1/beta1_power"
string_val: "deepq_1/beta2_power"
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}
}
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}
}
}
}
:
:
node {
name: "save/restore_all"
op: "NoOp"
input: "^save/Assign"
input: "^save/Assign_1"
input: "^save/Assign_10"
input: "^save/Assign_11"
input: "^save/Assign_12"
input: "^save/Assign_13"
input: "^save/Assign_14"
input: "^save/Assign_15"
input: "^save/Assign_16"
input: "^save/Assign_17"
input: "^save/Assign_18"
input: "^save/Assign_19"
input: "^save/Assign_2"
input: "^save/Assign_20"
input: "^save/Assign_21"
input: "^save/Assign_22"
input: "^save/Assign_23"
input: "^save/Assign_24"
input: "^save/Assign_25"
input: "^save/Assign_26"
input: "^save/Assign_27"
input: "^save/Assign_28"
input: "^save/Assign_29"
input: "^save/Assign_3"
input: "^save/Assign_30"
input: "^save/Assign_31"
input: "^save/Assign_32"
input: "^save/Assign_33"
input: "^save/Assign_34"
input: "^save/Assign_35"
input: "^save/Assign_36"
input: "^save/Assign_37"
input: "^save/Assign_38"
input: "^save/Assign_39"
input: "^save/Assign_4"
input: "^save/Assign_40"
input: "^save/Assign_41"
input: "^save/Assign_42"
input: "^save/Assign_43"
input: "^save/Assign_44"
input: "^save/Assign_45"
input: "^save/Assign_46"
input: "^save/Assign_47"
input: "^save/Assign_48"
input: "^save/Assign_49"
input: "^save/Assign_5"
input: "^save/Assign_50"
input: "^save/Assign_6"
input: "^save/Assign_7"
input: "^save/Assign_8"
input: "^save/Assign_9"
}
versions {
producer: 27
}
答案 0 :(得分：1)
我无法提供完整答案。
但是，要标识输入节点，通常需要搜索带有“占位符”操作的节点。可能不止一个，特别是当使用复杂的优化器或退出层时。
当涉及到输出时，它甚至是值得的：每个节点本质上都是一个输出。我可以在这里推荐2个选项：寻找具有预期输出名称的操作，例如softmax。或解析整个文件定义并找到终端节点。在其他任何操作中未使用的节点。
另一种选择是加载图形，保存检查点并在张量板上研究它。
我不知道能够很好地表示tf.graph用于python查询的编程工具。
如果您愿意生成.pb文件，则可以使用以下答案的帮助：Given a tensor flow model graph, how to find the input node and output node names