对于我的项目，我需要将有向图转换为图的张量流实现，就好像它是神经网络一样。在tensorflow版本1中，我可以将所有输入定义为占位符，然后使用广度优先搜索图为输出生成数据流图。然后，我只需使用feed_dict来输入我的输入。但是，在TensorFlow v2.0中，他们决定完全取消占位符。
在不使用占位符的情况下，如何为每个具有可变输入量并返回可变输出量的图形制作tf.function？
我想生成一个类似tf.function的函数，该函数可用于任意非循环有向图，以便在生成图之后，我可以利用tensorflow GPU支持来连续运行图前馈数千次。
编辑代码示例：
我的图被定义为字典。每个键代表一个节点，并具有另一个字典的对应值，该字典指定具有权重的入站和出站链接。
{
"A": {
"incoming": [("B", 2), ("C", -1)],
"outgoing": [("D", 3)]
}
}
为简洁起见，我省略了B，C和D的条目。 这是我如何在tensorflow v1.0中构造我想要的代码的方法，其中输入只是严格输入到图形的键值列表
def construct_graph(graph_dict, inputs, outputs):
queue = inputs[:]
make_dict = {}
for key, val in graph_dict.items():
if key in inputs:
make_dict[key] = tf.placeholder(tf.float32, name=key)
else:
make_dict[key] = None
# Breadth-First search of graph starting from inputs
while len(queue) != 0:
cur = graph_dict[queue[0]]
for outg in cur["outgoing"]:
if make_dict[outg[0]]: # If discovered node, do add/multiply operation
make_dict[outg[0]] = tf.add(make_dict[outg[0]], tf.multiply(outg[1], make_dict[queue[0]]))
else: # If undiscovered node, input is just coming in multiplied and add outgoing to queue
make_dict[outg[0]] = tf.multiply(make_dict[queue[0]], outg[1])
for outgo in graph_dict[outg[0]]["outgoing"]:
queue.append(outgo[0])
queue.pop(0)
# Returns one data graph for each output
return [make_dict[x] for x in outputs]
然后我将能够多次运行输出，因为它们只是带有占位符的图形，我将为其提供feed_dict。
显然，这不是TensorFlow v2.0中的预期方式，因为它们似乎强烈不鼓励在此新版本中使用占位符。
关键是我只需要对图形进行一次预处理，因为它返回的数据图与graph_dict定义无关。
答案 0 :(得分：3)
下面是可以与TF 2.0一起使用的示例代码。
它依靠compatibility API
可以通过tensorflow.compat.v1访问，并且需要disable v2 behaviors。
我不知道它的行为是否符合您的预期。
如果没有，请向我们提供更多有关您要实现的目标的解释。
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
@tf.function
def construct_graph(graph_dict, inputs, outputs):
queue = inputs[:]
make_dict = {}
for key, val in graph_dict.items():
if key in inputs:
make_dict[key] = tf.placeholder(tf.float32, name=key)
else:
make_dict[key] = None
# Breadth-First search of graph starting from inputs
while len(queue) != 0:
cur = graph_dict[queue[0]]
for outg in cur["outgoing"]:
if make_dict[outg[0]]: # If discovered node, do add/multiply operation
make_dict[outg[0]] = tf.add(make_dict[outg[0]], tf.multiply(outg[1], make_dict[queue[0]]))
else: # If undiscovered node, input is just coming in multiplied and add outgoing to queue
make_dict[outg[0]] = tf.multiply(make_dict[queue[0]], outg[1])
for outgo in graph_dict[outg[0]]["outgoing"]:
queue.append(outgo[0])
queue.pop(0)
# Returns one data graph for each output
return [make_dict[x] for x in outputs]
def main():
graph_def = {
"B": {
"incoming": [],
"outgoing": [("A", 1.0)]
},
"C": {
"incoming": [],
"outgoing": [("A", 1.0)]
},
"A": {
"incoming": [("B", 2.0), ("C", -1.0)],
"outgoing": [("D", 3.0)]
},
"D": {
"incoming": [("A", 2.0)],
"outgoing": []
}
}
outputs = construct_graph(graph_def, ["B", "C"], ["A"])
print(outputs)
if __name__ == "__main__":
main()
[<tf.Tensor 'PartitionedCall:0' shape=<unknown> dtype=float32>]
尽管以上代码段有效，但仍与TF 1.0绑定。 要将其迁移到TF 2.0，您需要重构一些代码。
建议不要返回张量列表，而张量列表在TF 1.0中是可调用的。
keras.layers.Model。
下面是一个有效的示例：
import tensorflow as tf
def construct_graph(graph_dict, inputs, outputs):
queue = inputs[:]
make_dict = {}
for key, val in graph_dict.items():
if key in inputs:
# Use keras.Input instead of placeholders
make_dict[key] = tf.keras.Input(name=key, shape=(), dtype=tf.dtypes.float32)
else:
make_dict[key] = None
# Breadth-First search of graph starting from inputs
while len(queue) != 0:
cur = graph_dict[queue[0]]
for outg in cur["outgoing"]:
if make_dict[outg[0]] is not None: # If discovered node, do add/multiply operation
make_dict[outg[0]] = tf.keras.layers.add([
make_dict[outg[0]],
tf.keras.layers.multiply(
[[outg[1]], make_dict[queue[0]]],
)],
)
else: # If undiscovered node, input is just coming in multiplied and add outgoing to queue
make_dict[outg[0]] = tf.keras.layers.multiply(
[make_dict[queue[0]], [outg[1]]]
)
for outgo in graph_dict[outg[0]]["outgoing"]:
queue.append(outgo[0])
queue.pop(0)
# Returns one data graph for each output
model_inputs = [make_dict[key] for key in inputs]
model_outputs = [make_dict[key] for key in outputs]
return [tf.keras.Model(inputs=model_inputs, outputs=o) for o in model_outputs]
def main():
graph_def = {
"B": {
"incoming": [],
"outgoing": [("A", 1.0)]
},
"C": {
"incoming": [],
"outgoing": [("A", 1.0)]
},
"A": {
"incoming": [("B", 2.0), ("C", -1.0)],
"outgoing": [("D", 3.0)]
},
"D": {
"incoming": [("A", 2.0)],
"outgoing": []
}
}
outputs = construct_graph(graph_def, ["B", "C"], ["A"])
print("Builded models:", outputs)
for o in outputs:
o.summary(120)
print("Output:", o((1.0, 1.0)))
if __name__ == "__main__":
main()
这里要注意什么？
placeholder更改为keras.Input，需要设置输入的形状。keras.layers.[add|multiply]进行计算。
这可能不是必需的，而是坚持使用一个界面。
但是，它需要将因子包装在列表中（以处理批处理）keras.Model返回这是代码的输出。
Builded models: [<tensorflow.python.keras.engine.training.Model object at 0x7fa0b49f0f50>]
Model: "model"
________________________________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
========================================================================================================================
B (InputLayer) [(None,)] 0
________________________________________________________________________________________________________________________
C (InputLayer) [(None,)] 0
________________________________________________________________________________________________________________________
tf_op_layer_mul (TensorFlowOpLayer) [(None,)] 0 B[0][0]
________________________________________________________________________________________________________________________
tf_op_layer_mul_1 (TensorFlowOpLayer) [(None,)] 0 C[0][0]
________________________________________________________________________________________________________________________
add (Add) (None,) 0 tf_op_layer_mul[0][0]
tf_op_layer_mul_1[0][0]
========================================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
________________________________________________________________________________________________________________________
Output: tf.Tensor([2.], shape=(1,), dtype=float32)