让您的代码与 TF 2.0 一起工作
以下是可用于 TF 2.0 的示例代码。
它依赖于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，请向我们提供更多关于您尝试实现的目标的说明。
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>]
将您的代码迁移到 TF 2.0
虽然上面的 sn-p 是有效的，但它仍然与 TF 1.0 相关联。
要将其迁移到 TF 2.0，您必须重构一些代码。
我建议您返回一个张量列表，而不是返回一个张量列表，这些张量是 TF 1.0 的可调用对象。
keras.layers.Model.
下面是一个工作示例：
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？
- 从
placeholder 更改为keras.Input，需要设置输入的形状。
- 使用
keras.layers.[add|multiply] 进行计算。
这可能不是必需的，但请坚持使用一个界面。
但是，它需要将因子包装在列表中（以处理批处理）
- 构建
keras.Model 以返回
- 使用值元组（不再是字典）调用您的模型
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)