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python - ValueError：应在动态构造图的输入列表上调用合并层

问题描述

我的要求与替换占位符 for tensorflow v2中的要求相同。我从@AlexisBRENON（不是兼容模式下的代码）获取了为 Tensorflow 2 重构的代码并逐字运行。我收到一个ValueError: A merge layer should be called on a list of inputs指向multiply此片段部分的错误：

make_dict[outg[0]] = tf.keras.layers.add([
    make_dict[outg[0]],
    tf.keras.layers.multiply(
        [[outg[1]], make_dict[queue[0]]],
    )],
)

那里可能出了什么问题？从那时起 API 是否发生了变化？

我尝试包裹[[outg[1]], make_dict[queue[0]]]，tf.keras.layers.concatenate因为它是我能找到的最接近合并层的东西。它导致了另一个错误ValueError: A Concatenate layer should be called on a list of at least 2 inputs，即使列表中看起来有两个项目。

我正在使用 TensorFlow 2.4.1

标签： pythontensorflowkeras

解决方案

为了使旧答案起作用，我们需要进行以下更改。 multiply在 tensorflow 版本中设计2.4.1为仅接收张量作为列表中的输入。因此，我们需要将计算图的恒定权重更改为有效张量。我不确定它在原始答案中是如何工作的，这是现在的工作示例。

唯一真正的改变outg[1]是K.constant([outg[1]])

import tensorflow as tf
from keras import backend as K

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
    print(make_dict)
    while len(queue) != 0:
        cur = graph_dict[queue[0]]
        for outg in cur["outgoing"]:
            #print(outg)
            if make_dict[outg[0]] is not None: # If discovered node, do add/multiply operation
                #print(type(make_dict[outg[0]]))
                print(outg[1])
                make_dict[outg[0]] = tf.keras.layers.add([
                    make_dict[outg[0]],
                    tf.keras.layers.multiply(
                        [ K.constant([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]], K.constant([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 make_graph():
    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)))


make_graph()

给出输出

Model: "model"
________________________________________________________________________________________________________________________
Layer (type)                           Output Shape               Param #       Connected to                            
========================================================================================================================
B (InputLayer)                         [(None,)]                  0                                                     
________________________________________________________________________________________________________________________
C (InputLayer)                         [(None,)]                  0                                                     
________________________________________________________________________________________________________________________
multiply_33 (Multiply)                 (None,)                    0             B[0][0]                                 
________________________________________________________________________________________________________________________
multiply_34 (Multiply)                 (None,)                    0             C[0][0]                                 
________________________________________________________________________________________________________________________
add (Add)                              (None,)                    0             multiply_33[0][0]                       
                                                                                multiply_34[0][0]                       
========================================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
________________________________________________________________________________________________________________________
Output: tf.Tensor([2.], shape=(1,), dtype=float32)

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