AttributeError：“NoneType”对象在尝试添加多个 keras 密集层时没有属性“_inbound_nodes”答案

【问题标题】：AttributeError: 'NoneType' object has no attribute '_inbound_nodes' while trying to add multiple keras Dense layersAttributeError：“NoneType”对象在尝试添加多个 keras 密集层时没有属性“_inbound_nodes”
【发布时间】：2018-09-21 17:38:28
【问题描述】：

输入是 1000 个特征的 3 个独立通道。我试图通过一个独立的 NN 路径传递每个通道，然后将它们连接成一个平面层。然后在 flatten 层上应用 FCN 进行二进制分类。我正在尝试将多个 Dense 图层添加在一起，如下所示：

def tst_1():

inputs = Input((3, 1000, 1))

dense10 = Dense(224, activation='relu')(inputs[0,:,1])
dense11 = Dense(112, activation='relu')(dense10)
dense12 = Dense(56, activation='relu')(dense11)

dense20 = Dense(224, activation='relu')(inputs[1,:,1])
dense21 = Dense(112, activation='relu')(dense20)
dense22 = Dense(56, activation='relu')(dense21)

dense30 = Dense(224, activation='relu')(inputs[2,:,1])
dense31 = Dense(112, activation='relu')(dense30)
dense32 = Dense(56, activation='relu')(dense31)

flat = keras.layers.Add()([dense12, dense22, dense32])

dense1 = Dense(224, activation='relu')(flat)
drop1 = Dropout(0.5)(dense1)
dense2 = Dense(112, activation='relu')(drop1)
drop2 = Dropout(0.5)(dense2)
dense3 = Dense(32, activation='relu')(drop2)
densef = Dense(1, activation='sigmoid')(dense3)

model = Model(inputs = inputs, outputs = densef)

model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

return model
model = tst_1()

model.summary()

但我收到了这个错误：

/usr/local/lib/python2.7/dist-packages/keras/engine/network.pyc in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1310 ValueError：如果检测到循环。第1311章 -> 1312 节点 = layer._inbound_nodes[node_index] 1313 1314 # 防止循环。

AttributeError: 'NoneType' 对象没有属性 '_inbound_nodes'

【问题讨论】：

您应该添加更多信息：例如，您要完成什么，数据集的性质，预处理...... model.summaty 的输出也很有用
输入为1000个观察的3个独立通道。我试图通过一个独立的 NN 路径传递每个通道，然后将它们连接成一个平面层。然后在 flatten 层上应用 FCN 进行二分类。
那么...为什么要添加而不是连接？公寓的大小会很不一样。
我应用了连接但我得到了同样的错误。我猜 Add() 在这里做同样的事情，因为输入是一维张量。

标签： python tensorflow keras

【解决方案1】：

问题是使用inputs[0,:,1]分割输入数据不是作为keras层来完成的。

您需要创建一个Lambda 层才能完成此操作。

以下代码：

from keras import layers
from keras.layers import Input, Add, Dense,Dropout, Lambda, Concatenate
from keras.layers import Flatten
from keras.optimizers import Adam
from keras.models import Model
import keras.backend as K


def tst_1(): 

    num_channels = 3
    inputs = Input(shape=(num_channels, 1000, 1))

    branch_outputs = []
    for i in range(num_channels):
        # Slicing the ith channel:
        out = Lambda(lambda x: x[:, i, :, :], name = "Lambda_" + str(i))(inputs)

        # Setting up your per-channel layers (replace with actual sub-models):
        out = Dense(224, activation='relu', name = "Dense_224_" + str(i))(out)
        out = Dense(112, activation='relu', name = "Dense_112_" + str(i))(out)
        out = Dense(56, activation='relu', name = "Dense_56_" + str(i))(out)
        branch_outputs.append(out)

    # Concatenating together the per-channel results:
    out = Concatenate()(branch_outputs)


    dense1 = Dense(224, activation='relu')(out)
    drop1 = Dropout(0.5)(dense1)
    dense2 = Dense(112, activation='relu')(drop1)
    drop2 = Dropout(0.5)(dense2)
    dense3 = Dense(32, activation='relu')(drop2)
    densef = Dense(1, activation='sigmoid')(dense3)

    model = Model(inputs = inputs, outputs = densef)

    return model

Net = tst_1()
Net.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

Net.summary()

正确创建了您想要的网络。

【讨论】：

感谢您的回复。但是crop函数的输出是(3,1)，而应该是(1000,1)。
我从你的回答中得到了想法，并以这种方式解决了它：
@Sam_Ha 修改了代码以实现您需要的功能。

【解决方案2】：

感谢@CAta.RAy

我是这样解决的：

import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense,Dropout, Lambda
from keras.layers import Flatten
from keras.optimizers import Adam
from keras.models import Model
import keras.backend as K




def tst_1(): 
    inputs = Input((3, 1000))

    x1 = Lambda(lambda x:x[:,0])(inputs)
    dense10 = Dense(224, activation='relu')(x1)
    dense11 = Dense(112, activation='relu')(dense10)
    dense12 = Dense(56, activation='relu')(dense11)

    x2 = Lambda(lambda x:x[:,1])(inputs)
    dense20 = Dense(224, activation='relu')(x2)
    dense21 = Dense(112, activation='relu')(dense20)
    dense22 = Dense(56, activation='relu')(dense21)

    x3 = Lambda(lambda x:x[:,2])(inputs)
    dense30 = Dense(224, activation='relu')(x3)
    dense31 = Dense(112, activation='relu')(dense30)
    dense32 = Dense(56, activation='relu')(dense31)

    flat = Add()([dense12, dense22, dense32])

    dense1 = Dense(224, activation='relu')(flat)
    drop1 = Dropout(0.5)(dense1)
    dense2 = Dense(112, activation='relu')(drop1)
    drop2 = Dropout(0.5)(dense2)
    dense3 = Dense(32, activation='relu')(drop2)
    densef = Dense(1, activation='sigmoid')(dense3)

    model = Model(inputs = inputs, outputs = densef)

    return model

Net = tst_1()
Net.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

Net.summary()

【讨论】：