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

Question

输入是3个具有1000个功能的独立通道。我试图通过独立的NN路径传递每个通道，然后将它们连接成一个平坦的层。然后在扁平层上应用FCN进行二进制分类。 我正在尝试将多个密集层添加在一起，就像这样：

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()

但是我得到了这个错误：

  

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

     

AttributeError：“ NoneType”对象没有属性“ _inbound_nodes”

Answer 1

问题是使用inputs[0,:,1]分割输入数据不会作为keras层完成。

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()

正确创建了所需的网络。

Answer 2

感谢@ CAta.RAy

我以这种方式解决了：

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()