可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试):
问题:
The input are 3 independent channels of 1000 features. I'm trying to pass each channel through a independent NN path, then concatenate them into a flat layer. Then apply a FCN on the flatten layer for a binary classification. I'm trying to add multiple Dense layers together, like this:
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()
but I got this error:
/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: if a cycle is detected. 1311 """ -> 1312 node = layer._inbound_nodes[node_index] 1313 1314 # Prevent cycles.
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
回答1:
The problem is that splitting the input data using inputs[0,:,1] is not done as a keras layer.
You need to create a Lambda layer to be able to accomplish this.
The following code:
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()
correctly created the net that you want.
回答2:
Thanks to @CAta.RAy
I solved it in this way:
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()
