python - ValueError：不支持无值

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Sep 4 16:53:08 2019 @author: hec """ import keras from keras import backend as K from keras import optimizers from keras.models import Model from keras.regularizers import l2 from keras.layers import Input, Dense, Multiply,Dropout,Lambda,Reshape,LeakyReLU,Activation,BatchNormalization #Custom loss function of Attention network def custom_loss(layer): layer1 = K.squeeze(layer,axis=2) sum1 = K.sum(layer1,axis=-1) sum2 = K.reshape(sum1,(-1,1)) layer2 = Lambda(lambda x: x[0]/x[1])([layer1,sum2]) def loss(y_true,y_pred): return K.binary_crossentropy(y_true,y_pred)+(0.3* K.sum(layer2,axis=-1)) # Return a function return loss def model_attention(): model_in =Input(shape=(None,1024)) x = Dense(512, kernel_initializer='glorot_uniform', bias_initializer='zeros')(model_in) x =Activation('sigmoid')(x) x = Dropout(0.8)(x) x = Dense(1, kernel_initializer='glorot_uniform', bias_initializer='zeros')(x) att_weights = Activation('sigmoid')(x) att_mull = Multiply()([model_in,att_weights]) mean = Lambda(lambda x:K.mean(x,axis=1))(att_mull) y = Dense(512, kernel_initializer='glorot_uniform', bias_initializer='zeros')(mean) y = Activation('sigmoid')(y) y = Dropout(0.7)(y) y = Dense(1, activation='sigmoid', kernel_initializer='glorot_uniform', bias_initializer='zeros')(y) model = Model(model_in, y) adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=True) for i, layer in enumerate(model.layers): layer.name = 'layer_' + str(i) model.compile(optimizer=adam,loss = custom_loss(att_weights),metrics=['accuracy']) return model