我有一个具有两个输出（解码，pred_w）自动编码器，一个输出是重建的输入图像和另一种是一个重构的二进制图像。我在最后层中使用S形的激活函数而输出是浮点数，我需要网络标号为0或1。我附我的代码在这里每一个象素。你可以请指导我，我应该做些什么来解决这个问题？谢谢。
from keras.layers import Input, Concatenate, GaussianNoise,Dropout
from keras.layers import Conv2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
from keras import backend as K
from keras import layers
import matplotlib.pyplot as plt
import tensorflow as tf
import keras as Kr
import numpy as np
import pylab as pl
import matplotlib.cm as cm
import keract
from tensorflow.python.keras.layers import Lambda;
#-----------------building w train---------------------------------------------
w_main = np.random.randint(2,size=(1,4,4,1))
w_main=w_main.astype(np.float32)
w_expand=np.zeros((1,28,28,1),dtype='float32')
w_expand[:,0:4,0:4]=w_main
w_expand.reshape(1,28,28,1)
w_expand=np.repeat(w_expand,49999,0)
#-----------------building w validation---------------------------------------------
w_valid = np.random.randint(2,size=(1,4,4,1))
w_valid=w_valid.astype(np.float32)
wv_expand=np.zeros((1,28,28,1),dtype='float32')
wv_expand[:,0:4,0:4]=w_valid
wv_expand.reshape(1,28,28,1)
wv_expand=np.repeat(wv_expand,9999,0)
#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4,1))
w_test=w_test.astype(np.float32)
wt_expand=np.zeros((1,28,28,1),dtype='float32')
wt_expand[:,0:4,0:4]=w_test
wt_expand.reshape(1,28,28,1)
#wt_expand=np.repeat(wt_expand,10000,0)
#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(16, (3, 3), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(32, (3, 3), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e')(conv2)
DrO1=Dropout(0.25)(conv3)
encoded = Conv2D(1, (3, 3), activation='relu', padding='same',name='reconstructed_I')(DrO1)
#-----------------------adding w---------------------------------------
#add_const = Kr.layers.Lambda(lambda x: x + Kr.backend.constant(w_expand))
#encoded_merged=Kr.layers.Add()([encoded,wtm])
add_const = Kr.layers.Lambda(lambda x: x + wtm)
encoded_merged = add_const(encoded)
encoder=Model(inputs=image, outputs= encoded_merged)
encoder.summary()
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
#encoded_merged = Input((28, 28, 2))
deconv1 = Conv2D(16, (3, 3), activation='relu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(32, (3, 3), activation='relu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(8, (3, 3), activation='relu',padding='same', name='convl3d')(deconv2)
DrO2=Dropout(0.25)(deconv3)
decoded = Conv2D(1, (3, 3), activation='relu', padding='same', name='decoder_output')(DrO2)
#decoder=Model(inputs=encoded_merged, outputs=decoded)
#decoder.summary()
model=Model(inputs=image,outputs=decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(16, (3,3), activation='relu', padding='same', name='conl1w')(decoded)
convw2 = Conv2D(32, (3, 3), activation='relu', padding='same', name='convl2w')(convw1)
convw3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='conl3w')(convw2)
DrO3=Dropout(0.25)(convw3)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W')(DrO3)
# reconsider activation (is W positive?)
# should be filter=1 to match W
w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparesion----------------------------------------
(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1))
#---------------------compile and train the model------------------------------
# is accuracy sensible metric for this model?
w_extraction.compile(optimizer='adadelta', loss={'decoder_output':'mse','reconstructed_W':'mse'}, metrics=['mae'])
w_extraction.fit([x_train,w_expand], [x_train,w_expand],
epochs=100,
batch_size=128,
validation_data=([x_validation,wv_expand], [x_validation,wv_expand]),
callbacks=[TensorBoard(log_dir='E:/tmp/AutewithW200', histogram_freq=0, write_graph=False)])
model.summary()
如果你需要这个模型里面，你可以从K.round()使用keras.backend。请注意，这不会是微，且将无法在训练中使用得非常好。
如果你只需要的结果，你可以简单的定义阈值（通常为0.5）：
binary_reslts = results > threshold
您可以通过添加这一轮的数据指标看结果。该标准指标可能是"accuracy"或"categorical_accuracy"。您可以定义自己的指标，如：
def diceMetric(yTrue, yPred):
yTrue = K.batch_flatten(yTrue)
yPred = K.batch_flatten(yPred)
#round
yPred = K.greater(yPred, 0.5)
yPred = K.cast(yPred, K.floatx())
intersection = yPred * yTrue
sum = yTrue + yPred
return (2*intersection + K.epsilon())/(sum + K.epsilon())
度量在compile补充说：
model.compile(optimizer=..., loss=..., metrics = [diceMetric, 'categorical_accuracy'])
指标不影响训练，他们只是反馈让你知道发生了什么。
为什么需要网络精确输出0或1？可以确定输入像素correponds为0类或1。所以训练模型期间试图逼近未知的概率分布可以解释你的网络的输出概率措施的可能性有多大。
当谈到预测，你可以使用阈值像0.5，或者你可以使用类似大津阈值。然后，你将获得一个二进制输出。不幸的是门槛将创造一定的差距或缩小一些预测形状的区域。
注：通常你想下来，上采样在自动编码，因为否则的模型，可以得知idendity功能是最优的。