使用条件变分自编码器进行回归 (CVAE)

问题描述

我正在尝试为回归问题实施条件 VAE，我的数据集由图像和每个图像的连续值组成。 我从这个用于分类问题的示例 CVAE on mnist dataset 开始，那么我必须进行哪些更改才能处理连续值？

我认为，如果我简单地将 img（我的数据）与单个回归值连接起来，并将其作为编码器和解码器的输入，我将不会正确对待这个问题，还是我错了？

我的最终目标是为模型提供回归值并生成图像。

import warnings
import numpy as np
from keras.layers import Input,Dense,Lambda
from keras.layers.merge import concatenate as concat
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
from keras.utils import to_categorical
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from scipy.misc import imsave
import matplotlib.pyplot as plt

warnings.filterwarnings('ignore')
%pylab inline

(X_train,Y_train),(X_test,Y_test) = mnist.load_data()

X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.

n_pixels = np.prod(X_train.shape[1:])
X_train = X_train.reshape((len(X_train),n_pixels))
X_test = X_test.reshape((len(X_test),n_pixels))

y_train = to_categorical(Y_train)
y_test = to_categorical(Y_test)

n_z = 2 # latent space size
encoder_dim1 = 512 # dim of encoder hidden layer
decoder_dim = 512 # dim of decoder hidden layer
decoder_out_dim = 784 # dim of decoder output layer
activ = 'relu'
optim = Adam(lr=0.001)


n_x = X_train.shape[1]
n_y = y_train.shape[1]


n_epoch = 50

X = Input(shape=(n_x,))
label = Input(shape=(n_y,))

inputs = concat([X,label])

encoder_h = Dense(encoder_dim1,activation=activ)(inputs)
mu = Dense(n_z,activation='linear')(encoder_h)
l_sigma = Dense(n_z,activation='linear')(encoder_h)

def sample_z(args):
    mu,l_sigma = args
    eps = K.random_normal(shape=(m,n_z),mean=0.,stddev=1.)
    return mu + K.exp(l_sigma / 2) * eps


# Sampling latent space
z = Lambda(sample_z,output_shape = (n_z,))([mu,l_sigma])


z = Lambda(sample_z,l_sigma])

# merge latent space with label
zc = concat([z,label])

decoder_hidden = Dense(decoder_dim,activation=activ)
decoder_out = Dense(decoder_out_dim,activation='sigmoid')
h_p = decoder_hidden(zc)
outputs = decoder_out(h_p)

def vae_loss(y_true,y_pred):
    recon = K.sum(K.binary_crossentropy(y_true,y_pred),axis=-1)
    kl = 0.5 * K.sum(K.exp(l_sigma) + K.square(mu) - 1. - l_sigma,axis=-1)
    return recon + kl

def KL_loss(y_true,y_pred):
    return(0.5 * K.sum(K.exp(l_sigma) + K.square(mu) - 1. - l_sigma,axis=1))

def recon_loss(y_true,y_pred):
    return K.sum(K.binary_crossentropy(y_true,axis=-1)

cvae = Model([X,label],outputs)
encoder = Model([X,mu)

d_in = Input(shape=(n_z+n_y,))
d_h = decoder_hidden(d_in)
d_out = decoder_out(d_h)
decoder = Model(d_in,d_out)

cvae.compile(optimizer=optim,loss=vae_loss,metrics = [KL_loss,recon_loss])


# compile and fit
cvae_hist = cvae.fit([X_train,y_train],X_train,verbose = 1,batch_size=m,epochs=n_epoch,validation_data = ([X_test,y_test],X_test),callbacks = [EarlyStopping(patience = 5)])

plt.imshow(X_train[0].reshape(28,28),cmap = plt.cm.gray),axis('off')
plt.show()

print(Y_train[0])

encoded_X0 = encoder.predict([X_train[0].reshape((1,784)),y_train[0].reshape((1,10))])
print(encoded_X0)

z_train = encoder.predict([X_train,y_train])
encodings= np.asarray(z_train)
encodings = encodings.reshape(X_train.shape[0],n_z)
plt.figure(figsize=(7,7))
plt.scatter(encodings[:,0],encodings[:,1],c=Y_train,cmap=plt.cm.jet)
plt.show()

def construct_numvec(digit,z = None):
    out = np.zeros((1,n_z + n_y))
    out[:,digit + n_z] = 1.
    if z is None:
        return(out)
    else:
        for i in range(len(z)):
            out[:,i] = z[i]
        return(out)
    
sample_3 = construct_numvec(3)
print(sample_3)

plt.figure(figsize=(3,3))
plt.imshow(decoder.predict(sample_3).reshape(28,axis('off')
plt.show()

dig = 3
sides = 8
max_z = 1.5

img_it = 0
for i in range(0,sides):
    z1 = (((i / (sides-1)) * max_z)*2) - max_z
    for j in range(0,sides):
        z2 = (((j / (sides-1)) * max_z)*2) - max_z
        z_ = [z1,z2]
        vec = construct_numvec(dig,z_)
        decoded = decoder.predict(vec)
        subplot(sides,sides,1 + img_it)
        img_it +=1
        plt.imshow(decoded.reshape(28,axis('off')
plt.subplots_adjust(left=0,bottom=0,right=1,top=1,wspace=0,hspace=.2)
plt.show()

dig = 2
sides = 8
max_z = 1.5

img_it = 0
for i in range(0,hspace=.2)
plt.show()

解决方法

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