我试图改变VAE https://blog.keras.io/building-autoencoders-in-keras.html
的Keras示例我修改了代码，使用嘈杂的mnist图像作为自动编码器的输入，并将原始的无噪声mnist图像作为输出。
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Layer
from keras.models import Model
from keras import backend as K
from keras import metrics
from keras.datasets import mnist
batch_size = 100
original_dim = 784
latent_dim = 2
intermediate_dim = 256
epochs = 1
epsilon_std = 1.0
x = Input(shape=(original_dim,))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0.,
stddev=epsilon_std)
return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
# we instantiate these layers separately so as to reuse them later
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
# Custom loss layer
class CustomVariationalLayer(Layer):
def __init__(self, **kwargs):
self.is_placeholder = True
super(CustomVariationalLayer, self).__init__(**kwargs)
def vae_loss(self, x, x_decoded_mean):
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(xent_loss + kl_loss)
def call(self, inputs):
x = inputs[0]
x_decoded_mean = inputs[1]
loss = self.vae_loss(x, x_decoded_mean)
self.add_loss(loss, inputs=inputs)
# We won't actually use the output.
return x
y = CustomVariationalLayer()([x, x_decoded_mean])
vae = Model(x, y)
vae.compile(optimizer='rmsprop', loss=None)
# train the VAE on MNIST digits
(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.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
vae.fit(x_train_noisy, x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=( x_test_noisy,x_test))
但是我收到以下错误消息：
File "ask_vae.py", line 86, in <module>
validation_data=( x_test_noisy,x_test))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1574, in fit
batch_size=batch_size)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1411, in _standardize_user_data
exception_prefix='target')
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 58, in _standardize_input_data
'expected no data, but got:', data)
ValueError: ('Error when checking model target: expected no data, but got:', array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
...,
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32))
似乎该模型无法接收输出;当我将输出更改为None时，它会起作用，如下所示：
vae.fit(x_train_noisy, None,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=( x_test_noisy,None))
这是因为自定义丢失层的定义方式吗？我该怎么办？
谢谢：）
答案 0 :(得分：1)
我用不同的方法来定义VAE损失，如下所示：
https://github.com/keras-team/keras/blob/keras-2/examples/variational_autoencoder.py
我将其更改为允许对数据进行去噪。 它现在有效，但我必须使用超参数来使它能够正确地重建原始图像。
import numpy as np
import time
import sys
import os
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import metrics
from keras.datasets import mnist
from keras.callbacks import ModelCheckpoint
filepath_for_w='denoise_by_VAE_weights_1.h5'
###########
##########
experiment_dir= 'exp_'+str(int(time.time()))
os.mkdir(experiment_dir)
this_script=sys.argv[0]
from shutil import copyfile
copyfile(this_script, experiment_dir+'/'+this_script)
##########
###########
batch_size = 100
original_dim = 784
latent_dim = 2
intermediate_dim = 256
epochs = 10
epsilon_std = 1.0
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_var = Dense(latent_dim)(h)
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.,
stddev=epsilon_std)
return z_mean + K.exp(z_log_var / 2) * epsilon
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
# we instantiate these layers separately so as to reuse them later
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
def vae_loss(x, x_decoded_mean):
xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return xent_loss + kl_loss
vae = Model(x, x_decoded_mean)
vae.compile(optimizer='rmsprop', loss=vae_loss)
# train the VAE on MNIST digits
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#after loading the data, change to the new experiment dir
os.chdir(experiment_dir) #
##########################
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
noise_factor = 0.5
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
for i in range (10):
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
checkpointer=ModelCheckpoint(filepath_for_w, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1)
vae.fit(x_train_noisy, x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test_noisy, x_test),
callbacks=[checkpointer])
vae.load_weights(filepath_for_w)
#print (x_train.shape)
#print (x_test.shape)
decoded_imgs = vae.predict(x_test,batch_size=batch_size)
np.save('decoded'+str(i)+'.npy',decoded_imgs)
np.save('tested.npy',x_test_noisy)
#np.save ('true_catagories.npy',y_test)
np.save('original.npy',x_test)