VAE重建的图像非常模糊

如何解决VAE重建的图像非常模糊

我对机器学习非常陌生，并且已经从Keras VAE代码示例构建了VAE。我只更改了模型中的几层。我在kaggle猫狗数据集中训练了该模型，然后尝试重建一些图像。所有重建的图像看起来都像这些Reconstructed Images一样。这可能是什么原因？是因为模型不好，训练时间短还是在重建图像时出现错误？

编码器型号：

latent_dim = 2
encoder_inputs = keras.Input(shape=(328,328,3))
x = layers.Conv2D(32,3,strides=2,padding="same")(encoder_inputs)
x = layers.Activation("relu")(x)
x = layers.Batchnormalization()(x)
x = layers.Conv2D(64,padding="same")(x)
x = layers.Activation("relu")(x)
x = layers.Batchnormalization()(x)
x = layers.Conv2D(128,padding="same")(x)  #neu
x = layers.Activation("relu")(x)
x = layers.Batchnormalization()(x)
x = layers.Flatten()(x)
x = layers.Dense(16,activation="relu")(x)
z_mean = layers.Dense(latent_dim,name="z_mean")(x)
z_log_var = layers.Dense(latent_dim,name="z_log_var")(x)
z = Sampling()([z_mean,z_log_var])
encoder = keras.Model(encoder_inputs,[z_mean,z_log_var,z],name="encoder")
encoder.summary()

解码器模型：

x = layers.Dense(41 * 41 * 128,activation="relu")(latent_inputs)
x = layers.Reshape((41,41,128))(x)
x = layers.Conv2DTranspose(128,activation="relu",padding="same")(x)
x = layers.Batchnormalization()(x)
x = layers.Conv2DTranspose(64,padding="same")(x)
x = layers.Batchnormalization()(x)
x = layers.Conv2DTranspose(32,padding="same")(x)
x = layers.Batchnormalization()(x)
decoder_outputs = layers.Conv2DTranspose(3,activation="sigmoid",padding="same")(x)
decoder = keras.Model(latent_inputs,decoder_outputs,name="decoder")
decoder.summary()

培训：

train_data_dir ='/content/Petimages'
nb_train_samples = 200
nb_epoch = 50
batch_size = 32
img_width = 328
img_height = 328

def fixed_generator(generator):
    for batch in generator:
        yield (batch,batch)

train_datagen = ImageDataGenerator(
        rescale=1./255,)

train_generator = train_datagen.flow_from_directory(
        train_data_dir,target_size=(img_width,img_height),batch_size=batch_size,class_mode=None)

vae = VAE(encoder,decoder)
vae.compile(optimizer=keras.optimizers.Adam())
vae.fit(
        fixed_generator(train_generator),steps_per_epoch=nb_train_samples,epochs=nb_epoch,)

并重建图像：

import matplotlib.pyplot as plt

test2_datagen = ImageDataGenerator(rescale=1./255)

test2_generator = test2_datagen.flow_from_directory(
        train_data_dir,batch_size=10,class_mode=None)

sample_img = next(test2_generator)

z_points = vae.encoder.predict(sample_img)

reconst_images = vae.decoder.predict(z_points)

fig = plt.figure(figsize=(10,8))
fig.subplots_adjust(hspace=0.1,wspace=0.1)

n_to_show =2

for i in range(n_to_show):
    img = sample_img[i].squeeze()
    sub = fig.add_subplot(2,n_to_show,i+1)
    sub.axis('off')        
    sub.imshow(img)

for i in range(n_to_show):
    img = reconst_images[i].squeeze()
    sub = fig.add_subplot(2,i+n_to_show+1)
    sub.axis('off')
    sub.imshow(img)