Keras-WGAN 评论家和生成器的准确度停留在 0

Question

我正在尝试在 Keras 中实现 WGAN。 我正在使用 David Foster 的 Generative Deep Learning Book 和此代码作为参考。 我写下了这个简单的代码。 但是，每当我开始训练 model 时，准确度始终为 0，Critic 和 Discriminator 的损失约为 0。

无论他们训练多少个 epoch，他们都被困在这些数字上。 我尝试了各种网络配置和不同的超参数，但结果似乎没有改变。 谷歌也没有多大帮助。 我无法确定这种行为的根源。

这是我写的代码。

from os.path import expanduser
import os
import struct as st

import numpy as np
import matplotlib.pyplot as plt

from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import RMSprop
import keras.backend as K

def wasserstein_loss(y_true, y_pred):
    return K.mean(y_true * y_pred)

class WGAN:

    def __init__(self):

        # Data Params
        self.genInput=100
        self.imChannels=1
        self.imShape = (28,28,1)

        # Build Models
        self.onBuildDiscriminator()
        self.onBuildGenerator()
        self.onBuildGAN()

        pass

    def onBuildGAN(self):

        if self.mGenerator is None or self.mDiscriminator is None: raise Exception('Generator Or Descriminator Uninitialized.')

        self.mDiscriminator.trainable=False

        self.mGAN=Sequential()
        self.mGAN.add(self.mGenerator)
        self.mGAN.add(self.mDiscriminator)

        ganOptimizer=RMSprop(lr=0.00005)
        self.mGAN.compile(loss=wasserstein_loss, optimizer=ganOptimizer, metrics=['accuracy'])

        print('GAN Model')
        self.mGAN.summary()
        pass

    def onBuildGenerator(self):

        self.mGenerator=Sequential()

        self.mGenerator.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.genInput))
        self.mGenerator.add(Reshape((7, 7, 128)))
        self.mGenerator.add(UpSampling2D())
        self.mGenerator.add(Conv2D(128, kernel_size=4, padding="same"))
        self.mGenerator.add(BatchNormalization(momentum=0.8))
        self.mGenerator.add(Activation("relu"))
        self.mGenerator.add(UpSampling2D())
        self.mGenerator.add(Conv2D(64, kernel_size=4, padding="same"))
        self.mGenerator.add(BatchNormalization(momentum=0.8))
        self.mGenerator.add(Activation("relu"))
        self.mGenerator.add(Conv2D(self.imChannels, kernel_size=4, padding="same"))
        self.mGenerator.add(Activation("tanh"))

        print('Generator Model')
        self.mGenerator.summary()
        pass

    def onBuildDiscriminator(self):

        self.mDiscriminator = Sequential()

        self.mDiscriminator.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.imShape, padding="same"))
        self.mDiscriminator.add(LeakyReLU(alpha=0.2))
        self.mDiscriminator.add(Dropout(0.25))
        self.mDiscriminator.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
        self.mDiscriminator.add(ZeroPadding2D(padding=((0,1),(0,1))))
        self.mDiscriminator.add(BatchNormalization(momentum=0.8))
        self.mDiscriminator.add(LeakyReLU(alpha=0.2))
        self.mDiscriminator.add(Dropout(0.25))
        self.mDiscriminator.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
        self.mDiscriminator.add(BatchNormalization(momentum=0.8))
        self.mDiscriminator.add(LeakyReLU(alpha=0.2))
        self.mDiscriminator.add(Dropout(0.25))
        self.mDiscriminator.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
        self.mDiscriminator.add(BatchNormalization(momentum=0.8))
        self.mDiscriminator.add(LeakyReLU(alpha=0.2))
        self.mDiscriminator.add(Dropout(0.25))
        self.mDiscriminator.add(Flatten())
        self.mDiscriminator.add(Dense(1))

        disOptimizer=RMSprop(lr=0.00005)
        self.mDiscriminator.compile(loss=wasserstein_loss, optimizer=disOptimizer, metrics=['accuracy'])

        print('Discriminator Model')
        self.mDiscriminator.summary()

        pass

    def fit(self, trainData, nEpochs=1000, batchSize=64):

        lblForReal = -np.ones((batchSize, 1))
        lblForGene = np.ones((batchSize, 1))

        for ep in range(1, nEpochs+1):

            for __ in range(5):

                # Get Valid Images
                validImages = trainData[ np.random.randint(0, trainData.shape[0], batchSize) ]

                # Get Generated Images
                noiseForGene=np.random.normal(0, 1, size=(batchSize, self.genInput))
                geneImages=self.mGenerator.predict(noiseForGene)

                # Train Critic On Valid And Generated Images With Labels -1 And 1 Respectively
                disValidLoss=self.mDiscriminator.train_on_batch(validImages, lblForReal)
                disGeneLoss=self.mDiscriminator.train_on_batch(geneImages, lblForGene)

                # Perform Critic Weight Clipping
                for l in self.mDiscriminator.layers:
                    weights = l.get_weights()
                    weights = [np.clip(w, -0.01, 0.01) for w in weights]
                    l.set_weights(weights)

            # Train Generator Using Combined Model
            geneLoss=self.mGAN.train_on_batch(noiseForGene, lblForReal)

            print(' Epoch', ep, 'Critic Valid Loss,Acc', disValidLoss, 'Critic Generated Loss,Acc', disGeneLoss, 'Generator Loss,Acc', geneLoss)
        pass

    pass

if __name__ == '__main__':
    (trainData, __), (__, __) = mnist.load_data()
    trainData = (trainData.astype(np.float32)/127.5) - 1
    trainData = np.expand_dims(trainData, axis=3)

    WGan = WGAN()
    WGan.fit(trainData)

对于我尝试的所有配置，我得到的 output 与以下非常相似。

 Epoch 1 Critic Valid Loss,Acc [-0.00016362152, 0.0] Critic Generated Loss,Acc [0.0003417502, 0.0] Generator Loss,Acc [-0.00016735379, 0.0]
 Epoch 2 Critic Valid Loss,Acc [-0.0001719332, 0.0] Critic Generated Loss,Acc [0.0003365979, 0.0] Generator Loss,Acc [-0.00017250411, 0.0]
 Epoch 3 Critic Valid Loss,Acc [-0.00017473527, 0.0] Critic Generated Loss,Acc [0.00032945914, 0.0] Generator Loss,Acc [-0.00017612436, 0.0]
 Epoch 4 Critic Valid Loss,Acc [-0.00017181305, 0.0] Critic Generated Loss,Acc [0.0003266656, 0.0] Generator Loss,Acc [-0.00016987178, 0.0]
 Epoch 5 Critic Valid Loss,Acc [-0.0001683443, 0.0] Critic Generated Loss,Acc [0.00032702673, 0.0] Generator Loss,Acc [-0.00016638976, 0.0]
 Epoch 6 Critic Valid Loss,Acc [-0.00017005506, 0.0] Critic Generated Loss,Acc [0.00032805002, 0.0] Generator Loss,Acc [-0.00017040147, 0.0]
 Epoch 7 Critic Valid Loss,Acc [-0.00017353195, 0.0] Critic Generated Loss,Acc [0.00033711304, 0.0] Generator Loss,Acc [-0.00017537423, 0.0]
 Epoch 8 Critic Valid Loss,Acc [-0.00017059325, 0.0] Critic Generated Loss,Acc [0.0003263024, 0.0] Generator Loss,Acc [-0.00016974319, 0.0]
 Epoch 9 Critic Valid Loss,Acc [-0.00017530039, 0.0] Critic Generated Loss,Acc [0.00032463064, 0.0] Generator Loss,Acc [-0.00017845634, 0.0]
 Epoch 10 Critic Valid Loss,Acc [-0.00017530067, 0.0] Critic Generated Loss,Acc [0.00033131015, 0.0] Generator Loss,Acc [-0.00017526663, 0.0]

Answer 1

我遇到了类似的问题。 WGAN 的问题在于权重裁剪方法确实削弱了模型的学习能力。 学习可以很快饱和。 权重在每个 epoch 之后通过反向传播进行更新，但随后它们被剪裁。 我建议您将削波值更极端地进行试验。 尝试 [-1,1] 和 [-0.0001, 0.0001]。 你肯定会看到变化。 饱和示例：

如您所见，损失值在前几百次迭代中达到 0.999975，然后在 100000 次迭代中完全没有变化。 我尝试使用不同的裁剪值进行试验，损失值不同但行为相同。 当我尝试 [-0.005, 0.005] 时，损失在 1 左右饱和，而 [-0.02, 0.02] 在 0.8 左右。

你的实现看起来是正确的，但有时在 GAN 中你能做的只有这么多。 所以我建议你尝试使用梯度惩罚的 WGAN。 它有一个很好的方法来强制 K-Lipschitz 连续性，通过将插值图像的 L2 范数固定为接近 1（查看论文）。 对于 WGAN-GP 中的评估，理想情况下，您应该看到评论家的损失值从某个较大的负数开始，然后收敛到 0。