Keras-WGAN Critic和Generator的准确率为0

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)

 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]