python-3.x - 为什么我的 GAN 在某个点之后没有产生更多好的图像？

Question

问题

我正在训练一个 gan 来生成人脸。在大约 500 个 epoch 内，它学会了生成如下图像：

好吧，现在这个形象还不错。我们可以在图像的中心看到一张脸。

然后我对它进行了 1000 多个 epoch 的训练，但它什么也没学到。它仍在生成与上图相同类型的图像。那是为什么？为什么我的 gan 没有学会制作更好的图像？

模型代码

这是鉴别器的代码：

    def define_discriminator(in_shape=(64, 64, 3)):
        Model = Sequential([
                Conv2D(32, (3, 3), padding='same', input_shape=in_shape),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),
                MaxPooling2D(pool_size=(2,2)),
                Dropout(0.2),

                Conv2D(64, (3,3), padding='same'),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),
                MaxPooling2D(pool_size=(2,2)),
                Dropout(0.3),

                Conv2D(128, (3,3), padding='same'),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),
                MaxPooling2D(pool_size=(2,2)),
                Dropout(0.3),

                Conv2D(256, (3,3), padding='same'),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),
                MaxPooling2D(pool_size=(2,2)),
                Dropout(0.4),

                Flatten(),

                Dense(1, activation='sigmoid')
])
        opt = Adam(lr=0.00002)
        Model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])

        return Model

下面是生成器和 GAN 的代码：

def define_generator(in_shape=100):
    Model = Sequential([
                Dense(256*8*8, input_dim=in_shape),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),
                Reshape((8, 8, 256)),

                Conv2DTranspose(256, (3,3), strides=(2,2), padding='same'),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),

                Conv2DTranspose(64, (3,3), strides=(2,2), padding='same'),
                BatchNormalization(),
                LeakyReLU(alpha=0.2),

                Conv2DTranspose(3, (4, 4), strides=(2,2), padding='same', activation='sigmoid')
    ])
    return Model

def define_gan(d_model, g_model):
    d_model.trainable = False
    model = Sequential([
                g_model,
                d_model
    ])
    opt = Adam(lr=0.0002, beta_1=0.5)
    model.compile(loss='binary_crossentropy', optimizer=opt)
    return model

整个可重现的代码

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, BatchNormalization
from tensorflow.keras.layers import Dropout, Flatten, Dense, Conv2DTranspose
from tensorflow.keras.layers import MaxPooling2D, Activation, Reshape, LeakyReLU
from tensorflow.keras.datasets import mnist
from tensorflow.keras.optimizers import Adam
from numpy import ones
from numpy import zeros
from numpy.random import rand
from numpy.random import randint
from numpy.random import randn
from numpy import vstack
from numpy import array
import os
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from matplotlib import pyplot


def load_data(filepath):
    image_array = []
    n = 0
    for fold in os.listdir(filepath):
      if fold != 'wiki.mat':
        if n > 1:
            break
        for img in os.listdir(os.path.join(filepath, fold)):
            image = load_img(filepath + fold +  '/'+ img, target_size=(64, 64))
            img_array = img_to_array(image)
            img_array = img_array.astype('float32')
            img_array = img_array / 255.0
            image_array.append(img_array)
        n += 1
    return array(image_array)
def generate_latent_points(n_samples, latent_dim=100):
    latent_points = randn(n_samples * latent_dim)
    latent_points = latent_points.reshape(n_samples, latent_dim)
    return latent_points

def generate_real_samples(n_samples, dataset):
    ix = randint(0, dataset.shape[0], n_samples)
    x = dataset[ix]
    y = ones((n_samples, 1))
    return x, y

def generate_fake_samples(g_model, n_samples):
    latent_points = generate_latent_points(n_samples)
    x = g_model.predict(latent_points)
    y = zeros((n_samples, 1))
    return x, y

def save_plot(examples, epoch, n=10):
    # plot images
    for i in range(n * n):
        # define subplot
        pyplot.subplot(n, n, 1 + i)
        # turn off axis
        pyplot.axis('off')
        # plot raw pixel data
    pyplot.imshow(examples[i, :, :, 0])
    # save plot to file
    filename = 'generated_plot_e%03d.png' % (epoch+1)
    pyplot.savefig(filename)
    pyplot.close()

def summarize_performance(d_model, g_model, gan_model, dataset, epoch, n_samples=100):
    real_x, real_y = generate_real_samples(n_samples, dataset)
    _, d_real_acc = d_model.evaluate(real_x, real_y)
    fake_x, fake_y = generate_fake_samples(g_model, n_samples)
    _, d_fake_acc = d_model.evaluate(fake_x, fake_y)

    latent_points, y = generate_latent_points(n_samples), ones((n_samples, 1))
    gan_loss = gan_model.evaluate(latent_points, y)

    print('Epoch %d, acc_real=%.3d, acc_fake=%.3f, gan_loss=%.3f' % (epoch, d_real_acc, d_fake_acc, gan_loss))

save_plot(fake_x, epoch)
filename = 'Genarator_Model % d' % (epoch + 1)
g_model.save(filename)

def train(d_model, g_model, gan_model, dataset, epochs=200):
    batch_size = 64
    half_batch = int(batch_size / 2)
    batch_per_epoch = int(dataset.shape[0] / batch_size)
    for epoch in range(epochs):
        for i in range(batch_per_epoch):
            real_x, real_y = generate_real_samples(half_batch, dataset)
            _, d_real_acc = d_model.train_on_batch(real_x, real_y)
            fake_x, fake_y = generate_fake_samples(g_model, half_batch)
            _, d_fake_acc = d_model.train_on_batch(fake_x, fake_y)

            latent_points, y = generate_latent_points(batch_size), ones((batch_size, 1))
            gan_loss = gan_model.train_on_batch(latent_points, y)

            print('Epoch %d, acc_real=%.3d, acc_fake=%.3f, gan_loss=%.3f' % (epoch, d_real_acc, d_fake_acc, gan_loss))
        if (epoch % 2) == 0:
            summarize_performance(d_model, g_model, gan_model, dataset, epoch)

dataset = load_data(filepath) # filepath is not defined since every person will have seperate filepath

discriminator_model = define_discriminator()
generator_model = define_generator()
gan_model = define_gan(discriminator_model, generator_model)

train(discriminator_model, generator_model, gan_model, dataset)

数据集

如果你想要这里是数据集。

标签： python-3.xmachine-learningdeep-learningtf.kerasgenerative-adversarial-network