基于CelebA数据集的GAN模型-2

# example of a gan for generating faces
from numpy import load
from numpy import zeros
from numpy import ones
from numpy.random import randn
from numpy.random import randint
from keras.optimizers import Adam
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Reshape
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import Conv2DTranspose
from keras.layers import LeakyReLU
from keras.layers import Dropout
from matplotlib import pyplot

# define the standalone discriminator model
def define_discriminator(in_shape=(80,80,3)):
  model = Sequential()
  # normal
  model.add(Conv2D(128, (5,5), padding='same', input_shape=in_shape))
  model.add(LeakyReLU(alpha=0.2))
  # downsample to 40x40
  model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # downsample to 20x30
  model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # downsample to 10x10
  model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # downsample to 5x5
  model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # classifier
  model.add(Flatten())
  model.add(Dropout(0.4))
  model.add(Dense(1, activation='sigmoid'))
  # compile model
  opt = Adam(lr=0.0002, beta_1=0.5)
  model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
  return model

# define the standalone generator model
def define_generator(latent_dim):
  model = Sequential()
  # foundation for 5x5 feature maps
  n_nodes = 128 * 5 * 5
  model.add(Dense(n_nodes, input_dim=latent_dim))
  model.add(LeakyReLU(alpha=0.2))
  model.add(Reshape((5, 5, 128)))
  # upsample to 10x10
  model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # upsample to 20x20
  model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # upsample to 40x40
  model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # upsample to 80x80
  model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
  model.add(LeakyReLU(alpha=0.2))
  # output layer 80x80x3
  model.add(Conv2D(3, (5,5), activation='tanh', padding='same'))
  return model

# define the combined generator and discriminator model, for updating the generator
def define_gan(g_model, d_model):
  # make weights in the discriminator not trainable
  d_model.trainable = False
  # connect them
  model = Sequential()
  # add generator
  model.add(g_model)
  # add the discriminator
  model.add(d_model)
  # compile model
  opt = Adam(lr=0.0002, beta_1=0.5)
  model.compile(loss='binary_crossentropy', optimizer=opt)
  return model

# load and prepare training images
def load_real_samples():
  # load the face dataset
  data = load('img_align_celeba.npz')
  X = data['arr_0']
  # convert from unsigned ints to floats
  X = X.astype('float32')
  # scale from [0,255] to [-1,1]
  X = (X - 127.5) / 127.5
  return X

# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
  # generate points in the latent space
  x_input = randn(latent_dim * n_samples)
  # reshape into a batch of inputs for the network
  x_input = x_input.reshape(n_samples, latent_dim)
  return x_input

# use the generator to generate n fake examples, with class labels
def generate_fake_samples(g_model, latent_dim, n_samples):
  # generate points in latent space
  x_input = generate_latent_points(latent_dim, n_samples)
  # predict outputs
  X = g_model.predict(x_input)
  # create 'fake' class labels (0)
  y = zeros((n_samples, 1))
  return X, y

# create and save a plot of generated images
def save_plot(examples, epoch, n=10):
  # scale from [-1,1] to [0,1]
  examples = (examples + 1) / 2.0
  # 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])
  # save plot to file
  filename = 'generated_plot_e%03d.png' % (epoch+1)
  pyplot.savefig(filename)
  pyplot.close()

# evaluate the discriminator, plot generated images, save generator model
def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=100):
  # prepare real samples
  X_real, y_real = generate_real_samples(dataset, n_samples)
  # evaluate discriminator on real examples
  _, acc_real = d_model.evaluate(X_real, y_real, verbose=0)
  # prepare fake examples
  x_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_samples)
  # evaluate discriminator on fake examples
  _, acc_fake = d_model.evaluate(x_fake, y_fake, verbose=0)
  # summarize discriminator performance
  print('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real*100, acc_fake*100))
  # save plot
  save_plot(x_fake, epoch)
  # save the generator model tile file
  filename = 'generator_model_%03d.h5' % (epoch+1)
  g_model.save(filename)

# train the generator and discriminator
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=100, n_batch=128):
  bat_per_epo = int(dataset.shape[0] / n_batch)
  half_batch = int(n_batch / 2)
  # manually enumerate epochs
  for i in range(n_epochs):
    # enumerate batches over the training set
    for j in range(bat_per_epo):
      # get randomly selected 'real' samples
      X_real, y_real = generate_real_samples(dataset, half_batch)
      # update discriminator model weights
      d_loss1, _ = d_model.train_on_batch(X_real, y_real)
      # generate 'fake' examples
      X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
      # update discriminator model weights
      d_loss2, _ = d_model.train_on_batch(X_fake, y_fake)
      # prepare points in latent space as input for the generator
      X_gan = generate_latent_points(latent_dim, n_batch)
      # create inverted labels for the fake samples
      y_gan = ones((n_batch, 1))
      # update the generator via the discriminator's error
      g_loss = gan_model.train_on_batch(X_gan, y_gan)
      # summarize loss on this batch
      print('>%d, %d/%d, d1=%.3f, d2=%.3f g=%.3f' %
        (i+1, j+1, bat_per_epo, d_loss1, d_loss2, g_loss))
    # evaluate the model performance, sometimes
    if (i+1) % 10 == 0:
      summarize_performance(i, g_model, d_model, dataset, latent_dim)

# size of the latent space
latent_dim = 100
# create the discriminator
d_model = define_discriminator()
# create the generator
g_model = define_generator(latent_dim)
# create the gan
gan_model = define_gan(g_model, d_model)
# load image data
dataset = load_real_samples()
# train model
train(g_model, d_model, gan_model, dataset, latent_dim)