生成器模型在訓練期間不生成圖片

我正在用 MNIST 訓練 GAN，我想在訓練期間用噪聲輸入可視化生成器輸出。

這是代碼：

from numpy import expand_dims
import numpy as np
import time
import tensorflow as tf
from numpy import zeros
from numpy import ones
from numpy import vstack
from numpy.random import randn
from numpy.random import randint
from tensorflow.keras.datasets.mnist import load_data
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers, Sequential
import matplotlib.pyplot as plt
from IPython import display
import imageio # for creating gifs
import PIL

(trainX, _), (_, _) = load_data()
# add channels dimension
X = expand_dims(trainX, axis=-1)
# convert from unsigned ints to floats
X = X.astype('float32')
# scale from [0,255] to [0,1]
dataset = X / 255.0

def define_generator(latent_dim):
    model = Sequential()
    # foundation for 7x7 image
    n_nodes = 128 * 7 * 7
    model.add(layers.Dense(n_nodes, input_dim=latent_dim))
    model.add(layers.LeakyReLU())
    model.add(layers.Reshape((7, 7, 128)))
    # upsample to 14x14
    model.add(layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
    model.add(layers.LeakyReLU())
    # upsample to 28x28
    model.add(layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Conv2D(1, (7,7), activation='sigmoid', padding='same'))
    return model

def define_discriminator(in_shape=(28,28,1)):
    model = Sequential()
    model.add(layers.Conv2D(128, (5,5), strides=(2, 2), padding='same', input_shape=in_shape))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))
    model.add(layers.Conv2D(128, (5,5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))
    model.add(layers.Flatten())
    model.add(layers.Dense(1, activation='sigmoid'))
    # compile model
    opt = Adam(lr=0.0002)
    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
    return model

# size of the noise vector
latent_dim = 100
num_examples_to_generate = 16

g_model = define_generator(latent_dim)

d_model = define_discriminator()

# 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)
    model.compile(loss='binary_crossentropy', optimizer=opt)
    return model

gan_model = define_gan(g_model, d_model)

# select real samples
def generate_real_samples(dataset, n_samples):
    # choose random instances
    ix = randint(0, dataset.shape[0], n_samples)
    # retrieve selected images
    X = dataset[ix]
    # generate 'real' class labels (1)
    y = ones((n_samples, 1))
    return X, y

# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
    # generate noise vector for training
    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

def generate_and_save_images(epoch):
  # for generating the fake images after each epoch

  # generate points in the latent space
  noise = randn(latent_dim * num_examples_to_generate)
  # reshape into a batch of inputs for the network
  noise = noise.reshape(num_examples_to_generate, latent_dim)
 
  predictions = g_model(noise, training=False)

  fig = plt.figure(figsize=(4, 4))

  for i in range(predictions.shape[0]):
      plt.subplot(4, 4, i 1)
      plt.imshow(predictions[i, :, :, 0], cmap='gray')
      plt.axis('off')

  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
  plt.show()

def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=60, n_batch=256):
  bat_per_epo = int(dataset.shape[0] / n_batch)
  half_batch = int(n_batch / 2)

  for epoch in range(n_epochs):

    start = time.time()

    for batch in range(bat_per_epo):
      
      X_real, y_real = generate_real_samples(dataset, half_batch)

      X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)

      X = vstack((X_real, X_fake))

      y = vstack((y_real, y_fake))

      d_loss, _ = d_model.train_on_batch(X,y)

      X_gan = generate_latent_points(latent_dim, n_batch)

      y_gan = ones((n_batch, 1))

      g_loss = gan_model.train_on_batch(X_gan, y_gan)

    display.clear_output(wait=True)
    generate_and_save_images(epoch   1)

    print('Time for epoch {} is {} sec'.format(epoch   1, time.time()-start))

  display.clear_output(wait=True)
  generate_and_save_images(n_epochs)

train(g_model, d_model, gan_model, dataset, latent_dim)

我得到的輸出是：

train(...) 命令的輸出

沒有錯誤，或者只是我看不到帶有噪聲輸入的發生器的輸出。

應該顯示輸出的函式是 generate_and_save_images.

uj5u.com熱心網友回復：

當您使用cmap="gray"in時，plt.imshow()您必須取消縮放輸出或設定 vmin 和 vmax。從我看到你通過除以 255 進行縮放，所以你必須將你的資料乘以 255，或者，替代設定 vmin=0, vmax=1 Option1：

plt.imshow(predictions[i, :, :, 0]*255, cmap='gray')

選項2：

plt.imshow(predictions[i, :, :, 0], cmap='gray', vmin=0, vmax=1)

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