系列文章目录

深度学习GAN（一）之简单介绍
深度学习GAN（二）之基于CIFAR10数据集的例子
深度学习GAN（三）之基于手写体Mnist数据集的例子
深度学习GAN（四）之PIX2PIX GAN的例子

GAN基于手写体Mnist数据集生成新图片

1. 代码运行结果
2. GAN基于mnist数据集的完整代码

1. 代码运行结果

下图是GAN生成的手写体数字，用了10个epoch

2. GAN基于mnist数据集的完整代码

代码结构很像我的第二篇博客，如果你没看过，请先看那篇博客。里面有详细的代码讲解。

import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
import matplotlib.pyplot as plt# define the standalone discriminator model
def define_discriminator(in_shape=(28,28,1)):model = keras.models.Sequential()# normalmodel.add(keras.layers.Conv2D(64, (3,3), padding='same', input_shape=in_shape))model.add(keras.layers.LeakyReLU(alpha=0.2))# downsamplemodel.add(keras.layers.Conv2D(128, (3,3), strides=(2,2), padding='same'))model.add(keras.layers.LeakyReLU(alpha=0.2))# downsamplemodel.add(keras.layers.Conv2D(128, (3,3), strides=(2,2), padding='same'))model.add(keras.layers.LeakyReLU(alpha=0.2))# downsamplemodel.add(keras.layers.Conv2D(256, (3,3), strides=(2,2), padding='valid'))model.add(keras.layers.LeakyReLU(alpha=0.2))# classifiermodel.add(keras.layers.Flatten())model.add(keras.layers.Dropout(0.4))model.add(keras.layers.Dense(1, activation='sigmoid'))# compile modelopt = keras.optimizers.Adam(lr=0.0002, beta_1=0.5)model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])model.summary()return model# load and prepare cifar10 training images
def load_real_samples():# load cifar10 dataset(trainX, _), (_, _) = tf.keras.datasets.mnist.load_data()# convert from unsigned ints to floats#X = trainX.astype('float32')X = trainX.reshape(trainX.shape[0], 28, 28, 1).astype('float32')# scale from [0,255] to [-1,1]X = (X - 127.5) / 127.5return X# select real samples
def generate_real_samples(dataset, n_samples):# choose random instancesix = np.random.randint(0, dataset.shape[0], n_samples)# retrieve selected imagesX = dataset[ix]# generate 'real' class labels (1)y = np.ones((n_samples, 1))return X, ydef generate_fake_samples1(n_samples):# generate uniform random numbers in [0,1]X = np.random.rand(28 * 28 * 1 * n_samples)# update to have the range [-1, 1]X = -1 + X * 2# reshape into a batch of color imagesX = X.reshape((n_samples, 28, 28, 1))# generate 'fake' class labels (0)y = np.zeros((n_samples, 1))return X, y# train the discriminator model
def train_discriminator(model, dataset, n_iter=20, n_batch=128):half_batch = int(n_batch / 2)# manually enumerate epochsfor i in range(n_iter):# get randomly selected 'real' samplesX_real, y_real = generate_real_samples(dataset, half_batch)# update discriminator on real samples_, real_acc = model.train_on_batch(X_real, y_real)# generate 'fake' examplesX_fake, y_fake = generate_fake_samples1(half_batch)# update discriminator on fake samples_, fake_acc = model.train_on_batch(X_fake, y_fake)# summarize performanceprint('>%d real=%.0f%% fake=%.0f%%' % (i+1, real_acc*100, fake_acc*100))def test_train_discriminator():# define the discriminator modelmodel = define_discriminator()# load image datadataset = load_real_samples()# fit the modeltrain_discriminator(model, dataset)# define the standalone generator model
def define_generator(latent_dim):model = keras.models.Sequential()# foundation for 4x4 imagen_nodes = 256 * 3 * 3model.add(keras.layers.Dense(n_nodes, input_dim=latent_dim))model.add(keras.layers.LeakyReLU(alpha=0.2))model.add(keras.layers.Reshape((3, 3, 256)))# upsample to 8x8model.add(keras.layers.Conv2DTranspose(128, (3,3), strides=(2,2), padding='valid'))model.add(keras.layers.LeakyReLU(alpha=0.2))# upsample to 16x16model.add(keras.layers.Conv2DTranspose(128, (3,3), strides=(2,2), padding='same'))model.add(keras.layers.LeakyReLU(alpha=0.2))# upsample to 32x32model.add(keras.layers.Conv2DTranspose(64, (3,3), strides=(2,2), padding='same'))model.add(keras.layers.LeakyReLU(alpha=0.2))# output layermodel.add(keras.layers.Conv2D(1, (3,3), activation='tanh', padding='same'))return model# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):# generate points in the latent spacex_input = np.random.randn(latent_dim * n_samples)# reshape into a batch of inputs for the networkx_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 spacex_input = generate_latent_points(latent_dim, n_samples)# predict outputsX = g_model.predict(x_input)# create 'fake' class labels (0)y = np.zeros((n_samples, 1))return X, ydef show_fake_sample():# size of the latent spacelatent_dim = 100# define the discriminator modelmodel = define_generator(latent_dim)# generate samplesn_samples = 49X, _ = generate_fake_samples(model, latent_dim, n_samples)# scale pixel values from [-1,1] to [0,1]X = (X + 1) / 2.0# plot the generated samplesfor i in range(n_samples):# define subplotplt.subplot(7, 7, 1 + i)# turn off axis labelsplt.axis('off')# plot single imageplt.imshow(X[i])# show the figureplt.show()# define the combined generator and discriminator model, for updating the generator
def define_gan(g_model, d_model):# make weights in the discriminator not trainabled_model.trainable = False# connect themmodel = tf.keras.models.Sequential()# add generatormodel.add(g_model)# add the discriminatormodel.add(d_model)# compile modelopt = tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5)model.compile(loss='binary_crossentropy', optimizer=opt)return modeldef show_gan_module():# size of the latent spacelatent_dim = 100# create the discriminatord_model = define_discriminator()# create the generatorg_model = define_generator(latent_dim)# create the gangan_model = define_gan(g_model, d_model)# summarize gan modelgan_model.summary()# train the composite model
def train_gan(gan_model, latent_dim, n_epochs=200, n_batch=128):# manually enumerate epochsfor i in range(n_epochs):# prepare points in latent space as input for the generatorx_gan = generate_latent_points(latent_dim, n_batch)# create inverted labels for the fake samplesy_gan = np.ones((n_batch, 1))# update the generator via the discriminator's errorgan_model.train_on_batch(x_gan, y_gan)# evaluate the discriminator, plot generated images, save generator model
def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=150):# prepare real samplesX_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 examplesx_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 performanceprint('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real * 100, acc_fake * 100))# save plot#save_plot(x_fake, epoch)# save the generator model tile filefilename = 'minst_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=200, n_batch=128):bat_per_epo = int(dataset.shape[0] / n_batch)half_batch = int(n_batch / 2)# manually enumerate epochsfor i in range(n_epochs):# enumerate batches over the training setfor j in range(bat_per_epo):# get randomly selected 'real' samplesX_real, y_real = generate_real_samples(dataset, half_batch)# update discriminator model weightsd_loss1, _ = d_model.train_on_batch(X_real, y_real)# generate 'fake' examplesX_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)# update discriminator model weightsd_loss2, _ = d_model.train_on_batch(X_fake, y_fake)# prepare points in latent space as input for the generatorX_gan = generate_latent_points(latent_dim, n_batch)# create inverted labels for the fake samplesy_gan = np.ones((n_batch, 1))# update the generator via the discriminator's errorg_loss = gan_model.train_on_batch(X_gan, y_gan)# summarize loss on this batchprint('>%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, sometimesif (i + 1) % 10 == 0:summarize_performance(i, g_model, d_model, dataset, latent_dim)def test_train_gan():# size of the latent spacelatent_dim = 100# create the discriminatord_model = define_discriminator()# create the generatorg_model = define_generator(latent_dim)# create the gangan_model = define_gan(g_model, d_model)# load image datadataset = load_real_samples()# train modeltrain(g_model, d_model, gan_model, dataset, latent_dim)# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):# generate points in the latent spacex_input = np.random.randn(latent_dim * n_samples)# reshape into a batch of inputs for the networkx_input = x_input.reshape(n_samples, latent_dim)return x_input# plot the generated images
def create_plot(examples, n):# plot imagesfor i in range(n * n):# define subplotplt.subplot(n, n, 1 + i)# turn off axisplt.axis('off')# plot raw pixel dataplt.imshow(examples[i, :, :], cmap='gray')plt.show()def show_imgs_for_final_generator_model():# load modelmodel = tf.keras.models.load_model('minst_generator_model_010.h5')# generate imageslatent_points = generate_latent_points(100, 100)# generate imagesX = model.predict(latent_points)# scale from [-1,1] to [0,1]X = (X + 1) / 2.0# plot the resultX = X.reshape(X.shape[0], 28,28)create_plot(X, 10)def show_single_imgs():model = tf.keras.models.load_model('minst_generator_model_010.h5')# all 0svector = np.asarray([[0.75 for _ in range(100)]])# generate imageX = model.predict(vector)# scale from [-1,1] to [0,1]X = (X + 1) / 2.0# plot the resultplt.imshow(X[0, :, :])plt.show()if __name__ == '__main__':#define_discriminator()#test_train_discriminator()# show_fake_sample()#show_gan_module()test_train_gan()#g_module = define_generator(100)#print(g_module.summary())show_imgs_for_final_generator_model()# define the size of the latent space

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[深度学习-实践]GAN基于手写体Mnist数据集生成新图片

GAN基于手写体Mnist数据集生成新图片

1. 代码运行结果

2. GAN基于mnist数据集的完整代码

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