介绍CGAN和ACGAN的原理,通过引入额外的Condition来控制生成的图片,并在DCGAN和WGAN的基础上进行实现
样本 可以包含一些属性,或者说条件,记作
例如MNIST中每张图片对应的数字可以是0至9
从一张图来了解CGAN(Conditional GAN)的思想
生成器 从随机噪音 和条件 生成假样本,判别器 接受真假样本和条件 ,判断样本是否为满足条件 的真实样本
总的目标函数如下
先用MNIST,在DCGAN的基础上稍作改动以实现CGAN
载入库
# -*- coding: utf-8 -*-
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt %matplotlib inline import os, imageio from tqdm import tqdm
载入数据,指定one_hot=True
one_hot=True
from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(MNIST_data, one_hot=True)
定义一些常量、网路输入、辅助函数,这里加上了y_label和y_noise
y_label
y_noise
batch_size = 100 z_dim = 100 WIDTH = 28 HEIGHT = 28 LABEL = 10
OUTPUT_DIR = samples if not os.path.exists(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)
X = tf.placeholder(dtype=tf.float32, shape=[None, HEIGHT, WIDTH, 1], name=X) y_label = tf.placeholder(dtype=tf.float32, shape=[None, HEIGHT, WIDTH, LABEL], name=y_label) noise = tf.placeholder(dtype=tf.float32, shape=[None, z_dim], name=noise) y_noise = tf.placeholder(dtype=tf.float32, shape=[None, LABEL], name=y_noise) is_training = tf.placeholder(dtype=tf.bool, name=is_training)
def lrelu(x, leak=0.2): return tf.maximum(x, leak * x)
def sigmoid_cross_entropy_with_logits(x, y): return tf.nn.sigmoid_cross_entropy_with_logits(logits=x, labels=y)
判别器部分
def discriminator(image, label, reuse=None, is_training=is_training): momentum = 0.9 with tf.variable_scope(discriminator, reuse=reuse): h0 = tf.concat([image, label], axis=3) h0 = lrelu(tf.layers.conv2d(h0, kernel_size=5, filters=64, strides=2, padding=same))
h1 = tf.layers.conv2d(h0, kernel_size=5, filters=128, strides=2, padding=same) h1 = lrelu(tf.contrib.layers.batch_norm(h1, is_training=is_training, decay=momentum))
h2 = tf.layers.conv2d(h1, kernel_size=5, filters=256, strides=2, padding=same) h2 = lrelu(tf.contrib.layers.batch_norm(h2, is_training=is_training, decay=momentum))
h3 = tf.layers.conv2d(h2, kernel_size=5, filters=512, strides=2, padding=same) h3 = lrelu(tf.contrib.layers.batch_norm(h3, is_training=is_training, decay=momentum))
h4 = tf.contrib.layers.flatten(h3) h4 = tf.layers.dense(h4, units=1) return tf.nn.sigmoid(h4), h4
生成器部分
def generator(z, label, is_training=is_training): momentum = 0.9 with tf.variable_scope(generator, reuse=None): d = 3 z = tf.concat([z, label], axis=1) h0 = tf.layers.dense(z, units=d * d * 512) h0 = tf.reshape(h0, shape=[-1, d, d, 512]) h0 = tf.nn.relu(tf.contrib.layers.batch_norm(h0, is_training=is_training, decay=momentum))
h1 = tf.layers.conv2d_transpose(h0, kernel_size=5, filters=256, strides=2, padding=same) h1 = tf.nn.relu(tf.contrib.layers.batch_norm(h1, is_training=is_training, decay=momentum))
h2 = tf.layers.conv2d_transpose(h1, kernel_size=5, filters=128, strides=2, padding=same) h2 = tf.nn.relu(tf.contrib.layers.batch_norm(h2, is_training=is_training, decay=momentum))
h3 = tf.layers.conv2d_transpose(h2, kernel_size=5, filters=64, strides=2, padding=same) h3 = tf.nn.relu(tf.contrib.layers.batch_norm(h3, is_training=is_training, decay=momentum))
h4 = tf.layers.conv2d_transpose(h3, kernel_size=5, filters=1, strides=1, padding=valid, activation=tf.nn.tanh, name=g) return h4
损失函数
g = generator(noise, y_noise) d_real, d_real_logits = discriminator(X, y_label) d_fake, d_fake_logits = discriminator(g, y_label, reuse=True)
vars_g = [var for var in tf.trainable_variables() if var.name.startswith(generator)] vars_d = [var for var in tf.trainable_variables() if var.name.startswith(discriminator)]
loss_d_real = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_real_logits, tf.ones_like(d_real))) loss_d_fake = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_fake_logits, tf.zeros_like(d_fake))) loss_g = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_fake_logits, tf.ones_like(d_fake))) loss_d = loss_d_real + loss_d_fake
优化函数
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): optimizer_d = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(loss_d, var_list=vars_d) optimizer_g = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(loss_g, var_list=vars_g)
拼接图片的函数
def montage(images): if isinstance(images, list): images = np.array(images) img_h = images.shape[1] img_w = images.shape[2] n_plots = int(np.ceil(np.sqrt(images.shape[0]))) m = np.ones((images.shape[1] * n_plots + n_plots + 1, images.shape[2] * n_plots + n_plots + 1)) * 0.5 for i in range(n_plots): for j in range(n_plots): this_filter = i * n_plots + j if this_filter < images.shape[0]: this_img = images[this_filter] m[1 + i + i * img_h:1 + i + (i + 1) * img_h, 1 + j + j * img_w:1 + j + (j + 1) * img_w] = this_img return m
训练模型,加入条件信息
sess = tf.Session() sess.run(tf.global_variables_initializer()) z_samples = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32) y_samples = np.zeros([batch_size, LABEL]) for i in range(LABEL): for j in range(LABEL): y_samples[i * LABEL + j, i] = 1 samples = [] loss = {d: [], g: []}
for i in tqdm(range(60000)): n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32) batch, label = mnist.train.next_batch(batch_size=batch_size) batch = np.reshape(batch, [batch_size, HEIGHT, WIDTH, 1]) batch = (batch - 0.5) * 2 yn = np.copy(label) yl = np.reshape(label, [batch_size, 1, 1, LABEL]) yl = yl * np.ones([batch_size, HEIGHT, WIDTH, LABEL])
d_ls, g_ls = sess.run([loss_d, loss_g], feed_dict={X: batch, noise: n, y_label: yl, y_noise: yn, is_training: True}) loss[d].append(d_ls) loss[g].append(g_ls)
sess.run(optimizer_d, feed_dict={X: batch, noise: n, y_label: yl, y_noise: yn, is_training: True}) sess.run(optimizer_g, feed_dict={X: batch, noise: n, y_label: yl, y_noise: yn, is_training: True}) sess.run(optimizer_g, feed_dict={X: batch, noise: n, y_label: yl, y_noise: yn, is_training: True})
if i % 1000 == 0: print(i, d_ls, g_ls) gen_imgs = sess.run(g, feed_dict={noise: z_samples, y_noise: y_samples, is_training: False}) gen_imgs = (gen_imgs + 1) / 2 imgs = [img[:, :, 0] for img in gen_imgs] gen_imgs = montage(imgs) plt.axis(off) plt.imshow(gen_imgs, cmap=gray) imageio.imsave(os.path.join(OUTPUT_DIR, sample_%d.jpg % i), gen_imgs) plt.show() samples.append(gen_imgs)
plt.plot(loss[d], label=Discriminator) plt.plot(loss[g], label=Generator) plt.legend(loc=upper right) plt.savefig(Loss.png) plt.show() imageio.mimsave(os.path.join(OUTPUT_DIR, samples.gif), samples, fps=5)
生成的手写数字图片如下,每一行对应的数字相同
保存模型,便于后续使用
saver = tf.train.Saver() saver.save(sess, ./mnist_cgan, global_step=60000)
在单机上使用模型生成手写数字图片
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt
batch_size = 100 z_dim = 100 LABEL = 10
sess = tf.Session() sess.run(tf.global_variables_initializer())
saver = tf.train.import_meta_graph(./mnist_cgan-60000.meta) saver.restore(sess, tf.train.latest_checkpoint(./))
graph = tf.get_default_graph() g = graph.get_tensor_by_name(generator/g/Tanh:0) noise = graph.get_tensor_by_name(noise:0) y_noise = graph.get_tensor_by_name(y_noise:0) is_training = graph.get_tensor_by_name(is_training:0)
n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32) y_samples = np.zeros([batch_size, LABEL]) for i in range(LABEL): for j in range(LABEL): y_samples[i * LABEL + j, i] = 1 gen_imgs = sess.run(g, feed_dict={noise: n, y_noise: y_samples, is_training: False}) gen_imgs = (gen_imgs + 1) / 2 imgs = [img[:, :, 0] for img in gen_imgs] gen_imgs = montage(imgs) plt.axis(off) plt.imshow(gen_imgs, cmap=gray) plt.show()
了解CGAN的原理和实现之后,再尝试下别的数据集,比如之前用过的CelebA
CelebA提供了每张图片40个属性的01标注,这里将Male(是否为男性)作为条件,在WGAN的基础上实现CGAN
import tensorflow as tf import numpy as np import os import matplotlib.pyplot as plt %matplotlib inline from imageio import imread, imsave, mimsave import cv2 import glob from tqdm import tqdm
载入图片
images = glob.glob(celeba/*.jpg) print(len(images))
读取图片的Male标签
tags = {} target = Male with open(list_attr_celeba.txt, r) as fr: lines = fr.readlines() all_tags = lines[0].strip( ).split() for i in range(1, len(lines)): line = lines[i].strip( ).split() if int(line[all_tags.index(target) + 1]) == 1: tags[line[0]] = [1, 0] # 男 else: tags[line[0]] = [0, 1] # 女 print(len(tags)) print(all_tags)
定义一些常量、网路输入、辅助函数
batch_size = 100 z_dim = 100 WIDTH = 64 HEIGHT = 64 LABEL = 2 LAMBDA = 10 DIS_ITERS = 3 # 5
X = tf.placeholder(dtype=tf.float32, shape=[batch_size, HEIGHT, WIDTH, 3], name=X) y_label = tf.placeholder(dtype=tf.float32, shape=[batch_size, HEIGHT, WIDTH, LABEL], name=y_label) noise = tf.placeholder(dtype=tf.float32, shape=[batch_size, z_dim], name=noise) y_noise = tf.placeholder(dtype=tf.float32, shape=[batch_size, LABEL], name=y_noise) is_training = tf.placeholder(dtype=tf.bool, name=is_training)
h1 = lrelu(tf.layers.conv2d(h0, kernel_size=5, filters=128, strides=2, padding=same))
h2 = lrelu(tf.layers.conv2d(h1, kernel_size=5, filters=256, strides=2, padding=same))
h3 = lrelu(tf.layers.conv2d(h2, kernel_size=5, filters=512, strides=2, padding=same))
h4 = tf.contrib.layers.flatten(h3) h4 = tf.layers.dense(h4, units=1) return h4
def generator(z, label, is_training=is_training): momentum = 0.9 with tf.variable_scope(generator, reuse=None): d = 4 z = tf.concat([z, label], axis=1) h0 = tf.layers.dense(z, units=d * d * 512) h0 = tf.reshape(h0, shape=[-1, d, d, 512]) h0 = tf.nn.relu(tf.contrib.layers.batch_norm(h0, is_training=is_training, decay=momentum))
h4 = tf.layers.conv2d_transpose(h3, kernel_size=5, filters=3, strides=2, padding=same, activation=tf.nn.tanh, name=g) return h4
定义损失函数
g = generator(noise, y_noise) d_real = discriminator(X, y_label) d_fake = discriminator(g, y_label, reuse=True)
loss_d_real = -tf.reduce_mean(d_real) loss_d_fake = tf.reduce_mean(d_fake) loss_g = -tf.reduce_mean(d_fake) loss_d = loss_d_real + loss_d_fake
alpha = tf.random_uniform(shape=[batch_size, 1, 1, 1], minval=0., maxval=1.) interpolates = alpha * X + (1 - alpha) * g grad = tf.gradients(discriminator(interpolates, y_label, reuse=True), [interpolates])[0] slop = tf.sqrt(tf.reduce_sum(tf.square(grad), axis=[1])) gp = tf.reduce_mean((slop - 1.) ** 2) loss_d += LAMBDA * gp
定义优化器
def montage(images): if isinstance(images, list): images = np.array(images) img_h = images.shape[1] img_w = images.shape[2] n_plots = int(np.ceil(np.sqrt(images.shape[0]))) if len(images.shape) == 4 and images.shape[3] == 3: m = np.ones( (images.shape[1] * n_plots + n_plots + 1, images.shape[2] * n_plots + n_plots + 1, 3)) * 0.5 elif len(images.shape) == 4 and images.shape[3] == 1: m = np.ones( (images.shape[1] * n_plots + n_plots + 1, images.shape[2] * n_plots + n_plots + 1, 1)) * 0.5 elif len(images.shape) == 3: m = np.ones( (images.shape[1] * n_plots + n_plots + 1, images.shape[2] * n_plots + n_plots + 1)) * 0.5 else: raise ValueError(Could not parse image shape of {}.format(images.shape)) for i in range(n_plots): for j in range(n_plots): this_filter = i * n_plots + j if this_filter < images.shape[0]: this_img = images[this_filter] m[1 + i + i * img_h:1 + i + (i + 1) * img_h, 1 + j + j * img_w:1 + j + (j + 1) * img_w] = this_img return m
整理数据
X_all = [] Y_all = [] for i in tqdm(range(len(images))): image = imread(images[i]) h = image.shape[0] w = image.shape[1] if h > w: image = image[h // 2 - w // 2: h // 2 + w // 2, :, :] else: image = image[:, w // 2 - h // 2: w // 2 + h // 2, :] image = cv2.resize(image, (WIDTH, HEIGHT)) image = (image / 255. - 0.5) * 2 X_all.append(image)
image_name = images[i][images[i].find(/) + 1:] Y_all.append(tags[image_name])
X_all = np.array(X_all) Y_all = np.array(Y_all) print(X_all.shape, Y_all.shape)
查看数据样例
for i in range(10): plt.imshow((X_all[i, :, :, :] + 1) / 2) plt.show() print(Y_all[i, :])
定义随机产生批数据的函数
def get_random_batch(): data_index = np.arange(X_all.shape[0]) np.random.shuffle(data_index) data_index = data_index[:batch_size] X_batch = X_all[data_index, :, :, :] Y_batch = Y_all[data_index, :] yn = np.copy(Y_batch) yl = np.reshape(Y_batch, [batch_size, 1, 1, LABEL]) yl = yl * np.ones([batch_size, HEIGHT, WIDTH, LABEL])
return X_batch, yn, yl
训练模型
sess = tf.Session() sess.run(tf.global_variables_initializer()) zs = np.random.uniform(-1.0, 1.0, [batch_size // 2, z_dim]).astype(np.float32) z_samples = [] y_samples = [] for i in range(batch_size // 2): z_samples.append(zs[i, :]) y_samples.append([1, 0]) z_samples.append(zs[i, :]) y_samples.append([0, 1]) samples = [] loss = {d: [], g: []}
for i in tqdm(range(60000)): for j in range(DIS_ITERS): n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32) X_batch, yn, yl = get_random_batch() _, d_ls = sess.run([optimizer_d, loss_d], feed_dict={X: X_batch, noise: n, y_label: yl, y_noise: yn, is_training: True})
_, g_ls = sess.run([optimizer_g, loss_g], feed_dict={X: X_batch, noise: n, y_label: yl, y_noise: yn, is_training: True})
loss[d].append(d_ls) loss[g].append(g_ls)
if i % 500 == 0: print(i, d_ls, g_ls) gen_imgs = sess.run(g, feed_dict={noise: z_samples, y_noise: y_samples, is_training: False}) gen_imgs = (gen_imgs + 1) / 2 imgs = [img[:, :, :] for img in gen_imgs] gen_imgs = montage(imgs) plt.axis(off) plt.imshow(gen_imgs) imsave(os.path.join(OUTPUT_DIR, sample_%d.jpg % i), gen_imgs) plt.show() samples.append(gen_imgs)
plt.plot(loss[d], label=Discriminator) plt.plot(loss[g], label=Generator) plt.legend(loc=upper right) plt.savefig(Loss.png) plt.show() mimsave(os.path.join(OUTPUT_DIR, samples.gif), samples, fps=10)
结果如下,对于每一组图片,噪音部分相同但条件不同,男左女右
保存模型
saver = tf.train.Saver() saver.save(sess, ./celeba_cgan, global_step=60000)
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