一、背景
前一阶段比较忙，很久没有继续做GAN的实验了。近期终于抽空做完了infoGAN，个人认为infoGAN是对GAN的更进一步改进，由于GAN是输入的随机生成噪声，所以生成的图像也是随机的，而infoGAN想要生成的是指定特征的图像，因此infoGAN对GAN的随机输入加了约束，这是其最大的改进之处。infoGAN是16年6月份由Xi Chen等人提出的一种模型。本实验主要利用infoGAN生成宽窄不一，高低各异的服装影像。
本实验以fashion-mnist数据集为例，用尽可能少的代码实现infoGAN。
[1]文章链接：https://arxiv.org/abs/1606.03657
二、infoGAN原理
infoGAN的原理网上介绍的也比较多，这里不再过多叙述。推荐一篇对原理讲解比较清楚的文章：
[2]InfoGAN介绍
从文章中作者的介绍来看，infoGAN最主要的贡献是引入了互信息(mutual information)，通过最大化(maximizing)GAN噪声变量子集和观测值之间的互信息，以实现对学习过程的可解译性。作者将实验应用于MNIST， CelebA，SVHN数据集，结果表明引入互信息的模型都能够取得更好的效果。
In this paper, we present a simple modification to the generative adversarial network objective that encourages it to learn interpretable and meaningful representations. We do so by maximizing the mutual information between a fixed small subset of the GAN’s noise variables and the observations, which turns out to be relatively straightforward. Despite its simplicity, we found our method to be surprisingly effective: it was able to discover highly semantic and meaningful hidden representations on a number of image datasets: digits (MNIST), faces (CelebA), and house numbers (SVHN). The quality of our unsupervised disentangled representation matches previous works that made use of supervised label information [5–9]. These results suggest that generative modelling augmented with a mutual information cost could be a fruitful approach for learning disentangled representations.
通俗一点来说，“GAN模型在生成器使用噪声z的时候没有加任何的限制，所以在以一种高度混合的方式使用z，z的任何一个维度都没有明显的表示一个特征，所以在数据生成过程中，我们无法得知什么样的噪声z可以用来生成数字1，什么样的噪声z可以用来生成数字3，我们对这些一无所知，这从一点程度上限制了我们对GAN的使用[2]”。而作者对infoGAN的改进便针对这个问题，“Info代表互信息，它表示生成数据x与隐藏编码c之间关联程度的大小，为了使的x与c之间关联密切，所以我们需要最大化互信息的值，据此对原始GAN模型的值函数做了一点修改，相当于加了一个互信息的正则化项。是一个超参，通过之后的实验选择了一个最优值1。[2]”。
后文中作者也提到了，infoGAN主要针对GAN的问题进行了改进，采用的模型基础仍是DCGAN，因此infoGAN的具体实现过程可以参照DCGAN。文章中作者进行了很多公式的推导，得出的最终结论为：
Hence, InfoGAN is defined as the following minimax game with a variational regularization of mutual information and a hyperparameter λ:
不用理解上述公式也没关系，我们只要知道了infoGAN主要做了哪方面的改进就行。关于infoGAN的实现代码，网上也比较多，下面给出几个比较好的代码：
[3]https://github.com/openai/InfoGAN
[4]https://github.com/AndyHsiao26/InfoGAN-tensorflow
[5]https://github.com/hwalsuklee/tensorflow-generative-model-collections
本实验的目的就在于用最少，最简单的代码实现infoGAN，主要参考了[5]的实现过程，并在原代码的基础上进行了少量改进，只不过这次保留了原代码中的类，而我之前的GAN实现都是尽量写成函数的形式。
三、infoGAN实现
1.数据准备
这次的实验数据采用的是fashion-mnist数据集，顾名思义，该数据集与mnist数据集的格式相同，只不过该数据集是10类服饰，但图像仍是28*28的灰度图：
该数据集的下载地址为：https://github.com/zalandoresearch/fashion-mnist
打开上述地址，找到下面的数据集，点击download即可开始下载：
下载好的数据集，我们放在'./data/fashion-mnist/'文件夹下，这样就准备好了数据，不用解压，下面即可开始实验部分：
2.数据操作函数准备(utils.py)
这一部分主要准备一些数据操作函数，包括数据的加载，图像的存储，文件夹的建立等函数。这部分函数DCGAN中也能用到，因此直接将其拷贝过来，进行后续的使用。这里将该文件命名为utils.py，直接给出该文件的代码：
"""
Most codes from https://github.com/carpedm20/DCGAN-tensorflow
"""
from __future__ import division
import scipy.misc
import numpy as np
import os, gzip
import tensorflow as tf
import tensorflow.contrib.slim as slim
def load_mnist(dataset_name):
data_dir = 'data/' + dataset_name
def extract_data(filename, num_data, head_size, data_size):
with gzip.open(filename) as bytestream:
bytestream.read(head_size)
buf = bytestream.read(data_size * num_data)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float)
return data
data = extract_data(data_dir + '/train-images-idx3-ubyte.gz', 60000, 16, 28 * 28)
trX = data.reshape((60000, 28, 28, 1))
data = extract_data(data_dir + '/train-labels-idx1-ubyte.gz', 60000, 8, 1)
trY = data.reshape((60000))
data = extract_data(data_dir + '/t10k-images-idx3-ubyte.gz', 10000, 16, 28 * 28)
teX = data.reshape((10000, 28, 28, 1))
data = extract_data(data_dir + '/t10k-labels-idx1-ubyte.gz', 10000, 8, 1)
teY = data.reshape((10000))
trY = np.asarray(trY)
teY = np.asarray(teY)
X = np.concatenate((trX, teX), axis=0)
y = np.concatenate((trY, teY), axis=0).astype(np.int)
seed = 547
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(y)
y_vec = np.zeros((len(y), 10), dtype=np.float)
for i, label in enumerate(y):
y_vec[i, y[i]] = 1.0
return X / 255., y_vec
def check_folder(log_dir):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def show_all_variables():
model_vars = tf.trainable_variables()
slim.model_analyzer.analyze_vars(model_vars, print_info=True)
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
if (images.shape[3] in (3,4)):
c = images.shape[3]
img = np.zeros((h * size[0], w * size[1], c))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j * h:j * h + h, i * w:i * w + w, :] = image
return img
elif images.shape[3]==1:
img = np.zeros((h * size[0], w * size[1]))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j * h:j * h + h, i * w:i * w + w] = image[:,:,0]
return img
else:
raise ValueError('in merge(images,size) images parameter ''must have dimensions: HxW or HxWx3 or HxWx4')
def imsave(images, size, path):
image = np.squeeze(merge(images, size))
return scipy.misc.imsave(path, image)
def inverse_transform(images):
return (images+1.)/2.3.图层函数(layers.py)
这一部分主要是编写图层函数，并将这部分函数保存到layers.py文件当中，这里直接给出layers.py的代码：
"""
Most codes from https://github.com/carpedm20/DCGAN-tensorflow
"""
from utils import *
if "concat_v2" in dir(tf):
def concat(tensors, axis, *args, **kwargs):
return tf.concat_v2(tensors, axis, *args, **kwargs)
else:
def concat(tensors, axis, *args, **kwargs):
return tf.concat(tensors, axis, *args, **kwargs)
def bn(x, is_training, scope):
return tf.contrib.layers.batch_norm(x,
decay=0.9,
updates_collections=None,
epsilon=1e-5,
scale=True,
is_training=is_training,
scope=scope)
def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, name="deconv2d", stddev=0.02, with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
def lrelu(x, leak=0.2, name="lrelu"):
return tf.maximum(x, leak*x)
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
shape = input_.get_shape().as_list()
with tf.variable_scope(scope or "Linear"):
matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
else:
return tf.matmul(input_, matrix) + bias4.infoGAN的模型实现(infoGAN.py)
这一部分主要编写infoGAN的实现，包括参数，生成器和判别器，以及loss函数，这部分的代码比较长，回头我在对其进行详细的解释。最终infoGAN的代码为：
from __future__ import division
import time
from layers import *
from utils import *
class infoGAN(object):
model_name = "infoGAN" # name for checkpoint
def __init__(self, sess, epoch, batch_size, z_dim, dataset_name, checkpoint_dir, result_dir, log_dir, SUPERVISED=True):
self.sess = sess
self.dataset_name = dataset_name
self.checkpoint_dir = checkpoint_dir
self.result_dir = result_dir
self.log_dir = log_dir
self.epoch = epoch
self.batch_size = batch_size
if dataset_name == 'mnist' or dataset_name == 'fashion-mnist':
# parameters
self.input_height = 28
self.input_width = 28
self.output_height = 28
self.output_width = 28
self.z_dim = z_dim # dimension of noise-vector
self.y_dim = 12 # dimension of code-vector (label+two features)
self.c_dim = 1
self.SUPERVISED = SUPERVISED # if it is true, label info is directly used for code
# train
self.learning_rate = 0.0002
self.beta1 = 0.5
# test
self.sample_num = 64 # number of generated images to be saved
# code
self.len_discrete_code = 10 # categorical distribution (i.e. label)
self.len_continuous_code = 2 # gaussian distribution (e.g. rotation, thickness)
# load mnist
self.data_X, self.data_y = load_mnist(self.dataset_name)
# get number of batches for a single epoch
self.num_batches = len(self.data_X) // self.batch_size
else:
raise NotImplementedError
def classifier(self, x, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : (64)5c2s-(128)5c2s_BL-FC1024_BL-FC128_BL-FC12S’
# All layers except the last two layers are shared by discriminator
# Number of nodes in the last layer is reduced by half. It gives better results.
with tf.variable_scope("classifier", reuse=reuse):
net = lrelu(bn(linear(x, 64, scope='c_fc1'), is_training=is_training, scope='c_bn1'))
out_logit = linear(net, self.y_dim, scope='c_fc2')
out = tf.nn.softmax(out_logit)
return out, out_logit
def discriminator(self, x, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : (64)4c2s-(128)4c2s_BL-FC1024_BL-FC1_S
with tf.variable_scope("discriminator", reuse=reuse):
net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name='d_conv1'))
net = lrelu(bn(conv2d(net, 128, 4, 4, 2, 2, name='d_conv2'), is_training=is_training, scope='d_bn2'))
net = tf.reshape(net, [self.batch_size, -1])
net = lrelu(bn(linear(net, 1024, scope='d_fc3'), is_training=is_training, scope='d_bn3'))
out_logit = linear(net, 1, scope='d_fc4')
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def generator(self, z, y, is_training=True, reuse=False):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : FC1024_BR-FC7x7x128_BR-(64)4dc2s_BR-(1)4dc2s_S
with tf.variable_scope("generator", reuse=reuse):
# merge noise and code
z = concat([z, y], 1)
net = tf.nn.relu(bn(linear(z, 1024, scope='g_fc1'), is_training=is_training, scope='g_bn1'))
net = tf.nn.relu(bn(linear(net, 128 * 7 * 7, scope='g_fc2'), is_training=is_training, scope='g_bn2'))
net = tf.reshape(net, [self.batch_size, 7, 7, 128])
net = tf.nn.relu(
bn(deconv2d(net, [self.batch_size, 14, 14, 64], 4, 4, 2, 2, name='g_dc3'), is_training=is_training,
scope='g_bn3'))
out = tf.nn.sigmoid(deconv2d(net, [self.batch_size, 28, 28, 1], 4, 4, 2, 2, name='g_dc4'))
return out
def build_model(self):
# some parameters
image_dims = [self.input_height, self.input_width, self.c_dim]
bs = self.batch_size
""" Graph Input """
# images
self.inputs = tf.placeholder(tf.float32, [bs] + image_dims, name='real_images')
# labels
self.y = tf.placeholder(tf.float32, [bs, self.y_dim], name='y')
# noises
self.z = tf.placeholder(tf.float32, [bs, self.z_dim], name='z')
""" Loss Function """
## 1. GAN Loss
# output of D for real images
D_real, D_real_logits, _ = self.discriminator(self.inputs, is_training=True, reuse=False)
# output of D for fake images
G = self.generator(self.z, self.y, is_training=True, reuse=False)
D_fake, D_fake_logits, input4classifier_fake = self.discriminator(G, is_training=True, reuse=True)
# get loss for discriminator
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real_logits, labels=tf.ones_like(D_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.zeros_like(D_fake)))
self.d_loss = d_loss_real + d_loss_fake
# get loss for generator
self.g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.ones_like(D_fake)))
## 2. Information Loss
code_fake, code_logit_fake = self.classifier(input4classifier_fake, is_training=True, reuse=False)
# discrete code : categorical
disc_code_est = code_logit_fake[:, :self.len_discrete_code]
disc_code_tg = self.y[:, :self.len_discrete_code]
q_disc_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=disc_code_est, labels=disc_code_tg))
# continuous code : gaussian
cont_code_est = code_logit_fake[:, self.len_discrete_code:]
cont_code_tg = self.y[:, self.len_discrete_code:]
q_cont_loss = tf.reduce_mean(tf.reduce_sum(tf.square(cont_code_tg - cont_code_est), axis=1))
# get information loss
self.q_loss = q_disc_loss + q_cont_loss
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'd_' in var.name]
g_vars = [var for var in t_vars if 'g_' in var.name]
q_vars = [var for var in t_vars if ('d_' in var.name) or ('c_' in var.name) or ('g_' in var.name)]
# optimizers
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.learning_rate, beta1=self.beta1) \
.minimize(self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.learning_rate * 5, beta1=self.beta1) \
.minimize(self.g_loss, var_list=g_vars)
self.q_optim = tf.train.AdamOptimizer(self.learning_rate * 5, beta1=self.beta1) \
.minimize(self.q_loss, var_list=q_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z, self.y, is_training=False, reuse=True)
""" Summary """
d_loss_real_sum = tf.summary.scalar("d_loss_real", d_loss_real)
d_loss_fake_sum = tf.summary.scalar("d_loss_fake", d_loss_fake)
d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
q_loss_sum = tf.summary.scalar("g_loss", self.q_loss)
q_disc_sum = tf.summary.scalar("q_disc_loss", q_disc_loss)
q_cont_sum = tf.summary.scalar("q_cont_loss", q_cont_loss)
# final summary operations
self.g_sum = tf.summary.merge([d_loss_fake_sum, g_loss_sum])
self.d_sum = tf.summary.merge([d_loss_real_sum, d_loss_sum])
self.q_sum = tf.summary.merge([q_loss_sum, q_disc_sum, q_cont_sum])
def train(self):
# initialize all variables
tf.global_variables_initializer().run()
# graph inputs for visualize training results
self.sample_z = np.random.uniform(-1, 1, size=(self.batch_size , self.z_dim))
self.test_labels = self.data_y[0:self.batch_size]
self.test_codes = np.concatenate((self.test_labels, np.zeros([self.batch_size, self.len_continuous_code])),
axis=1)
# saver to save model
self.saver = tf.train.Saver()
# summary writer
self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_name, self.sess.graph)
# restore check-point if it exits
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
start_epoch = (int)(checkpoint_counter / self.num_batches)
start_batch_id = checkpoint_counter - start_epoch * self.num_batches
counter = checkpoint_counter
print(" [*] Load SUCCESS")
else:
start_epoch = 0
start_batch_id = 0
counter = 1
print(" [!] Load failed...")
# loop for epoch
start_time = time.time()
for epoch in range(start_epoch, self.epoch):
# get batch data
for idx in range(start_batch_id, self.num_batches):
batch_images = self.data_X[idx*self.batch_size:(idx+1)*self.batch_size]
# generate code
if self.SUPERVISED == True:
batch_labels = self.data_y[idx * self.batch_size:(idx + 1) * self.batch_size]
else:
batch_labels = np.random.multinomial(1,
self.len_discrete_code * [float(1.0 / self.len_discrete_code)],
size=[self.batch_size])
batch_codes = np.concatenate((batch_labels, np.random.uniform(-1, 1, size=(self.batch_size, 2))),
axis=1)
batch_z = np.random.uniform(-1, 1, [self.batch_size, self.z_dim]).astype(np.float32)
# update D network
_, summary_str, d_loss = self.sess.run([self.d_optim, self.d_sum, self.d_loss],
feed_dict={self.inputs: batch_images, self.y: batch_codes,
self.z: batch_z})
self.writer.add_summary(summary_str, counter)
# update G and Q network
_, summary_str_g, g_loss, _, summary_str_q, q_loss = self.sess.run(
[self.g_optim, self.g_sum, self.g_loss, self.q_optim, self.q_sum, self.q_loss],
feed_dict={self.inputs: batch_images, self.z: batch_z, self.y: batch_codes})
self.writer.add_summary(summary_str_g, counter)
self.writer.add_summary(summary_str_q, counter)
# display training status
counter += 1
print("Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, self.num_batches, time.time() - start_time, d_loss, g_loss))
# save training results for every 300 steps
if np.mod(counter, 300) == 0:
samples = self.sess.run(self.fake_images,
feed_dict={self.z: self.sample_z, self.y: self.test_codes})
tot_num_samples = min(self.sample_num, self.batch_size)
manifold_h = int(np.floor(np.sqrt(tot_num_samples)))
manifold_w = int(np.floor(np.sqrt(tot_num_samples)))
save_images(samples[:manifold_h * manifold_w, :, :, :], [manifold_h, manifold_w],
'./' + check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_train_{:02d}_{:04d}.png'.format(
epoch, idx))
# After an epoch, start_batch_id is set to zero
# non-zero value is only for the first epoch after loading pre-trained model
start_batch_id = 0
# save model
self.save(self.checkpoint_dir, counter)
# show temporal results
self.visualize_results(epoch)
# save model for final step
self.save(self.checkpoint_dir, counter)
def visualize_results(self, epoch):
tot_num_samples = min(self.sample_num, self.batch_size)
image_frame_dim = int(np.floor(np.sqrt(tot_num_samples)))
""" random noise, random discrete code, fixed continuous code """
y = np.random.choice(self.len_discrete_code, self.batch_size)
y_one_hot = np.zeros((self.batch_size, self.y_dim))
y_one_hot[np.arange(self.batch_size), y] = 1
z_sample = np.random.uniform(-1, 1, size=(self.batch_size, self.z_dim))
samples = self.sess.run(self.fake_images, feed_dict={self.z: z_sample, self.y: y_one_hot})
save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_epoch%03d' % epoch + '_test_all_classes.png')
""" specified condition, random noise """
n_styles = 10 # must be less than or equal to self.batch_size
np.random.seed()
si = np.random.choice(self.batch_size, n_styles)
for l in range(self.len_discrete_code):
y = np.zeros(self.batch_size, dtype=np.int64) + l
y_one_hot = np.zeros((self.batch_size, self.y_dim))
y_one_hot[np.arange(self.batch_size), y] = 1
samples = self.sess.run(self.fake_images, feed_dict={self.z: z_sample, self.y: y_one_hot})
# save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
# check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_epoch%03d' % epoch + '_test_class_%d.png' % l)
samples = samples[si, :, :, :]
if l == 0:
all_samples = samples
else:
all_samples = np.concatenate((all_samples, samples), axis=0)
""" save merged images to check style-consistency """
canvas = np.zeros_like(all_samples)
for s in range(n_styles):
for c in range(self.len_discrete_code):
canvas[s * self.len_discrete_code + c, :, :, :] = all_samples[c * n_styles + s, :, :, :]
save_images(canvas, [n_styles, self.len_discrete_code],
check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_epoch%03d' % epoch + '_test_all_classes_style_by_style.png')
""" fixed noise """
assert self.len_continuous_code == 2
c1 = np.linspace(-1, 1, image_frame_dim)
c2 = np.linspace(-1, 1, image_frame_dim)
xv, yv = np.meshgrid(c1, c2)
xv = xv[:image_frame_dim,:image_frame_dim]
yv = yv[:image_frame_dim, :image_frame_dim]
c1 = xv.flatten()
c2 = yv.flatten()
z_fixed = np.zeros([self.batch_size, self.z_dim])
for l in range(self.len_discrete_code):
y = np.zeros(self.batch_size, dtype=np.int64) + l
y_one_hot = np.zeros((self.batch_size, self.y_dim))
y_one_hot[np.arange(self.batch_size), y] = 1
y_one_hot[np.arange(image_frame_dim*image_frame_dim), self.len_discrete_code] = c1
y_one_hot[np.arange(image_frame_dim*image_frame_dim), self.len_discrete_code+1] = c2
samples = self.sess.run(self.fake_images,
feed_dict={ self.z: z_fixed, self.y: y_one_hot})
save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_epoch%03d' % epoch + '_test_class_c1c2_%d.png' % l)
@property
def model_dir(self):
return "{}_{}_{}_{}".format(
self.model_name, self.dataset_name,
self.batch_size, self.z_dim)
def save(self, checkpoint_dir, step):
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir, self.model_name)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,os.path.join(checkpoint_dir, self.model_name+'.model'), global_step=step)
def load(self, checkpoint_dir):
import re
print(" [*] Reading checkpoints...")
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir, self.model_name)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
counter = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name)).group(0))
print(" [*] Success to read {}".format(ckpt_name))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 05.main函数实现模型训练(main.py)
最终就是训练模型过程了，这了main.py文件的主要工作就是设置参数，创建infoGAN模型，进行模型的训练，main.py文件的主要代码为：
from infoGAN import infoGAN
import tensorflow as tf
"""main"""
def main():
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# declare instance for GAN
infogan = infoGAN(sess,
epoch=20,
batch_size=64,
z_dim=62,
dataset_name='fashion-mnist',
checkpoint_dir='checkpoint',
result_dir='results',
log_dir='logs')
# build graph
infogan.build_model()
# show network architecture
# show_all_variables()
# launch the graph in a session
infogan.train()
print(" [*] Training finished!")
# visualize learned generator
infogan.visualize_results(20-1)
print(" [*] Testing finished!")
if __name__ == '__main__':
main()6.模型的执行
模型的执行只需运行main.py文件即可，然后等模型训练完毕即可查看模型的结果。
四、实验结果
实验一共设置了20个epoch，训练效果比较好。这里直接展示关于衣服的训练结果。
当epoch=1时候，实验的结果为：
当epoch=5的时候，实验的结果为：
当epoch=10的时候，实验的结果为：
当epoch=20的时候，实验的结果为：
五、分析
1.实验的结果还是比较好的，即使epoch=1，也能够比较清晰的看出最后的生成服饰图像，而且也能够明显的看到生成的服饰宽窄各异的衣服。
2.所有文件的结构为：
-- data (原始数据集的文件夹)
|------ fashion-mnist
|------ t10k-images-idx3-ubyte.gz
|------ t10k-labels-idx1-ubyte.gz
|------ train-images-idx3-ubyte.gz
|------ train-labels-idx1-ubyte.gz
-- utils.py
{
import...
def load_mnist(dataset_name):...
def check_folder(log_dir):...
def show_all_variables():...
def save_images(images, size, image_path):...
def merge(images, size):...
def imsave(images, size, path):...
def inverse_transform(images):...
}
-- layers.py
{
import...
def bn(x, is_training, scope):...
def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d"):...
def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, name="deconv2d", stddev=0.02, with_w=False):...
def lrelu(x, leak=0.2, name="lrelu"):...
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):...
}
-- infoGAN.py
{
import...
class infoGAN(object):
def __init__(self, sess, epoch, batch_size, z_dim, dataset_name, checkpoint_dir, result_dir, log_dir, SUPERVISED=True):...
def classifier(self, x, is_training=True, reuse=False):...
def discriminator(self, x, is_training=True, reuse=False):...
def generator(self, z, y, is_training=True, reuse=False):...
def build_model(self):...
def train(self):...
def visualize_results(self, epoch):...
def model_dir(self):...
def save(self, checkpoint_dir, step):...
def load(self, checkpoint_dir):...
}
-- main.py
{
import ...
def main():...
if __name__ == '__main__':...
}