为什么 GAN 无法从某些分布中生成样本？
mrg*_*oom 7 python deep-learning keras generative-adversarial-network
我试图基于这个实现在 Keras 中实现基本的 GAN 。
如果我在抛物线 GAN 上采样点是收敛的并且能够从这个分布中产生样本，但是如果例如我在圆上采样点它就会失败。我想知道为什么 GAN 很难？如何修复？
这是抛物线的学习过程：
以下是圆的学习过程：
这是要重现的代码：
from __future__ import print_function, division
import warnings
warnings.filterwarnings('ignore')
import os
import shutil
from datetime import datetime
from keras.layers import Input, Dense
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
from sklearn import datasets
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import matplotlib.pyplot as plt
import cv2
# Derived from original code https://github.com/eriklindernoren/Keras-GAN/blob/master/gan/gan.py
def print_env_info():
print('-' * 60)
import keras
print('keras.__version__', keras.__version__)
print('-' * 60)
import tensorflow as tf
print('tf.__version__', tf.__version__)
print('-' * 60)
class GAN():
def __init__(self):
self.latent_dim = 128
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generates imgs
z = Input(shape=(self.latent_dim,))
img = self.generator(z)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The discriminator takes generated images as input and determines validity
validity = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model(z, validity)
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
# Tensorboard writer
log_dir = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S")
self.writer = tf.summary.FileWriter(log_dir)
def build_generator(self):
model = Sequential()
model.add(Dense(64, input_dim=self.latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(128, input_dim=2))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(2, activation='tanh'))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
def build_discriminator(self):
model = Sequential()
model.add(Dense(64, input_dim=2))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(128, input_dim=2))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=(2, ))
validity = model(img)
return Model(img, validity)
def generate_dataset(self, n_samples=10000):
# # V1: y = x^2
x = np.random.uniform(-1, 1, size=n_samples)
y = x ** 2
data = np.stack([x, y], axis=1)
# V2: x ^ 2 + y ^ 2 = 1
# angle = np.random.uniform(0, 1, size=n_samples) * (np.pi * 2)
# x = np.cos(angle)
# y = np.sin(angle)
# data = np.stack([x, y], axis=1)
# V3: swiss roll
# data, _ = datasets.make_swiss_roll(n_samples=n_samples, noise=0.0, random_state=0)
# data = np.stack([data[:, 0], data[:, 2]], axis=1)
# data = data - np.min(data, axis=0)
# data = data / np.max(data, axis=0)
# data = 2 * data - 1.0
# # V4:
# data, _ = datasets.make_moons(n_samples=n_samples, shuffle=False, noise=None, random_state=0)
# data = data - np.min(data, axis=0)
# data = data / np.max(data, axis=0)
# data = 2 * data - 1.0
return data
def summary_image(self, tensor):
import io
from PIL import Image
tensor = tensor.astype(np.uint8)
height, width, channel = tensor.shape
image = Image.fromarray(tensor)
output = io.BytesIO()
image.save(output, format='PNG')
image_string = output.getvalue()
output.close()
return tf.Summary.Image(height=height,
width=width,
colorspace=channel,
encoded_image_string=image_string)
def get_visualization(self, epoch):
def generate_fake_data(n_samples):
noise = np.random.normal(0, 1, (n_samples, self.latent_dim))
X_hat = self.generator.predict(noise)
x = X_hat[:, 0]
y = X_hat[:, 1]
return x, y
def save_figure():
x_fake, y_fake = generate_fake_data(n_samples=100)
data = self.generate_dataset(n_samples=1000)
x_real, y_real = data[:, 0], data[:, 1]
axes = plt.gca()
axes.set_xlim([-1, 1])
axes.set_ylim([-1, 1])
axes.set_aspect('equal', 'datalim')
plt.scatter(x_real, y_real, s=1, color='b', alpha=0.2)
plt.scatter(x_fake, y_fake, s=1, color='r')
plt.savefig(f'images/{epoch}.png')
plt.close()
save_figure()
image = cv2.imread(f'images/{epoch}.png')
image = self.summary_image(image)
return image
def train(self, epochs, batch_size, sample_interval):
# Load the dataset
X_train = self.generate_dataset()
print('X_train.shape', X_train.shape)
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in tqdm(range(epochs), total=epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
# Generate a batch of new images
gen_imgs = self.generator.predict(noise)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs, valid)
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
# Train the generator (to have the discriminator label samples as valid)
g_loss = self.combined.train_on_batch(noise, valid)
# Print the progress
# print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
if epoch % sample_interval == 0:
image_summary = tf.Summary(value=[tf.Summary.Value(tag='fake', image=self.get_visualization(epoch))])
self.writer.add_summary(image_summary, epoch)
if epoch % sample_interval == 0:
summary = tf.Summary(value=[
tf.Summary.Value(tag="loss/D_loss", simple_value=d_loss[0]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="D_loss/D_loss_real", simple_value=d_loss_real[0]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="D_loss/D_loss_fake", simple_value=d_loss_fake[0]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="loss/Acc", simple_value=100*d_loss[1]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="D_loss/Acc_real", simple_value=100*d_loss_real[1]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="D_loss/Acc_fake", simple_value=100*d_loss_fake[1]),
])
self.writer.add_summary(summary, epoch)
summary = tf.Summary(value=[
tf.Summary.Value(tag="loss/G_loss", simple_value=g_loss),
])
self.writer.add_summary(summary, epoch)
if __name__ == '__main__':
print_env_info()
if os.path.exists('logs'):
shutil.rmtree('logs')
if os.path.exists('images'):
shutil.rmtree('images')
os.makedirs('images')
gan = GAN()
gan.train(epochs=10000, batch_size=32, sample_interval=200)
从 Tensorboard 中的准确率图中可以看出，这里的主要问题在于判别器。因为它的准确率在 50-60% 左右波动并且没有提高。这是非常糟糕的，因为生成器位于下游，在鉴别器达到不错的精度之前无法进行训练。那么判别器有什么问题呢？
首先，这是你训练它的方式。您分两批向其喂入阳性样本和阴性样本。这会生成梯度，将模型系数随机推向相反的方向，且收敛性非常差。如果将两种类型的样本组合在一个批次中，收敛性将显着提高。
第二，批量大小。围绕一个圆的 32 个随机点太少，模型无法感受到与 32 个随机点的差异。您需要有至少 256 的批量大小。
第三，隐藏层神经元的数量。实际上，对于生成器和鉴别器中如此简单的数据来说，神经元太多了。判别器中的神经元太多似乎并没有多大害处，但是生成器中的神经元太多会使其变得太不稳定，判别器每次都会收到不同的训练数据，这也是它无法正确训练的又一个原因。如果将 16 和 32 个隐藏神经元而不是 64 和 128 个隐藏神经元放入生成器中，效果会好得多。
最后一点：让你的圆难以学习的不仅是圆形的形状，还有它的大小。它的半径为 1，而 1 是生成器的饱和值，因此它很容易生成 1 左右的值。这给生成器带来了额外的麻烦：它开始接收与真实数据太接近的假数据在达到不错的精度之前。
总结一下：
- 将真数据和假数据合并到一个批次中。
- 使用更大的批量大小（至少 256）。
- 至少减少生成器中的神经元数量（例如减少到 16 和 32）。
享受结果：
还有一件事：最好在这个社区https://stats.stackexchange.com/中提出此类问题。