不要怂,就是GAN (生成式对抗网络) (二):数据读取和操作
前面我们了解了 GAN 的原理,下面我们就来用 TensorFlow 搭建 GAN(严格说来是 DCGAN,如无特别说明,本系列文章所说的 GAN 均指 DCGAN),如前面所说,GAN 分为有约束条件的 GAN,和不加约束条件的GAN,我们先来搭建一个简单的 MNIST 数据集上加约束条件的 GAN。
首先下载数据:在 /home/your_name/TensorFlow/DCGAN/ 下建立文件夹 data/mnist,从 http://yann.lecun.com/exdb/mnist/ 网站上下载 mnist 数据集 train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz 到 mnist 文件夹下得到四个 .gz 文件。
数据下载好之后,在 /home/your_name/TensorFlow/DCGAN/ 下新建文件 read_data.py 读取数据,输入如下代码:
import os
import numpy as np
def read_data():
# 数据目录
data_dir = '/home/your_name/TensorFlow/DCGAN/data/mnist'
# 打开训练数据
fd = open(os.path.join(data_dir,'train-images-idx3-ubyte'))
# 转化成 numpy 数组
loaded = np.fromfile(file=fd,dtype=np.uint8)
# 根据 mnist 官网描述的数据格式,图像像素从 16 字节开始
trX = loaded[16:].reshape((60000,28,28,1)).astype(np.float)
# 训练 label
fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
trY = loaded[8:].reshape((60000)).astype(np.float)
# 测试数据
fd = open(os.path.join(data_dir,'t10k-images-idx3-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
teX = loaded[16:].reshape((10000,28,28,1)).astype(np.float)
# 测试 label
fd = open(os.path.join(data_dir,'t10k-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd,dtype=np.uint8)
teY = loaded[8:].reshape((10000)).astype(np.float)
trY = np.asarray(trY)
teY = np.asarray(teY)
# 由于生成网络由服从某一分布的噪声生成图片,不需要测试集,
# 所以把训练和测试两部分数据合并
X = np.concatenate((trX, teX), axis=0)
y = np.concatenate((trY, teY), axis=0)
# 打乱排序
seed = 547
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(y)
# 这里,y_vec 表示对网络所加的约束条件,这个条件是类别标签,
# 可以看到,y_vec 实际就是对 y 的独热编码,关于什么是独热编码,
# 请参考 http://www.cnblogs.com/Charles-Wan/p/6207039.html
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
这里顺便说明一下,由于 MNIST 数据总体占得内存不大(可以看下载的文件,最大的一个 45M 左右,)所以这样读取数据是允许的,一般情况下,数据特别庞大的时候,建议把数据转化成 tfrecords,用 TensorFlow 标准的数据读取格式,这样能带来比较高的效率。
然后,定义一些基本的操作层,例如卷积,池化,全连接等层,在 /home/your_name/TensorFlow/DCGAN/ 新建文件 ops.py,输入如下代码:
import tensorflow as tf
from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm
# 常数偏置
def bias(name, shape, bias_start = 0.0, trainable = True):
dtype = tf.float32
var = tf.get_variable(name, shape, tf.float32, trainable = trainable,
initializer = tf.constant_initializer(
bias_start, dtype = dtype))
return var
# 随机权重
def weight(name, shape, stddev = 0.02, trainable = True):
dtype = tf.float32
var = tf.get_variable(name, shape, tf.float32, trainable = trainable,
initializer = tf.random_normal_initializer(
stddev = stddev, dtype = dtype))
return var
# 全连接层
def fully_connected(value, output_shape, name = 'fully_connected', with_w = False):
shape = value.get_shape().as_list()
with tf.variable_scope(name):
weights = weight('weights', [shape[1], output_shape], 0.02)
biases = bias('biases', [output_shape], 0.0)
if with_w:
return tf.matmul(value, weights) + biases, weights, biases
else:
return tf.matmul(value, weights) + biases
# Leaky-ReLu 层
def lrelu(x, leak=0.2, name = 'lrelu'):
with tf.variable_scope(name):
return tf.maximum(x, leak*x, name = name)
# ReLu 层
def relu(value, name = 'relu'):
with tf.variable_scope(name):
return tf.nn.relu(value)
# 解卷积层
def deconv2d(value, output_shape, k_h = 5, k_w = 5, strides =[1, 2, 2, 1],
name = 'deconv2d', with_w = False):
with tf.variable_scope(name):
weights = weight('weights',
[k_h, k_w, output_shape[-1], value.get_shape()[-1]])
deconv = tf.nn.conv2d_transpose(value, weights,
output_shape, strides = strides)
biases = bias('biases', [output_shape[-1]])
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, weights, biases
else:
return deconv
# 卷积层
def conv2d(value, output_dim, k_h = 5, k_w = 5,
strides =[1, 2, 2, 1], name = 'conv2d'):
with tf.variable_scope(name):
weights = weight('weights',
[k_h, k_w, value.get_shape()[-1], output_dim])
conv = tf.nn.conv2d(value, weights, strides = strides, padding = 'SAME')
biases = bias('biases', [output_dim])
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
# 把约束条件串联到 feature map
def conv_cond_concat(value, cond, name = 'concat'):
# 把张量的维度形状转化成 Python 的 list
value_shapes = value.get_shape().as_list()
cond_shapes = cond.get_shape().as_list()
# 在第三个维度上(feature map 维度上)把条件和输入串联起来,
# 条件会被预先设为四维张量的形式,假设输入为 [64, 32, 32, 32] 维的张量,
# 条件为 [64, 32, 32, 10] 维的张量,那么输出就是一个 [64, 32, 32, 42] 维张量
with tf.variable_scope(name):
return tf.concat(3, [value,
cond * tf.ones(value_shapes[0:3] + cond_shapes[3:])])
# Batch Normalization 层
def batch_norm_layer(value, is_train = True, name = 'batch_norm'):
with tf.variable_scope(name) as scope:
if is_train:
return batch_norm(value, decay = 0.9, epsilon = 1e-5, scale = True,
is_training = is_train,
updates_collections = None, scope = scope)
else:
return batch_norm(value, decay = 0.9, epsilon = 1e-5, scale = True,
is_training = is_train, reuse = True,
updates_collections = None, scope = scope)
TensorFlow 里使用 Batch Normalization 层,有很多种方法,这里我们直接使用官方 contrib 里面的层,其中 decay 指的是滑动平均的 decay,epsilon 作用是加到分母 variance 上避免分母为零,scale 是个布尔变量,如果为真值 True, 结果要乘以 gamma,否则 gamma 不使用,is_train 也是布尔变量,为真值代表训练过程,否则代表测试过程(在 BN 层中,训练过程和测试过程是不同的,具体请参考论文:https://arxiv.org/abs/1502.03167)。关于 batch_norm 的其他的参数,请看参考文献2。
参考文献:
1. https://github.com/carpedm20/DCGAN-tensorflow
2. https://github.com/tensorflow/tensorflow/blob/b826b79718e3e93148c3545e7aa3f90891744cc0/tensorflow/contrib/layers/python/layers/layers.py#L100