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.gztrain-labels-idx1-ubyte.gzt10k-images-idx3-ubyte.gzt10k-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 

 

posted @ 2017-01-09 21:35  Charles-Wan  阅读(20815)  评论(14编辑  收藏  举报