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

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:])])
  
# BN 层，这里我们直接用官方的 BN 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)