生成对抗网络DCGAN+Tensorflow代码学习笔记（三）----ops.py

ops.py主要定义了一些变量连接的函数、批处理规范化的函数、卷积函数、解卷积函数、激励函数、线性运算函数。

import mathimport numpy as np import tensorflow as tf#导入tensorflow.python.framework模块，包含了tensorflow中图、张量等的定义操作from tensorflow.python.framework import opsfrom utils import *try:  image_summary = tf.image_summary  scalar_summary = tf.scalar_summary  histogram_summary = tf.histogram_summary  merge_summary = tf.merge_summary  SummaryWriter = tf.train.SummaryWriterexcept:  image_summary = tf.summary.image  scalar_summary = tf.summary.scalar  histogram_summary = tf.summary.histogram  merge_summary = tf.summary.merge  SummaryWriter = tf.summary.FileWriter#连接多个tensorif "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)#函数批处理规范化class batch_norm(object):  def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):    with tf.variable_scope(name):      self.epsilon  = epsilon      self.momentum = momentum      self.name = name  def __call__(self, x, train=True):    return tf.contrib.layers.batch_norm(x,                      decay=self.momentum,                       updates_collections=None,                      epsilon=self.epsilon,                      scale=True,                      is_training=train,                      scope=self.name)#定义conv_cond_concat(x,y)函数。连接x,y与Int32型的[x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]]维度的张量乘积。def conv_cond_concat(x, y):  """Concatenate conditioning vector on feature map axis."""  x_shapes = x.get_shape()  y_shapes = y.get_shape()  return concat([    x, y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3)#卷积层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#反卷积层def deconv2d(input_, output_shape,       k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,       name="deconv2d", 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#定义一个lrelu激励函数def lrelu(x, leak=0.2, name="lrelu"):  return tf.maximum(x, leak*x)#进行线性运算，获取一个随机正态分布矩阵，获取初始偏置值，如果with_w为真，则返回xw+b，权值w和偏置值b；否则返回xw+b。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) + bias