CVAE(条件自编码) Condition GAN (条件GAN) 和 VAE-GAN模型之间的区别之相关文件_conditional gan-vae

第一个文件是用于定义网络中需要使用的各种参数关于卷积和反置卷积相关的图像

详细信息可以参考github上的DCGAN中如何设置模型

import math
import numpy as np 
import tensorflow as tf
 
from tensorflow.python.framework import ops
 
from utils import *
 
if "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)
 
def bn(x, is_training, scope):
    return tf.contrib.layers.batch_norm(x,
                                        decay=0.9,
                                        updates_collections=None,
                                        epsilon=1e-5,
                                        scale=True,
                                        is_training=is_training,
                                        scope=scope)
 
def conv_out_size_same(size, stride):
    return int(math.ceil(float(size) / float(stride)))
 
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)
 
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
 
def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, name="deconv2d", stddev=0.02, 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
 
def lrelu(x, leak=0.2, name="lrelu"):
    return tf.maximum(x, leak*x)
 
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