CVAE(条件自编码) Condition GAN (条件GAN) 和 VAE-GAN模型之间的区别之CVAE_vae与cvae区别

# 使用CVAE(条件自编码) 训练fashion-mnist数据集

import os
import time
import tensorflow as tf
import numpy as np
 
from ops import *
from utils import *
 
class CVAE(object):
    model_name = "CVAE"     # name for checkpoint
 
    def __init__(self, sess, epoch, batch_size, z_dim, dataset_name, checkpoint_dir, result_dir, log_dir):
        self.sess = sess
        self.dataset_name = dataset_name
        self.checkpoint_dir = checkpoint_dir
        self.result_dir = result_dir
        self.log_dir = log_dir
        self.epoch = epoch
        self.batch_size = batch_size
        self.mean = 0
        self.var =1
 
        if dataset_name == 'mnist' or dataset_name == 'fashion-mnist':
            # parameters
            self.input_height = 28
            self.input_width = 28
            self.output_height = 28
            self.output_width = 28
 
            self.z_dim = z_dim         # dimension of noise-vector
            self.y_dim = 10         # dimension of condition-vector (label)
            self.c_dim = 1
 
            # train
            self.learning_rate = 0.0002
            self.beta1 = 0.5
 
            # test
            self.sample_num = 64  # number of generated images to be saved
 
            # load mnist
            self.data_X, self.data_y = load_mnist(self.dataset_name)
 
            # get number of batches for a single epoch
            self.num_batches = len(self.data_X) // self.batch_size
        else:
            print("********there is no other dataset to do *********")
            raise NotImplementedError
 
    # 编码器中输入的是真实图像和噪音向量
    def encoder(self, x, y, is_training=True, reuse=False):
        with tf.variable_scope("encoder", reuse=reuse):
            y = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
            x = conv_cond_concat(x, y)
            
            net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name='en_conv1'))
            net = lrelu(bn(conv2d(net, 128, 4, 4, 2, 2, name='en_conv2'), is_training=is_training, scope='en_bn2'))
            net = tf.reshape(net, [self.batch_size, -1])
            net = lrelu(bn(linear(net, 1024, scope='en_fc3'), is_training=is_training, scope='en_bn3'))
            gaussian_params = linear(net, 2 * self.z_dim, scope='en_fc4')
 
            mean = gaussian_params[:, :self.z_dim]
            stddev = tf.nn.softplus(gaussian_params[:, self.z_dim:])
 
        return mean, stddev
 
    # 定义解码器的相关操作
    def decoder(self, z, y, is_training=True, reuse=False):
        with tf.variable_scope("decoder", reuse=reuse):
            
            z = concat([z, y], 1)
            net = tf.nn.relu(bn(linear(z, 1024, scope='de_fc1'), is_training=is_training, scope='de_bn1'))
            net = tf.nn.relu(bn(linear(net, 128 * 7 * 7, scope='de_fc2'), is_training=is_training, scope='de_bn2'))
            net = tf.reshape(net, [self.batch_size, 7, 7, 128])
            net = tf.nn.relu(
                bn(deconv2d(net, [self.batch_size, 14, 14, 64], 4, 4, 2, 2, name='de_dc3'), is_training=is_training,
                   scope='de_bn3'))
 
            out = tf.nn.sigmoid(deconv2d(net, [self.batch_size, 28, 28, 1], 4, 4, 2, 2, name='de_dc4'))
 
            return out
 
    def build_model(self):
        
        image_dims = [self.input_height, self.input_width, self.c_dim]
        bs = self.batch_size
        
 
        self.inputs = tf.placeholder(tf.float32, [bs] + image_dims, name='real_images')
        self.y = tf.placeholder(tf.float32, [bs, self.y_dim], name='y')
        self.z = tf.placeholder(tf.float32, [bs, self.z_dim], name='z')
 
        # 编码器中返回的数据是均值和方差 经过运算之后返回数据
        mu, sigma = self.encoder(self.inputs, self.y,  is_training=True, reuse=False)
        z = mu + sigma * tf.random_normal(tf.shape(mu), 0, 1, dtype=tf.float32)
       
        # 解码器输出真实图像 
        self.out = self.decoder(z, self.y, is_training=True, reuse=False)
 
        # 定义loss函数
        marginal_likelihood = tf.reduce_sum(self.inputs * tf.log(self.out) + (1 - self.inputs) * tf.log(1 - self.out), [1, 2])
        KL_divergence = 0.5 * tf.reduce_sum(tf.square(mu) + tf.square(sigma) - tf.log(1e-8 + tf.square(sigma)) - 1, [1])
 
        self.neg_loglikelihood = -tf.reduce_mean(marginal_likelihood)
        self.KL_divergence = tf.reduce_mean(KL_divergence)
        
        # 这个损失函数不是很懂 生成结果好 就这样用吧
        self.loss = self.neg_loglikelihood + self.KL_divergence
 
        # 定义优化器
        t_vars = tf.trainable_variables()
        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
            self.optim = tf.train.AdamOptimizer(self.learning_rate*5, beta1=self.beta1) \
                      .minimize(self.loss, var_list=t_vars)
 
        # is_training设置为false生成图像 但是不参与模型修改参数
        self.fake_images = self.decoder(self.z, self.y, is_training=False, reuse=True)
 
        self.merged_summary_op = tf.summary.merge_all()
 
    def train(self):
 
        tf.global_variables_initializer().run()
 
        # 标签使用的是前batch_size个图像
        self.sample_z = np.random.normal(self.mean, self.var, (self.batch_size, self.z_dim)).astype(np.float32)
        self.test_labels = self.data_y[0:self.batch_size]
 
        start_epoch = 0
        start_batch_id = 0
        counter = 1
       
        start_time = time.time()
        for epoch in range(start_epoch, self.epoch):
 
            for idx in range(start_batch_id, self.num_batches):
                batch_images = self.data_X[idx*self.batch_size:(idx+1)*self.batch_size]
                batch_labels = self.data_y[idx * self.batch_size:(idx + 1) * self.batch_size]
                batch_z = np.random.uniform(-1, 1, [self.batch_size, self.z_dim]).astype(np.float32)
 
                _, summary_str, loss, nll_loss, kl_loss = self.sess.run([self.optim, self.merged_summary_op, self.loss, self.neg_loglikelihood, self.KL_divergence],
                                               feed_dict={self.inputs: batch_images, self.y: batch_labels, self.z: batch_z})
 
                counter += 1
                print("Epoch: [%2d] [%4d/%4d] time: %4.4f, loss: %.8f, nll: %.8f, kl: %.8f" \
                      % (epoch, idx, self.num_batches, time.time() - start_time, loss, nll_loss, kl_loss))
 
                # save training results for every 300 steps
                if np.mod(counter, 300) == 0:
                    samples = self.sess.run(self.fake_images,
                                            feed_dict={self.z: self.sample_z, self.y: self.test_labels})
                    tot_num_samples = min(self.sample_num, self.batch_size)
                    manifold_h = int(np.floor(np.sqrt(tot_num_samples)))
                    manifold_w = int(np.floor(np.sqrt(tot_num_samples)))
                    save_images(samples[:manifold_h * manifold_w, :, :, :], [manifold_h, manifold_w],
                                './' + check_folder(self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_train_{:02d}_{:04d}.png'.format(
                                    epoch, idx))
            start_batch_id = 0
 
    @property
    def model_dir(self):
        return "{}_{}_{}_{}".format(
            self.model_name, self.dataset_name,
            self.batch_size, self.z_dim)

训练的结果：