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[TensorFlow] DCGAN으로 MNIST 이미지 만들기 실패 본문

개발 및 공부/라이브러리&프레임워크

[TensorFlow] DCGAN으로 MNIST 이미지 만들기 실패

NeuroWhAI 2018. 1. 29. 18:39


역시 초짜가 건들기엔 너무 무리한것 같습니다 ㅠㅠ
생성기 학습이 잘 안되네요...
판별기에 FC를 넣어서 그런가 아니면 속도좀 올린다고 conv 계층을 두개나 줄여서 그런건지.

코드:
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#-*- coding: utf-8 -*-
 
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./mnist/data/", one_hot=True)
 
total_epoch = 70
batch_size = 100
n_noise = 100
 
D_global_step = tf.Variable(0, trainable=False, name='D_global_step')
G_global_step = tf.Variable(0, trainable=False, name='G_global_step')
 
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
Z = tf.placeholder(tf.float32, [None, n_noise])
is_training = tf.placeholder(tf.bool)
 
def leaky_relu(x, leak=0.2):
    return tf.maximum(x, x * leak)
 
def generator(noise):
    with tf.variable_scope('generator'):
        output = tf.layers.dense(noise, 128*7*7)
        output = tf.reshape(output, [-1, 7, 7, 128])
        output = tf.nn.relu(tf.layers.batch_normalization(output, training=is_training))
        output = tf.layers.conv2d_transpose(output, 64, [5, 5], strides=(2, 2), padding='SAME')
        output = tf.nn.relu(tf.layers.batch_normalization(output, training=is_training))
        output = tf.layers.conv2d_transpose(output, 1, [5, 5], strides=(2, 2), padding='SAME')
        output = tf.tanh(output)
    return output
 
def discriminator(inputs, reuse=None):
    with tf.variable_scope('discriminator') as scope:
        if reuse:
            scope.reuse_variables()
        output = tf.layers.conv2d(inputs, 32, [5, 5], strides=(2, 2), padding='SAME')
        output = leaky_relu(tf.layers.batch_normalization(output, training=is_training))
        output = tf.layers.conv2d(inputs, 64, [5, 5], strides=(2, 2), padding='SAME')
        output = leaky_relu(tf.layers.batch_normalization(output, training=is_training))
        flat = tf.contrib.layers.flatten(output)
        hidden = tf.layers.dense(flat, 256, activation=tf.nn.relu)
        output = tf.layers.dense(hidden, 1, activation=None)
    return output
 
def get_noise(batch_size, n_noise):
    return np.random.uniform(-1., 1., size=[batch_size, n_noise])
 
G = generator(Z)
D_real = discriminator(X)
D_gene = discriminator(G, True)
 
loss_D_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    logits=D_real, labels=tf.ones_like(D_real)
))
loss_D_gene = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    logits=D_gene, labels=tf.zeros_like(D_gene)
))
 
loss_D = loss_D_real + loss_D_gene
loss_G = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
    logits=D_gene, labels=tf.ones_like(D_gene)
))
 
vars_D = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
    scope='discriminator')
vars_G = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
    scope='generator')
 
train_D = tf.train.AdamOptimizer().minimize(loss_D,
    var_list=vars_D, global_step=D_global_step)
train_G = tf.train.AdamOptimizer().minimize(loss_G,
    var_list=vars_G, global_step=G_global_step)
 
tf.summary.scalar('loss_D', loss_D)
tf.summary.scalar('loss_G', loss_G)
 
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
 
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter('./logs', sess.graph)
 
    total_batch = int(mnist.train.num_examples / batch_size)
 
    for epoch in range(total_epoch):
        loss_val_D, loss_val_G = 0, 0
 
        batch_xs, batch_ys = None, None
        noise = None
 
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            batch_xs = batch_xs.reshape(-1, 28, 28, 1)
            noise = get_noise(batch_size, n_noise)
 
            _, loss_val_D = sess.run([train_D, loss_D],
                feed_dict={X: batch_xs, Z: noise, is_training: True})
            _, loss_val_G = sess.run([train_G, loss_G],
                feed_dict={Z: noise, is_training: True})
 
        summary = sess.run(merged,
            feed_dict={X: batch_xs, Z: noise, is_training: False})
        writer.add_summary(summary, global_step=sess.run(G_global_step))
 
        if epoch == 0 or (epoch + 1) % 10 == 0:
            print('Epoch:', '%04d' % epoch,
                'D loss: {:.4}'.format(loss_val_D),
                'G loss: {:.4}'.format(loss_val_G))
 
            sample_size = 10
            noise = get_noise(sample_size, n_noise)
            samples = sess.run(G, feed_dict={Z: noise, is_training: False})
 
            fig, ax = plt.subplots(1, sample_size, figsize=(sample_size, 1))
 
            for i in range(sample_size):
                ax[i].set_axis_off()
                ax[i].imshow(np.reshape(samples[i], (28, 28)))
 
            plt.savefig('{}.png'.format(str(epoch).zfill(3)),
                bbox_inches='tight')
            
            plt.close(fig)
 
    
 
 cs


결과:
Epoch: 0000 D loss: 0.04285 G loss: 12.52
Epoch: 0009 D loss: 0.07978 G loss: 7.917
Epoch: 0019 D loss: 0.07283 G loss: 6.954
Epoch: 0029 D loss: 0.3557 G loss: 3.424
Epoch: 0039 D loss: 0.3438 G loss: 4.134
Epoch: 0049 D loss: 0.2844 G loss: 4.72
Epoch: 0059 D loss: 0.3627 G loss: 4.096
Epoch: 0069 D loss: 0.1933 G loss: 5.506



실행에 무려 20시간이 걸렸다면 믿으시겠습니까 흑흑

학습이 안되는건 아닌데 무엇이 문제일까요.
층이 너무 적어서?
epoch 횟수가 적어서?
FC 층 때문에?
으어엉


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