【深度学习与tensorflow2.0实战】（网易云课堂）13-GAN

GAN原理

Having Fun
▪ https://reiinakano.github.io/gan-playground/
▪ https://affinelayer.com/pixsrv/
▪ https://www.youtube.com/watch?v=9reHvktowLY&feature=youtu.be
▪ https://github.com/ajbrock/Neural-Photo-Editor
▪ https://github.com/nashory/gans-awesome-applications

纳什均衡-D、G

图片变大


初始不会有overlap（重叠）

EM距离

WD解决早期GAN不能convert的情况。梯度消失等问题

GAN实战

gan.py

import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers


class Generator(keras.Model):

    def __init__(self):
        super(Generator, self).__init__()  

        # z: [b, 100] => [b, 3*3*512] => [b, 3, 3, 512] => [b, 64, 64, 3]
        self.fc = layers.Dense(3*3*512)

        self.conv1 = layers.Conv2DTranspose(256, 3, 3, 'valid')
        self.bn1 = layers.BatchNormalization()

        self.conv2 = layers.Conv2DTranspose(128, 5, 2, 'valid')
        self.bn2 = layers.BatchNormalization()

        self.conv3 = layers.Conv2DTranspose(3, 4, 3, 'valid')

    def call(self, inputs, training=None):
        # [z, 100] => [z, 3*3*512]
        x = self.fc(inputs)
        x = tf.reshape(x, [-1, 3, 3, 512])
        x = tf.nn.leaky_relu(x)

        #
        x = tf.nn.leaky_relu(self.bn1(self.conv1(x), training=training))
        x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))
        x = self.conv3(x)
        x = tf.tanh(x)

        return x


class Discriminator(keras.Model):

    def __init__(self):
        super(Discriminator, self).__init__()

        # [b, 64, 64, 3] => [b, 1]
        self.conv1 = layers.Conv2D(64, 5, 3, 'valid')

        self.conv2 = layers.Conv2D(128, 5, 3, 'valid')
        self.bn2 = layers.BatchNormalization()

        self.conv3 = layers.Conv2D(256, 5, 3, 'valid')
        self.bn3 = layers.BatchNormalization()

        # [b, h, w ,c] => [b, -1]
        self.