条件生成对抗网络记录与tensorflow代码及pytorch代码分析

Written by	title	date
NockinOnHeavensDoor	《Conditional Generative Adversarial Nets》	2018-5-16 06:38:33

公式及结构

在GAN中的两个神经网络分别是生成器G(z)G(z) 和判别器 D(X)D(X),CGANs目的是给这两个网络都加入一个先验的信息，用向量yy表示；现在CGANs的生成器是G(z,y)G(z,y) 和D(X,y)D(X,y) ,即有X∼G(X|z,y)X∼G(X|z,y) 和d∼D(d|X,y)d∼D(d|X,y) .把yy加入网络结构的时候直接把yy添加到一个额外的输入层。如图： 

1. GANs公式： 
   

   minGmaxDV(D,G)=Ex∼Pdata(x)[logD(x)]+Ez∼Pz(z)[log(1−D(G(z)))](1)(1)minGmaxDV(D,G)=Ex∼Pdata(x)[log⁡D(x)]+Ez∼Pz(z)[log⁡(1−D(G(z)))]
   
   注：
   • 在最大化D的时候，如果等式V右边第二项判断G错误，即伪造数据判断为真实数据，公式结果趋于负无穷；同理等式V右边第一项判断错误也使得最后结果趋于负无穷，所以需要最大化D，从而修改D的参数使得判别器更精确。如果判断正确，则公式V达到最大,即为0。
   • 在最小化G的时候，只与等式V右边第二项有关。如果等式V右边第二项判断G错误，即伪造数据判断为真实数据，公式结果趋于负无穷；所以为了使得G伪造的数据尽可能使得D判断出错，需要最小化右侧公式第二项，使得G的参数得到调整。

2. CGANs公式： 
   

   minGmaxDV(D,G)=Ex∼Pdata(x)[logD(x,y)]+Ez∼Pz(z)[log(1−D(G(z,y),y))](2)(2)minGmaxDV(D,G)=Ex∼Pdata(x)[log⁡D(x,y)]+Ez∼Pz(z)[log(1−D(G(z,y),y))]

公式的不同仅在于多了额外的条件参数yy.

• Written by NockinOnHeavensDoor

Tensorflow实现

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os


mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
mb_size = 64
Z_dim = 100
X_dim = mnist.train.images.shape[1]
y_dim = mnist.train.labels.shape[1] #y标签的维度是10
h_dim = 128


def xavier_init(size):
    in_dim = size[0]
    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
    return tf.random_normal(shape=size, stddev=xavier_stddev)


""" Discriminator Net model """
X = tf.placeholder(tf.float32, shape=[None, 784])

y = tf.placeholder(tf.float32, shape=[None, y_dim])

D_W1 = tf.Variable(xavier_init([X_dim + y_dim, h_dim])) #输入层加入y，权值也多出y
D_b1 = tf.Variable(tf.zeros(shape=[h_dim]))

D_W2 = tf.Variable(xavier_init([h_dim, 1]))
D_b2 = tf.Variable(tf.zeros(shape=[1]))

theta_D = [D_W1, D_W2, D_b1, D_b2]


def discriminator(x, y):
    # Concatenate x and y
    inputs = tf.concat(axis=1, values=[x, y]) #维度是[None,784 + 10]
    D_h1 = tf.nn.relu(tf.matmul(inputs, D_W1) + D_b1)
    D_logit = tf.matmul(D_h1, D_W2) + D_b2
    D_prob = tf.nn.sigmoid(D_logit)

    return D_prob, D_logit


""" Generator Net model """
Z = tf.placeholder(tf.float32, shape=[None, Z_dim])

G_W1 = tf.Variable(xavier_init([Z_dim + y_dim, h_dim]))
G_b1 = tf.Variable(tf.zeros(shape=[h_dim]))

G_W2 = tf.Variable(xavier_init([h_dim, X_dim]))
G_b2 = tf.Variable(tf.zeros(shape=[X_dim]))

theta_G = [G_W1, G_W2, G_b1, G_b2]


def generator(z, y):
    # Concatenate x and y
    inputs = tf.concat(axis=1, values=[z, y])
    G_h1 = tf.nn.relu(tf.matmul(inputs, G_W1) + G_b1)
    G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
    G_prob = tf.nn.sigmoid(G_log_prob)

    return G_prob


def sample_Z(m, n):
    return np.random.uniform(-1., 1., size=[m, n])


def plot(samples):
    fig = plt.figure(figsize=(4, 4))
    gs = gridspec.GridSpec(4, 4)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')

    return fig


G_sample = generator(Z, y)
D_real, D_logit_real = discriminator(X, y)
D_fake, D_logit_fake = discriminator(G_sample, y)

D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))
D_loss = D_loss_real + D_loss_fake
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))

D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)


sess = tf.Session()
sess.run(tf.global_variables_initializer())

if not os.path.exists('out/'):
    os.makedirs('out/')

i = 0

for it in range(1000000):
    if it % 1000 == 0:
        n_sample = 16

        Z_sample = sample_Z(n_sample, Z_dim)

        y_sample = np.zeros(shape=[n_sample, y_dim])
        y_sample[:, 7] = 1  #生成的假的标签
        samples = sess.run(G_sample, feed_dict={Z: Z_sample, y:y_sample})

        fig = plot(samples)
        plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')
        i += 1
        plt.close(fig)

    X_mb, y_mb = mnist.train.next_batch(mb_size)

    Z_sample = sample_Z(mb_size, Z_dim)
    # feed the value of y into the networks:
    _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: Z_sample, y:y_mb})
    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: Z_sample, y:y_mb})

    if it % 1000 == 0:
        print('Iter: {}'.format(it))
        print('D loss: {:.4}'. format(D_loss_curr))
        print('G_loss: {:.4}'.format(G_loss_curr))
        print()

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pytorch代码

import torch
import torch.nn.functional as nn
import torch.autograd as autograd
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
mb_size = 64
Z_dim = 100
X_dim = mnist.train.images.shape[1]
y_dim = mnist.train.labels.shape[1]
h_dim = 128
cnt = 0
lr = 1e-3


def xavier_init(size):
    in_dim = size[0]
    xavier_stddev = 1. / np.sqrt(in_dim / 2.)
    return Variable(torch.randn(*size) * xavier_stddev, requires_grad=True)


""" ==================== GENERATOR ======================== """

Wzh = xavier_init(size=[Z_dim + y_dim, h_dim])
bzh = Variable(torch.zeros(h_dim), requires_grad=True)

Whx = xavier_init(size=[h_dim, X_dim])
bhx = Variable(torch.zeros(X_dim), requires_grad=True)


def G(z, c):
    inputs = torch.cat([z, c], 1)
    h = nn.relu(inputs @ Wzh + bzh.repeat(inputs.size(0), 1))
    X = nn.sigmoid(h @ Whx + bhx.repeat(h.size(0), 1))
    return X


""" ==================== DISCRIMINATOR ======================== """

Wxh = xavier_init(size=[X_dim + y_dim, h_dim])
bxh = Variable(torch.zeros(h_dim), requires_grad=True)

Why = xavier_init(size=[h_dim, 1])
bhy = Variable(torch.zeros(1), requires_grad=True)


def D(X, c):
    inputs = torch.cat([X, c], 1)
    h = nn.relu(inputs @ Wxh + bxh.repeat(inputs.size(0), 1))
    y = nn.sigmoid(h @ Why + bhy.repeat(h.size(0), 1))
    return y


G_params = [Wzh, bzh, Whx, bhx]
D_params = [Wxh, bxh, Why, bhy]
params = G_params + D_params


""" ===================== TRAINING ======================== """


def reset_grad():
    for p in params:
        if p.grad is not None:
            data = p.grad.data
            p.grad = Variable(data.new().resize_as_(data).zero_())


G_solver = optim.Adam(G_params, lr=1e-3)
D_solver = optim.Adam(D_params, lr=1e-3)

ones_label = Variable(torch.ones(mb_size))
zeros_label = Variable(torch.zeros(mb_size))


for it in range(100000):
    # Sample data
    z = Variable(torch.randn(mb_size, Z_dim))
    X, c = mnist.train.next_batch(mb_size)
    X = Variable(torch.from_numpy(X))
    c = Variable(torch.from_numpy(c.astype('float32')))

    # Dicriminator forward-loss-backward-update
    G_sample = G(z, c)
    D_real = D(X, c)
    D_fake = D(G_sample, c)

    D_loss_real = nn.binary_cross_entropy(D_real, ones_label)
    D_loss_fake = nn.binary_cross_entropy(D_fake, zeros_label)
    D_loss = D_loss_real + D_loss_fake

    D_loss.backward()
    D_solver.step()

    # Housekeeping - reset gradient
    reset_grad()

    # Generator forward-loss-backward-update
    z = Variable(torch.randn(mb_size, Z_dim))
    G_sample = G(z, c)
    D_fake = D(G_sample, c)

    G_loss = nn.binary_cross_entropy(D_fake, ones_label)

    G_loss.backward()
    G_solver.step()

    # Housekeeping - reset gradient
    reset_grad()

    # Print and plot every now and then
    if it % 1000 == 0:
        print('Iter-{}; D_loss: {}; G_loss: {}'.format(it, D_loss.data.numpy(), G_loss.data.numpy()))

        c = np.zeros(shape=[mb_size, y_dim], dtype='float32')
        c[:, np.random.randint(0, 10)] = 1.
        c = Variable(torch.from_numpy(c))
        samples = G(z, c).data.numpy()[:16]

        fig = plt.figure(figsize=(4, 4))
        gs = gridspec.GridSpec(4, 4)
        gs.update(wspace=0.05, hspace=0.05)

        for i, sample in enumerate(samples):
            ax = plt.subplot(gs[i])
            plt.axis('off')
            ax.set_xticklabels([])
            ax.set_yticklabels([])
            ax.set_aspect('equal')
            plt.imshow(sample.reshape(28, 28), cmap='Greys_r')

        if not os.path.exists('out/'):
            os.makedirs('out/')

        plt.savefig('out/{}.png'.format(str(cnt).zfill(3)), bbox_inches='tight')
        cnt += 1
        plt.close(fig)

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结果

• Written by NockinOnHeavensDoor