住房建设部投诉网站,无锡做公司网站,郑州市男科医院哪比较好,苏州网站设计公司兴田德润i简介2014年GAN发表#xff0c;直到最近大火的AI生成全部有GAN的踪迹#xff0c;快来简单实现它#xff01;#xff01;#xff01;GAN通过计算图和博弈论的创新组合#xff0c;他们表明#xff0c;如果有足够的建模能力#xff0c;相互竞争的两个模型将能够通过普通的旧反向…2014年GAN发表直到最近大火的AI生成全部有GAN的踪迹快来简单实现它GAN通过计算图和博弈论的创新组合他们表明如果有足够的建模能力相互竞争的两个模型将能够通过普通的旧反向传播进行共同训练。这些模型扮演着两种不同的字面意思是对抗的角色。给定一些真实的数据集RG是生成器试图创建看起来像真实数据的假数据而D是鉴别器从真实集或G获取数据并标记差异。 G就像一造假机器通过多次画画练习使得画出来的话像真图一样。而D是试图区分的侦探团队。除了在这种情况下伪造者G永远看不到原始数据——只能看到D的判断。他们就像盲人摸象的探索伪造的人。SourseGAN实现代码#!/usr/bin/env pythonimport numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variablematplotlib_is_available True
try:from matplotlib import pyplot as plt
except ImportError:print(Will skip plotting; matplotlib is not available.)matplotlib_is_available False# Data params
data_mean 4
data_stddev 1.25# ### Uncomment only one of these to define what data is actually sent to the Discriminator
#(name, preprocess, d_input_func) (Raw data, lambda data: data, lambda x: x)
#(name, preprocess, d_input_func) (Data and variances, lambda data: decorate_with_diffs(data, 2.0), lambda x: x * 2)
#(name, preprocess, d_input_func) (Data and diffs, lambda data: decorate_with_diffs(data, 1.0), lambda x: x * 2)
(name, preprocess, d_input_func) (Only 4 moments, lambda data: get_moments(data), lambda x: 4)print(Using data [%s] % (name))# ##### DATA: Target data and generator input datadef get_distribution_sampler(mu, sigma):return lambda n: torch.Tensor(np.random.normal(mu, sigma, (1, n))) # Gaussiandef get_generator_input_sampler():return lambda m, n: torch.rand(m, n) # Uniform-dist data into generator, _NOT_ Gaussian# ##### MODELS: Generator model and discriminator modelclass Generator(nn.Module):def __init__(self, input_size, hidden_size, output_size, f):super(Generator, self).__init__()self.map1 nn.Linear(input_size, hidden_size)self.map2 nn.Linear(hidden_size, hidden_size)self.map3 nn.Linear(hidden_size, output_size)self.f fdef forward(self, x):x self.map1(x)x self.f(x)x self.map2(x)x self.f(x)x self.map3(x)return xclass Discriminator(nn.Module):def __init__(self, input_size, hidden_size, output_size, f):super(Discriminator, self).__init__()self.map1 nn.Linear(input_size, hidden_size)self.map2 nn.Linear(hidden_size, hidden_size)self.map3 nn.Linear(hidden_size, output_size)self.f fdef forward(self, x):x self.f(self.map1(x))x self.f(self.map2(x))return self.f(self.map3(x))def extract(v):return v.data.storage().tolist()def stats(d):return [np.mean(d), np.std(d)]def get_moments(d):# Return the first 4 moments of the data providedmean torch.mean(d)diffs d - meanvar torch.mean(torch.pow(diffs, 2.0))std torch.pow(var, 0.5)zscores diffs / stdskews torch.mean(torch.pow(zscores, 3.0))kurtoses torch.mean(torch.pow(zscores, 4.0)) - 3.0 # excess kurtosis, should be 0 for Gaussianfinal torch.cat((mean.reshape(1,), std.reshape(1,), skews.reshape(1,), kurtoses.reshape(1,)))return finaldef decorate_with_diffs(data, exponent, remove_raw_dataFalse):mean torch.mean(data.data, 1, keepdimTrue)mean_broadcast torch.mul(torch.ones(data.size()), mean.tolist()[0][0])diffs torch.pow(data - Variable(mean_broadcast), exponent)if remove_raw_data:return torch.cat([diffs], 1)else:return torch.cat([data, diffs], 1)def train():# Model parametersg_input_size 1 # Random noise dimension coming into generator, per output vectorg_hidden_size 5 # Generator complexityg_output_size 1 # Size of generated output vectord_input_size 500 # Minibatch size - cardinality of distributionsd_hidden_size 10 # Discriminator complexityd_output_size 1 # Single dimension for real vs. fake classificationminibatch_size d_input_sized_learning_rate 1e-3g_learning_rate 1e-3sgd_momentum 0.9num_epochs 5000print_interval 100d_steps 20g_steps 20dfe, dre, ge 0, 0, 0d_real_data, d_fake_data, g_fake_data None, None, Nonediscriminator_activation_function torch.sigmoidgenerator_activation_function torch.tanhd_sampler get_distribution_sampler(data_mean, data_stddev)gi_sampler get_generator_input_sampler()G Generator(input_sizeg_input_size,hidden_sizeg_hidden_size,output_sizeg_output_size,fgenerator_activation_function)D Discriminator(input_sized_input_func(d_input_size),hidden_sized_hidden_size,output_sized_output_size,fdiscriminator_activation_function)criterion nn.BCELoss() # Binary cross entropy: http://pytorch.org/docs/nn.html#bcelossd_optimizer optim.SGD(D.parameters(), lrd_learning_rate, momentumsgd_momentum)g_optimizer optim.SGD(G.parameters(), lrg_learning_rate, momentumsgd_momentum)for epoch in range(num_epochs):for d_index in range(d_steps):# 1. Train D on realfakeD.zero_grad()# 1A: Train D on reald_real_data Variable(d_sampler(d_input_size))d_real_decision D(preprocess(d_real_data))d_real_error criterion(d_real_decision, Variable(torch.ones([1]))) # ones trued_real_error.backward() # compute/store gradients, but dont change params# 1B: Train D on faked_gen_input Variable(gi_sampler(minibatch_size, g_input_size))d_fake_data G(d_gen_input).detach() # detach to avoid training G on these labelsd_fake_decision D(preprocess(d_fake_data.t()))d_fake_error criterion(d_fake_decision, Variable(torch.zeros([1]))) # zeros faked_fake_error.backward()d_optimizer.step() # Only optimizes Ds parameters; changes based on stored gradients from backward()dre, dfe extract(d_real_error)[0], extract(d_fake_error)[0]for g_index in range(g_steps):# 2. Train G on Ds response (but DO NOT train D on these labels)G.zero_grad()gen_input Variable(gi_sampler(minibatch_size, g_input_size))g_fake_data G(gen_input)dg_fake_decision D(preprocess(g_fake_data.t()))g_error criterion(dg_fake_decision, Variable(torch.ones([1]))) # Train G to pretend its genuineg_error.backward()g_optimizer.step() # Only optimizes Gs parametersge extract(g_error)[0]if epoch % print_interval 0:print(Epoch %s: D (%s real_err, %s fake_err) G (%s err); Real Dist (%s), Fake Dist (%s) %(epoch, dre, dfe, ge, stats(extract(d_real_data)), stats(extract(d_fake_data))))if matplotlib_is_available:print(Plotting the generated distribution...)values extract(g_fake_data)print( Values: %s % (str(values)))plt.hist(values, bins50)plt.xlabel(Value)plt.ylabel(Count)plt.title(Histogram of Generated Distribution)plt.grid(True)plt.show()train()代码输出结果个人总结GAN从编程的角度来看纯个人理解不对可指正利用numpy的random方法随机生成多维的噪音向量创建一个G网络用来生成创建一个D网络用来判断俩个网络在训练时分别进行优化先训练D网络去判断真假如果训练D为真时进行传播如果训练D为假时进行传播投入优化器1为真0为假在D的基础上训练G。*因为是随机生成所以每次生成结果不同
