*Besides GAN, other famous generative models are Fully visible belief networks (FVBNs) and Variational autoencoder (VAE)*.When we actually train the model, the above min-max problem is solved by alternately updating the discriminator \(D({\bf s})\) and the generator \(G({\bf z})\) [4]. The actual training procedures are described as follows:

DCGANs stands for, Deep Convolutional GAN. Though GANs were both deep and convolutional prior to the DCGAN, thus the name DCGAN is useful to refer to this specific style of architecture **Now, lets define some notation to be used throughout tutorial starting with the discriminator**. Let \(x\) be data representing an image. \(D(x)\) is the discriminator network which outputs the (scalar) probability that \(x\) came from training data rather than the generator. Here, since we are dealing with images the input to \(D(x)\) is an image of CHW size 3x64x64. Intuitively, \(D(x)\) should be HIGH when \(x\) comes from training data and LOW when \(x\) comes from the generator. \(D(x)\) can also be thought of as a traditional binary classifier.Avoiding the over training of the discriminator, we have a more refined objective to have the statistics of the features in the generated images to match those of real images in the intermediate layer of the discriminator.In this tutorial, we generate images with generative adversarial network (GAN). It is a kind of generative model with deep neural network, and often applied to the image generation. The GAN technique is also applied to PaintsChainer a famous automatic coloring service.The training process is explained by the following mathematical expressions. First, since the discriminator \(D({\bf s})\) is the probability that a sample \({\bf s}\) is generated from the data distribution at, it can be expressed as follows:

** 45**. DCGAN • Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015 - Tricks for gradient flow • Max pooling → Strided convolution.. $ pwd /root2chainer/chainer/examples/dcgan $ python train_dcgan.py --gpu 0 GPU: 0 # Minibatch-size: 50 # n_hidden: 100 # epoch: 1000 epoch iteration gen/loss dis/loss ................] 0.01% 0 100 1.2292 1.76914 total [..................................................] 0.02% this epoch [#########.........................................] 19.00% 190 iter, 0 epoch / 1000 epochs 10.121 iters/sec. Estimated time to finish: 1 day, 3:26:26.372445. The results will be saved in the directory /root2chainer/chainer/examples/dcgan/result/. The image is generated by the generator trained for 1000 epochs, and the GIF image on the top of this page shows generated images after every 10 epochs.class Discriminator(nn.Module): def __init__(self, ngpu): super(Discriminator, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( # input is (nc) x 64 x 64 nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf) x 32 x 32 nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*2) x 16 x 16 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*4) x 8 x 8 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*8) x 4 x 4 nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False), nn.Sigmoid() ) def forward(self, input): return self.main(input) Now, as with the generator, we can create the discriminator, apply the weights_init function, and print the model’s structure.In the MNIST dataset, it will be nice to have a latent variable representing the class of the digit (0-9). Even better, we can have another variable for the digit’s angle and one for the stroke thickness. In GAN, the input of the encoder and the decoder are:""" Discriminator Net model """ D_W1 = tf.Variable(he_init([X_dim + y_dim, h_dim])) D_b1 = tf.Variable(tf.zeros(shape=[h_dim])) D_W2 = tf.Variable(he_init([h_dim, 1])) D_b2 = tf.Variable(tf.zeros(shape=[1])) def discriminator(x, y): inputs = tf.concat(axis=1, values=[x, y]) 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 Cost function for the discriminator and the generator:

- Also see their DCGAN code on GitHub. This post is part of a collaboration between O'Reilly and TensorFlow. See our statement of editorial independence
- ative model, we draw conclusion on something we observe. For example, we train a CNN discri
- ator architecture
- ator D(s) is a probability that the sample s is generated from the true distribution, it can be expressed as follows:
- DCGAN是将卷积神经网络和对抗网络结合起来的一篇经典论文[19]。 [19]Alec Radford.Unsupervised representation learning with deep convolutional generative adversarial networks

DCGAN 论文还展现了其他的技巧，还有对 DCGAN 训练的调节方法，比如用批归一化（batch normalization）或 leaky 图片来源：DCGAN paper. 而且你也可以在 z 输入空间中进行向量算法 We can apply Gradient descent to optimize both the generator and the discriminator. However, training GANs requires finding a Nash equilibrium of a non-convex game. Using Gradient descent to seek for a Nash equilibrium may fail: the solution may not converge. i.e. A modification to may reduce but increase or vice versa. The solution oscillates rather than converges.

By using DCGAN, BF-NSP can be generated and used for oversampling. Figure 5 shows the DCGAN architecture employed in this study, in which we considered the convergence problem of DCGAN At the beginning, are just random noisy images. In DCGAN, we use a second network called a discriminator to guide how images are generated. With the training dataset and the generated images from the generator network, we train the discriminator (just another CNN classifier) to classify whether its input image is real or generated. But simultaneously, for generated images, we backpropagation the score in the discimiantor to the generator network. The purpose is to train the of the generator network so it can generate more realistic images. So the discriminator servers 2 purposes. It determines the fake one from the real one and gives the score of the generated images to the generative model so it can train itself to create more realistic images. By training both networks simultaneously, the discriminator is better in distinguish generated images while the generator tries to narrow the gap between the real image and the generated image. As both improving, the gap between the real and generated one will be diminished. We evaluate the Bayesian DCGAN for semi-supervised learning using Ns = {20, 50, 100, 200} labelled training examples. We see in Table 1 that the Bayesian GAN has improved accuracy over the.. DCGAN. For the first time, convolutional neural networks were used in GAN and impressive results were achieved. Before that,CNNUnprecedented results have been achieved in overseeing computer.. if fix_std: std_contig = tf.ones_like(mean_contig) # We use standard deviation = 1 else: # We use the Q network to predict the SD std_contig = tf.sqrt(tf.exp(out[:, num_categorical + num_continuous:num_categorical + num_continuous * 2])) epsilon = (x - mean) / (std_contig + TINY) loss_q_continous = tf.reduce_sum( - 0.5 * np.log(2 * np.pi) - tf.log(std_contig + TINY) - 0.5 * tf.square(epsilon), reduction_indices=1, ) Mode collapse Mode collapse is when the generator maps several different input z values to the same output. Rather than converging to a distribution containing all of the modes in a training set, the generator produces only one mode at a time even they can cycle through to each others.

X_dim = mnist.train.images.shape[1] # x (image) dimension y_dim = mnist.train.labels.shape[1] # y dimensions = label dimension = 10 Z_dim = 100 # z (latent variables) dimension X = tf.placeholder(tf.float32, shape=[None, X_dim]) # (-1, 784) y = tf.placeholder(tf.float32, shape=[None, y_dim]) # (-1, 10) y: Use a one-hot vector for label Z = tf.placeholder(tf.float32, shape=[None, Z_dim]) # (-1, 100) z Create the generator and discriminator TensorFlow operations. and .This notebook demonstrates this process on the MNIST dataset. The following animation shows a series of images produced by the generator as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.def sample_Z(batch_size, z_dim): return np.random.uniform(-1., 1., size=[batch_size, z_dim]) def sample_c(batch_size): return np.random.multinomial(1, 10 * [0.1], size=batch_size) X_data, _ = mnist.train.next_batch(batch_size) Z_noise = sample_Z(batch_size, Z_dim) c_noise = sample_c(batch_size) _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_data, Z: Z_noise, c: c_noise}) _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: Z_noise, c: c_noise}) sess.run([Q_solver], feed_dict={Z: Z_noise, c: c_noise}) The full source code is in here which is modified from wiseodd. Renae Radford Compositing Artist. Creative Power. Learn how Red Giant gives digital artists the tools they need for incredible visual storytelling

Finally, we will do some statistic reporting and at the end of each epoch we will push our fixed_noise batch through the generator to visually track the progress of G’s training. The training statistics reported are: In Radford's DCGAN paper, they use all the convolutional layers of the To evaluate the quality of the representations learned by DCGANs for supervised tasks, we train on Imagenet-1k and then use the.. Now, if the decision boundary for the original class Dog is not that far (in terms of L2 norm), this additive noise puts the new image outside of the decision boundary. ^ Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi (2016) This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). The code is written using the Keras Sequential API with a tf.GradientTape training loop.

DCGAN Radford et al. Generated Results From The ImageNet Dataset. 200GB -> 10MB. Goodfellow et el. (2014). DCGAN (2015) discriminator = make_discriminator_model() decision = discriminator(generated_image) print (decision) tf.Tensor([[-0.0002661]], shape=(1, 1), dtype=float32) Define the loss and optimizers Define loss functions and optimizers for both models.Define network connections in the __call__ operator by using the chainer.links’s instances and chainer.functions.Sometimes it’s better to perform more than one step for Discriminator per every step of a Generator, so if your Generator starts “winning” in terms of a loss function, consider doing this.

Implementing Deep Convolutional Generative Adversarial Networks (DCGAN) towardsdatascience.com The input to the model is a 100-Dimensional vector (100 random numbers). We randomly select input vectors, says , and create images using multiple layers of transpose convolutions (CONV 1, … CONV 4).def make_generator_model(): model = tf.keras.Sequential() model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,))) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Reshape((7, 7, 256))) assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False)) assert model.output_shape == (None, 7, 7, 128) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False)) assert model.output_shape == (None, 14, 14, 64) model.add(layers.BatchNormalization()) model.add(layers.LeakyReLU()) model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh')) assert model.output_shape == (None, 28, 28, 1) return model Use the (as yet untrained) generator to create an image.

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- Download scientific diagram | DCGAN (Radford 15) interpolation pairs with identical endpoints and uniform prior. In each pair, the top series is linear interpolation and the bottom is spherical

X = tf.placeholder(tf.float32, shape=[None, 784]) Z = tf.placeholder(tf.float32, shape=[None, 16]) c = tf.placeholder(tf.float32, shape=[None, 10]) G_sample = generator(Z, c) D_real = discriminator(X) D_fake = discriminator(G_sample) Generator operationsAt the beginning of the training, the generated images look like random noise. As training progresses, the generated digits will look increasingly real. After about 50 epochs, they resemble MNIST digits. This may take about one minute / epoch with the default settings on Colab.The discriminator processes each datapoint independently and there is no mechanism to encourage the generator to create more diversify images (the generator may collapse to very similar output). In minibatch discrimination, we add information about its co-relationship with other images as input to the discriminator.When training the networks, we should match the distribution of the samples s∼p(s) generated from the true distribution with the distribution of the samples s=G(z) generated from the generator.*As you can see in the class definition, DCGANUpdater inherits StandardUpdater*. In this case, almost all necessary functions are defined in StandardUpdater, we just override the functions of __init__ and update_core.

We often compare these GAN networks as a counterfeiter (generator) and a bank (discriminator). Currency are labeled as real or counterfeit to train the bank in identifying fake money. However, the same training signal for the bank is repurposed for training the counterfeiter to print better counterfeit. If done correctly, we can lock both parties into competition that eventually the counterfeit is undistinguishable from real money.Okay, so, you are saying we can easily fool a network by adding random noise. What does it have to do with generating new images?**def z_sampler(self, dim1): return np**.random.normal(-1, 1, size=[dim1, self.z_dim]) def c_cat_sampler(self, dim1): return np.random.multinomial(1, [0.1] * self.c_cat, size=dim1) batch_xs, _ = self.mnist.train.next_batch(self.batch_size) feed_dict = {self.X: batch_xs, \ self.z: self.z_sampler(self.batch_size), \ self.c_i: self.c_cat_sampler(self.batch_size), \ self.training: True} ... _, D_loss = self.sess.run([self.D_optim, self.D_loss], feed_dict=feed_dict) _, G_loss = self.sess.run([self.G_optim, self.G_loss], feed_dict=feed_dict) _, Q_loss = self.sess.run([self.Q_optim, self.Q_loss], feed_dict=feed_dict) The full source code is in here which is modified from Kim.There is an example of DCGAN in the official repository of Chainer, so we will explain how to implement DCGAN based on this: chainer/examples/dcgan

By Lynn Radford. e-Gro. Water Management It’s helpful to look at some of the IS and FID scores that have been reported to get a feel for what good/bad scores look like and see how different models compare. These scores are for unsupervised models on CIFAR-10:3

from __future__ import print_function #%matplotlib inline import argparse import os import random import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils as vutils import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from IPython.display import HTML # Set random seed for reproducibility manualSeed = 999 #manualSeed = random.randint(1, 10000) # use if you want new results print("Random Seed: ", manualSeed) random.seed(manualSeed) torch.manual_seed(manualSeed) Out:It’s essential to process training and generated minibatches separately and compute the batch norms for different batches individually – going that ensures fast initial training of the Discriminator.

$ pwd/root2chainer/chainer/examples/dcgan$ python train_dcgan.py --gpu 0 GPU: 0# Minibatch-size: 50# n_hidden: 100# epoch: 1000epoch iteration gen/loss dis/loss ................] 0.01%0 100 1.2292 1.76914 total [..................................................] 0.02%this epoch [#########.........................................] 19.00% 190 iter, 0 epoch / 1000 epochs 10.121 iters/sec. Estimated time to finish: 1 day, 3:26:26.372445.The results will be saved in the director /root2chainer/chainer/examples/dcgan/result/. The image is generated by the generator trained with 1000 epochs, and the GIF image on the top of this page shows generated images at the each 10 epoch.You don’t need to be a world class topologist to understand manifolds or decision boundaries of certain classes. As each image is just a vector in a high-dimensional space, a classifier trained on them defines “all monkeys” as “all image vectors in this high-dimensional blob that is described by hidden parameters”. We refer to that blob as to the decision boundary for the class.When defining update_core, we may want to manipulate the underlying array of a Variable with numpy or cupy library. Note that the type of arrays on CPU is numpy.ndarray, while the type of arrays on GPU is cupy.ndarray. However, users do not need to write if condition explicitly, because the appropriate array module can be obtained by xp = chainer.backend.get_array_module(variable.array). If variable is on GPU, cupy is assigned to xp, otherwise numpy is assigned to xp.

- def generator(z, y): # Concatenate z and y as input inputs = tf.concat(axis=1, values=[z, y]) G_h1 = tf.nn.relu(tf.matmul(inputs, G_W1) + G_b1) ... The code for the generator:
- 2DJS===DKL(p(x)∣∣∣∣pˉ(x))+DKL(pmodel(x)∣∣∣∣pˉ(x))𝔼p(x)log2p(x)p(x)+pmodel(x)+𝔼pmodellog2pmodel(x)p(x)+pmodel(x)𝔼p(x)logD(x)+𝔼pmodellog(1−D(x))+log4
- ibatch of data. If the sample batch is small, it generates un-expected side effects to the generated images. For example, if one batch of training data has green-tinted while another batch of training data has yellow-tinted area. We see the generator starts create image with both yellow and green-tinted area.
- We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for..
- ationmodel in classification problem [1]. However, when we talk about generativemodel in GAN, it is natural to define it as a model of probability distributionthat generates training data.2. The difference among GAN and other generative modelsAs explained in the GAN tutorial in NIPS 2016 [2], the generative models can be classified into the categories as shown in the following figure:
- generator = make_generator_model() noise = tf.random.normal([1, 100]) generated_image = generator(noise, training=False) plt.imshow(generated_image[0, :, :, 0], cmap='gray') <matplotlib.image.AxesImage at 0x7f8a2c5c3550>
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- What is DCGAN? DCGAN stands for Deep Convolutional Generative Adversarial Networks, it was proposed by Alec Radford et al. in late 2015. To explain this model intuitively, I'll borrow a simple..
- ator.
- In this section, we will introduce the model called DCGAN(Deep Convolutional GAN) proposed by Radford et al.[6]. As shown below, it is a model using CNN(Convolutional Neural Network) as its name suggests.
- DCGAN successfully combined a convolutional architecture with GAN and was able to produce images with compelling quality. Architecture of Boundary Equilibrium GAN [1], extending the original DCGAN
- where and are the 2048-dimensional activations of the Inception-v3 pool3 layer for real and generated samples respectively.2
- ishes for MiniMax game and Maximum likelihood game. The generator cannot learn. For Maximum likelihood game, most gradient drops are on the right region. It has very high variance in the cost with most cost gradient co

Bradford White designs, engineers & builds water heating, space heating, combination heating and storage solutions for residential, commercial, & industrial applications * DCGAN trained without stabiliza-tion techniques exhibited generator loss that slowly increased over the course of training (Figure 3)*. This furthermore resulted in poor quality generated images

- DCGAN results. Generated bedrooms from reference implementation Notice repetition artifacts (analysis). DCGAN results. Interpolation between different points in the z space
- ator.
- for t in xrange(num_iterations): out_feats, cache = model.forward(X, end=layer) dout = 2 * (out_feats - target_feats) # Manuually override the gradient by the difference of features value. dX, grads = model.backward(dout, cache) dX += 2 * l2_reg * np.sum(X**2, axis=0) X -= learning_rate * dX # Use Gradient descent to change the image We turn around a CNN network to generate realistic images through backpropagation by exaggerate certain features.
- [5] Alec Radford, Luke Metz, Soumith Chintala (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434
- snapshot_interval = (args.snapshot_interval, 'iteration') display_interval = (args.display_interval, 'iteration') trainer.extend( extensions.snapshot(filename='snapshot_iter_{.updater.iteration}.npz'), trigger=snapshot_interval) trainer.extend(extensions.snapshot_object( gen, 'gen_iter_{.updater.iteration}.npz'), trigger=snapshot_interval) trainer.extend(extensions.snapshot_object( dis, 'dis_iter_{.updater.iteration}.npz'), trigger=snapshot_interval) trainer.extend(extensions.LogReport(trigger=display_interval)) trainer.extend(extensions.PrintReport([ 'epoch', 'iteration', 'gen/loss', 'dis/loss', ]), trigger=display_interval) trainer.extend(extensions.ProgressBar(update_interval=10)) trainer.extend( out_generated_image( gen, dis, 10, 10, args.seed, args.out), trigger=snapshot_interval)This Gist brought to you by gist-it.view rawexamples/dcgan/train_dcgan.py

There’s an old but brilliant mathematical result (Minimax theorem) that started the game theory as we know it and states that for two players in a zero-sum game the minimax solution is the same as the Nash equilibrium.theta_G = [G_W1, G_W2, G_b1, G_b2] theta_Q = [Q_W1, Q_W2, Q_b1, Q_b2] theta_Q = [Q_W1, Q_W2, Q_b1, Q_b2] D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D) G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G) Q_solver = tf.train.AdamOptimizer().minimize(Q_loss, var_list=theta_G + theta_Q) Training:During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.

**plt**.figure(figsize=(10,5)) **plt**.title("Generator and Discriminator Loss During Training") **plt**.plot(G_losses,label="G") **plt**.plot(D_losses,label="D") **plt**.xlabel("iterations") **plt**.ylabel("Loss") **plt**.legend() **plt**.show() Visualization of G’s progression Noel Radford - born December 24, 1970. Sue Radford - born March 22, 1975 (age 45). Categories: Radford Family Wiki. Community content is available under CC-BY-SA unless otherwise noted In CGAN’s MNIST, we read the labels of the images and explicitly pass it into the generator and discriminator as . DCGAN architecture has four convolutional layers for the Discriminator and four fractionally-strided convolutional layers for the Generator. The Discriminator is a 4-layer strided convolutions with batch..

The most remarkable thing about DCGAN was that this architecture was stable in most settings. It was one of the first papers to show the vector arithmetics as an intrinsic property of the representations learned by the Generator: it’s the same trick as with the word vectors in Word2Vec, but with images!#%%capture fig = plt.figure(figsize=(8,8)) plt.axis("off") ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list] ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True) HTML(ani.to_jshtml()) Real Images vs. Fake Imagesanim_file = 'dcgan.gif' with imageio.get_writer(anim_file, mode='I') as writer: filenames = glob.glob('image*.png') filenames = sorted(filenames) last = -1 for i,filename in enumerate(filenames): frame = 2*(i**0.5) if round(frame) > round(last): last = frame else: continue image = imageio.imread(filename) writer.append_data(image) image = imageio.imread(filename) writer.append_data(image) import IPython if IPython.version_info > (6,2,0,''): display.Image(filename=anim_file) If you're working in Colab you can download the animation with the code below:

To illustrate this, we consider a minimax game between Paul and Mary. Paul controls the value of and win the game if is the minimum. Mary controls the value of and win the game if is the maximum. The Nash equilibrium defines as a state which all players will not change their strategy regardless of opponent decisions. In this game, the Nash equilibrium is . When , Paul will not change the value of regardless of how Mary set . (or vice versa) (1, 1) is not a Nash equilibrium. If , Paul will change to negative to win.In CGAN, we explicitly define (the class, the digit’s angle, stroke thickness etc …) as an additional input to the encoder and the decoderimport tensorflow as tf tf.__version__ '2.1.0' # To generate GIFs !pip install -q imageio import glob import imageio import matplotlib.pyplot as plt import numpy as np import os import PIL from tensorflow.keras import layers import time from IPython import display Load and prepare the dataset You will use the MNIST dataset to train the generator and the discriminator. The generator will generate handwritten digits resembling the MNIST data.Note: This step might take a while, depending on how many epochs you run and if you removed some data from the dataset.Besides GAN, other famous generative models include Fully visible belief networks (FVBNs) and Variational autoencoder (VAE). Unlike FVBNs and VAE, GAN do not explicitly model the probability distribution \(p({\bf s})\) that generates training data. Instead, we model a generator \(G: {\bf z} \mapsto {\bf s}\). The generator \(G\) samples \({\bf s} \sim p({\bf s})\) from the latent variable \({\bf z}\). Apart from the generator \(G\), we create a discriminator \(D({\bf x})\) which discriminates between samples from the generator G and examples from training data. While training the discriminator \(D\), the generator \(G\) tries to maximize the probability of the discriminator \(D\) making a mistake. So, the generator \(G\) tries to create samples that seem to be drawn from the same distribution as the training data.

def generator(z): g_bn0 = batch_norm(name='g_bn0') g_bn1 = batch_norm(name='g_bn1') g_bn2 = batch_norm(name='g_bn2') g_bn3 = batch_norm(name='g_bn3') z2 = linear(z, DIM * 8 * 4 * 4, scope='g_h0') h0 = tf.nn.relu(g_bn0(tf.reshape(z2, [-1, 4, 4, DIM * 8]))) h1 = tf.nn.relu(g_bn1(conv_transpose(h0, [batchsize, 8, 8, DIM * 4], name="g_h1"))) h2 = tf.nn.relu(g_bn2(conv_transpose(h1, [batchsize, 16, 16, DIM * 2], name="g_h2"))) h3 = tf.nn.relu(g_bn3(conv_transpose(h2, [batchsize, 32, 32, DIM * 1], name="g_h3"))) h4 = conv_transpose(h3, [batchsize, 64, 64, 3], name="g_h4") return tf.nn.tanh(h4) We build a placeholder for image to the discriminator, and a placeholder for . We build 1 generator and initializes 2 discriminators. But both discriminators share the same trainable parameters so they are actually the same. However, with 2 instances, we can separate the scores (logits) for the real and the generated images by feed real images to 1 discriminator and generated images to another. Thanks to Jekyll and Agency Theme, soumith@dcgan.torch and jcjohnson@neural-style Introducing DCGAN Dogs Images. Keras DCGAN with Weight Normalization Deep Convolutional GAN (DCGAN): DCGAN is one of the most popular also the most successful implementation of GAN. It is composed of ConvNets in place of multi-layer perceptrons Remember how we saved the generator’s output on the fixed_noise batch after every epoch of training. Now, we can visualize the training progression of G with an animation. Press the play button to start the animation.

- DCGAN in R. Then, we will join them together. We want to create DCGAN for satellite imagery where the generator network will take random noise as input and will return the new image as an output
- Because the first argument of L.Deconvolution is the channel size of input and the second is the channel size of output, we can find that each layer halves the channel size. When we construct Generator with ch=1024, the network is same as the above image.
- ..New Mexico, US Quebec Queen Anne's, Maryland, US Queens, New York, US Queensland Quitman, Georgia, US Quitman, Mississippi, US Rabun, Georgia, US Racine, Wisconsin, US Radford, Virginia..
- In addition, although GAN is known for its difficulty in learning, this paper introduces various techniques for successful learning:
- As we can see from the initializer __init__, the Generator uses deconvolution layers Deconvolution2D and batch normalization layers BatchNormalization. In __call__, each layer is called and followed by relu except the last layer.

The generator uses tf.keras.layers.Conv2DTranspose (upsampling) layers to produce an image from a seed (random noise). Start with a Dense layer that takes this seed as input, then upsample several times until you reach the desired image size of 28x28x1. Notice the tf.keras.layers.LeakyReLU activation for each layer, except the output layer which uses tanh.** Original DCGAN uses transposed convolution**. But what about residual layers? Not sure that would get you anything in a DCGAN setting going from z to G(z) checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir)) <tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f89c41bfba8> Create a GIF # Display a single image using the epoch number def display_image(epoch_no): return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no)) display_image(EPOCHS)

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- imize the dissimilarity between the two distributions. It is common to use Jensen-Shannon Divergence \(D_{\mathrm{JS}}\) to measure the dissimilarity between distributions[3].
- /path/to/celeba -> img_align_celeba -> 188242.jpg -> 173822.jpg -> 284702.jpg -> 537394.jpg ... This is an important step because we will be using the ImageFolder dataset class, which requires there to be subdirectories in the dataset’s root folder. Now, we can create the dataset, create the dataloader, set the device to run on, and finally visualize some of the training data.
- In this tutorial, we generate images with generative adversarial networks (GAN). GAN are kinds of deep neural network for generative modeling that are often applied to image generation. GAN-based models are also used in PaintsChainer, an automatic colorization service.
- DCGAN uses ReLUs or leaky ReLUs except for the output of the generator. Makes sense - what if half of your embedding becomes zeros? Might be better to have a smoothly varying embedding between..
- DCGAN [Alec Radford, Luke Metz, Soumith Chintala: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial You can also continue with dcgan assignment
- This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN)

This tutorial will give an introduction to DCGANs through an example. We will train a generative adversarial network (GAN) to generate new celebrities after showing it pictures of many real celebrities. Most of the code here is from the dcgan implementation in pytorch/examples, and this document will give a thorough explanation of the implementation and shed light on how and why this model works. But don’t worry, no prior knowledge of GANs is required, but it may require a first-timer to spend some time reasoning about what is actually happening under the hood. Also, for the sake of time it will help to have a GPU, or two. Lets start from the beginning. This won't be the first time in history that cities and buildings will be reimagined in response to an increased understanding of disease..

- The deep convolutional GAN (DCGAN) Radford2016 () is a class of architectures of GANs based on convolutional neural networks (CNNs) mostly used for image generation tasks
- Before we get to describing GANs in details, let’s take a look at a similar topic. Given a trained classifier, can we generate a sample that would fool the network? And if we do, how would it look like?
- In this article, we gave an explanation of Generative Adversarial Networks along with practical tips for implementation and training. In the resources section, you will find the implementations of GANs which will help you to start your experiments.
- DCGANs were introduced in the paper titled Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks by Alec Radford, Luke Metz, Soumith Chintala

For many years this problem has been tackled by various generative models. They used different assumptions, often too strong to be practical, to model the underlying distribution of the data.GANs are a framework for teaching a DL model to capture the training data’s distribution so we can generate new data from that same distribution. GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets. They are made of two distinct models, a generator and a discriminator. The job of the generator is to spawn ‘fake’ images that look like the training images. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator. During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. The equilibrium of this game is when the generator is generating perfect fakes that look as if they came directly from the training data, and the discriminator is left to always guess at 50% confidence that the generator output is real or fake.

A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples Ben Radford / Staff / Global Look Press

The added cost help the gradient descent to find the equilibria of some low-dimensional, continuous non-convex games.Let’s add another network that will learn to generate fake images that Discriminator would misclassify as “genuine.” The procedure will be exactly like the one we have used in Adversarial examples part. This network is called Generator, and the process of adversarial training gives it fascinating properties.

Superunion is a next-generation brand agency built on a spirit of creative optimism Unsupervised representation learning with deep convolutional generative adversarial networks. A **Radford**, L Metz, S Chintala We do not need to define loss_dis and loss_gen because the functions are called only in update_core. It aims at improving readability.

Gavin Courtie & Liz Radford. Gayle Ellett. John Radford try: from google.colab import files except ImportError: pass else: files.download(anim_file) Next steps This tutorial has shown the complete code necessary to write and train a GAN. As a next step, you might like to experiment with a different dataset, for example the Large-scale Celeb Faces Attributes (CelebA) dataset available on Kaggle. To learn more about GANs we recommend the NIPS 2016 Tutorial: Generative Adversarial Networks.Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator ("the artist") learns to create images that look real, while a discriminator ("the art critic") learns to tell real images apart from fakes. ^ Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; **Radford**, Alec; Chen, Xi (2016)

When I was studying generative models the first time I couldn’t help but wonder – why bother with them when we have so many real life training examples already? The answer was quite compelling, here are just a few of possible applications that call for a good generative model:When it comes to practice, especially in Machine Learning, many things just stop working. Luckily, we gather some useful tips for achieving better results. In this article, we will start with reviewing some classic architectures and providing links to them.DCGAN is the simplest stable go-to model that we recommend to start with. We will add useful tips for training/implementation along with links to code examples further.So, what happens now? Let’s say your model can generate all kinds of animals, but you are really fond of cats. Instead of passing generated noise into the Generator and hoping for the best, you add a few labels to the second input, for example as the id of a class cat or just as word vectors. In this case, the Generator is said to be conditioned on the class of expected input.

Let’s retrieve the CIFAR-10 dataset by using Chainer’s dataset utility functionchainer.datasets.get_cifar10. CIFAR-10 is a set of small natural images. Each example is an RGB color image of size 32x32. In the original images, each component of pixels is represented by one-byte unsigned integer. This function scales the components to floating point values in the interval [0, scale]. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv Preprint ArXiv:1511.06434 DCGAN Architecture. Most deconvs are batch normalized. (Radford et al 2015). (Made by Goodfellow edited by Azizpour). DCGANs for LSUN Bedrooms

We can model the discriminator as a classification problem with one data feed coming from real images while another data feed from the generator. The cost function determines how well that can classify real and computer generated images. We want the probability to be 1 for real image and 0 for computer generated image.In addition, although GAN are known for its difficulty in training, this paper introduces various techniques for successful training:In this section, we will introduce the model called DCGAN(Deep Convolutional GAN) proposed by Radford et al.[5]. As shown below, it is a model using CNN(Convolutional Neural Network) as its name suggests.Even more – for virtually any given image classifier it’s possible to morph an image into another, which would be misclassified with high confidence while being visually indistinguishable from the original! Such process is called an adversarial attack, and the simplicity of the generating method explains quite a lot about GANs. An adversarial example in an example carefully computed with the purpose to be misclassified. Here is an illustration of this process. The panda on the left in indistinguishable from the one on the right – and yet it’s classified as a gibbon.

Generative models work in the opposite direction. We starts with some latent representations of the image and generate the image from these variables. For example, we start with some latent variables and generate a room picture using a deep network. 要志愿地撰写或者审核译文，请加入 docs-zh-cn@tensorflow.org Google Group。 本教程演示了如何使用深度卷积生成对抗网络（DCGAN）生成手写数字图片。 该代码是使用 Keras.. The results were suboptimal for most of the tasks we have now. Text generated with Hidden Markov Models was very dull and predictable, images from Variational Autoencoders were blurry and, despite the name, lacked variety. All those shortcomings called for an entirely new approach, and recently such method was invented.For the generator’s notation, let \(z\) be a latent space vector sampled from a standard normal distribution. \(G(z)\) represents the generator function which maps the latent vector \(z\) to data-space. The goal of \(G\) is to estimate the distribution that the training data comes from (\(p_{data}\)) so it can generate fake samples from that estimated distribution (\(p_g\)).

Asian Gay TV provides LGBT movies from more than 20 countries, with watch-online, no-download required experience. By signing up for an Asian Gay TV account you can have access to multiple.. Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, ICLR 2016 Deep Convolutional GAN(DCGAN) Generator Architecture Key idea.. [34] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015

In simpler terms, when two players (D and G) are competing against each other (zero-sum game), and both play optimally assuming that their opponent is optimally (minimax strategy), the outcome is predetermined and none of the players can change it (Nash equilibrium). Go further than you ever imagined in a new Ford vehicle, built just for you. See our full lineup

In 2014, Ian Goodfellow and his colleagues from University of Montreal introduced Generative Adversarial Networks (GANs). It was a novel method of learning an underlying distribution of the data that allowed generating artificial objects that looked strikingly similar to those from the real life. DCGAN能改进GAN训练稳定的原因主要有： ◆ 使用步长卷积代替上采样层，卷积在提取图像特征上具有很好的作用，并且使用卷积代替全连接层

The first animation is the a convolution of a 3x3 filter on a 4x4 input. The second animation is the corresponding transpose convolution of a 3x3 filter on a 2x2 input. Animation source: DCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (github). DE-GAN — Generative Adversarial Networks with Decoder-Encoder Output Noise

The advantages of GAN are low sampling cost and its state-of-the-art performance in image generation. The disadvantage is that we cannot calculate the likelihood \(p_{\mathrm {model}}({\bf s})\) because we do not model any probability distribution, and we cannot infer the latent variable \({\bf z}\) from a sample.h_dim = 128 """ Generator Net model """ G_W1 = tf.Variable(he_init([Z_dim + y_dim, h_dim])) G_b1 = tf.Variable(tf.zeros(shape=[h_dim])) G_W2 = tf.Variable(he_init([h_dim, X_dim])) G_b2 = tf.Variable(tf.zeros(shape=[X_dim])) def generator(z, y): # Concatenate z and y as input 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 The code for the discriminator: Below is my implementation on top of Pytorch's dcgan example (BN class starts at line 103). Although this implementation is very crude, it seems to work well when tested with this example

The discriminator (dashed blue line) estimates . Whenever the discriminator’s output is high, pmodel(x)pmodel(x) is too low, and whenever the the discriminator’s output is small, the model density is too high. The generator can produce a better model by following the discriminator uphill. i.e. Move G(z) value slightly in the direction that increases D(G(z)).d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits( logits=D_logit, labels=tf.ones_like(D_logit))) d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits( logits=D_fake_logit, labels=tf.zeros_like(D_fake_logit))) d_loss = d_loss_real + d_loss_fake We compute the lost function for the generator by using the logits of the generated images from the discriminator. Then, we backpropagate the gradient to train the such that it can later create more realistic images.

G_sample = generator(Z, y) D_real, D_logit_real = discriminator(X, y) D_fake, D_logit_fake = discriminator(G_sample, y) Concatenate and as input to the generator . Code for the generator: Joel Nothman | Nicky Ringland | Will Radford | Tara Murphy | James R. Curran The FID is supposed to improve on the IS by actually comparing the statistics of generated samples to real samples, instead of evaluating generated samples in a vacuum.1 (Heusel, Ramsauer, Unterthiner, Nessler, & Hochreiter, 2017) propose using the Fréchet distance between two multivariate Gaussians,

class DCGAN_D(nn.Module) It is well worth going back and looking at the DCGAN paper to see what these architectures are because it's assumed that when you read the Wasserstein GAN paper.. checkpoint_dir = './training_checkpoints' checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer, discriminator_optimizer=discriminator_optimizer, generator=generator, discriminator=discriminator) Define the training loop EPOCHS = 50 noise_dim = 100 num_examples_to_generate = 16 # We will reuse this seed overtime (so it's easier) # to visualize progress in the animated GIF) seed = tf.random.normal([num_examples_to_generate, noise_dim]) The training loop begins with generator receiving a random seed as input. That seed is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator.Then, when we match the distributions of the samples s∼p(s) generated from true distribution and the samples s∼pmodel(s) generated from the generator G, it means that we should minimize the dissimilarity between the two distributions. It is common to use Jensen-Shannon Divergence DJS to measure the dissimilarity between the distributions[4].

As an intuitive example, the relationship between counterfeiters of banknotes and police is frequently used. The counterfeiters try to make counterfeit notes that are similar to real banknotes. The police try to distinguish real bank notes from counterfeit notes. It is supposed that the ability of the police gradually rises, so that real banknotes and counterfeit notes can be recognized well. Then, the counterfeiters will not be able to use counterfeit banknotes, so they will build more similar counterfeit banknotes to real. As the police improve the skill further, so that they can distinguish real and counterfeit notes… Eventually, the counterfeiter will be able to produce as similar banknote as genuine.In the first place, we often just want to sample s∼p(s) according to the distribution in practice. The likelihood p(s) is used only for model training. In the case, we sometimes do not model the probability distribution p(s)directly, but other targets to facilitate sampling.This extension of a GAN meta architecture was proposed to improve the quality of generated images, and you would be 100% right to call it just a smart trick. The idea is that if you have labels for some data points, you can use them to help the network build salient representations. It doesn’t matter what architecture you use – the extension is the same every time. All you need to do is to add another input to the Generator.FVBNs decomposes the probability distribution p(s) into one-dimensional probability distributions using the Bayes’ theorem as shown in the following equation:The idea behind the GANs is very straightforward. Two networks — a Generator and a Discriminator play a game against each other. The objective of the Generator is to produce an object, say, a picture of a person, that would look like a real one. The goal of the Discriminator is to be able to tell the difference between generated and real images.

cross_ent = tf.reduce_mean(-tf.reduce_sum(tf.log(Q_c_given_x + 1e-8) * c, 1)) ent = tf.reduce_mean(-tf.reduce_sum(tf.log(c + 1e-8) * c, 1)) Q_loss = cross_ent + ent And the optimizer:# Root directory for dataset dataroot = "data/celeba" # Number of workers for dataloader workers = 2 # Batch size during training batch_size = 128 # Spatial size of training images. All images will be resized to this # size using a transformer. image_size = 64 # Number of channels in the training images. For color images this is 3 nc = 3 # Size of z latent vector (i.e. size of generator input) nz = 100 # Size of feature maps in generator ngf = 64 # Size of feature maps in discriminator ndf = 64 # Number of training epochs num_epochs = 5 # Learning rate for optimizers lr = 0.0002 # Beta1 hyperparam for Adam optimizers beta1 = 0.5 # Number of GPUs available. Use 0 for CPU mode. ngpu = 1 Data¶ In this tutorial we will use the Celeb-A Faces dataset which can be downloaded at the linked site, or in Google Drive. The dataset will download as a file named img_align_celeba.zip. Once downloaded, create a directory named celeba and extract the zip file into that directory. Then, set the dataroot input for this notebook to the celeba directory you just created. The resulting directory structure should be:n_sample = 16 Z_sample = sample_Z(n_sample, Z_dim) y_sample = np.zeros(shape=[n_sample, y_dim]) y_sample[:, 7] = 1 # Only generate the digit 7 samples = sess.run(G_sample, feed_dict={Z: Z_sample, y:y_sample}) Here is the model generated “7”:G_W1 = tf.Variable(he_init([26, 256])) G_b1 = tf.Variable(tf.zeros(shape=[256])) G_W2 = tf.Variable(he_init([256, 784])) G_b2 = tf.Variable(tf.zeros(shape=[784])) theta_G = [G_W1, G_W2, G_b1, G_b2] def generator(z, c): """ :param z: (-1, 16) :param c: (-1, 10) """ inputs = tf.concat(axis=1, values=[z, c]) 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 Discriminator operations Cassie Nightingale, Middleton's favorite enchantress, and her young-teenage daughter Grace, who shares that same special intuition as her mom, welcome Dr. Sam Radford and his son to town

Although Radford et al. (2015) provide a class of empirical architectural choices that are critical to stabilize GAN's training, it would be even better to train GANs more robustly and systematically be the feature vector of datapoint in the intermediate layer of the discriminator. is another tensor to train. ROCK ANTENNE - das ist der beste Rock nonstop mit den größten Rocksongs aller Zeiten. Dazu spannende Aktionen und ständig die neuesten Nachrichten aus der Welt des Rock

So, \(D(G(z))\) is the probability (scalar) that the output of the generator \(G\) is a real image. As described in Goodfellow’s paper, \(D\) and \(G\) play a minimax game in which \(D\) tries to maximize the probability it correctly classifies reals and fakes (\(logD(x)\)), and \(G\) tries to minimize the probability that \(D\) will predict its outputs are fake (\(log(1-D(G(x)))\)). From the paper, the GAN loss function is 7: DCGAN / Hybrid Models. Use DCGAN when you can. It works! if you cant use DCGANs and no model is stable, use a hybrid model : KL + GAN or VAE + GAN 13.08.2017 · DCGAN in Tensorflow. Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks