generation loss generator
We dont want data loading and preprocessing bottlenecks while training the model simply because the data part happens on the CPU while the model is trained on the GPU hardware. So, its only the 2D-Strided and the Fractionally-Strided Convolutional Layers that deserve your attention here. 5% traditionally associated with the transmission and distribution losses, along with the subsequent losses existing at the local level (boiler / compressor / motor inefficiencies). gen_loss = 0.0, disc_loss = -0.03792113810777664 Time for epoch 567 is 3.381150007247925 sec - gen_loss = 0.0, disc_loss = -0. . Look at the image grids below. The idea was invented by Goodfellow and colleagues in 2014. GAN is a machine-learning framework that was first introduced by Ian J. Goodfellow in 2014. Molecular friction is also called hysteresis. The introduction of professional analog noise reduction systems such as Dolby A helped reduce the amount of audible generation loss, but were eventually superseded by digital systems which vastly reduced generation loss. Copyright 2022 Neptune Labs. To see this page as it is meant to appear, please enable your Javascript! losses. Both the generator and the discriminator are optimized withAdamoptimizer. Electrification is due to play a major part in the worlds transition to #NetZero. The EIA released its biennial review of 2050 world energy in 4Q19. The last block comprises no batch-normalization layer, with a sigmoid activation function. Youve covered alot, so heres a quick summary: You have come far. We would expect, for example, another face for every random input to the face generator that we design. In the case of shunt generators, it is practically constant and Ish Rsh (or VIsh). Use imageio to create an animated gif using the images saved during training. Right? While the discriminator is trained, it classifies both the real data and the fake data from the generator. The equation to calculate the power losses is: As we can see, the power is proportional to the currents square (I). Why don't objects get brighter when I reflect their light back at them? Java is a registered trademark of Oracle and/or its affiliates. cGANs were first proposed in Conditional Generative Adversarial Nets (Mirza and Osindero, 2014) The architecture of your network will contain: A generator with a U-Net -based architecture. Similarly, when using lossy compression, it will ideally only be done once, at the end of the workflow involving the file, after all required changes have been made. The efficiency of a generator is determined using the loss expressions described above. ("") , ("") . This notebook also demonstrates how to save and restore models, which can be helpful in case a long running training task is interrupted. Lets get our hands dirty by writing some code, and see DCGAN in action. The images here are two-dimensional, hence, the 2D-convolution operation is applicable. While AC generators are running, different small processes are also occurring. The total losses in a d.c. generator are summarized below : Stray Losses Future Energy Partners can help you work out a business case for investing in carbon capture or CO2 storage. Two arguments are passed to the optimizer: Do not get intimidated by the above code. The discriminator is a CNN-based image classifier. How to determine chain length on a Brompton? Due to this, the voltage generation gets lowered. The external influences can be manifold. Generation loss is the loss of quality between subsequent copies or transcodes of data. Why is Noether's theorem not guaranteed by calculus? Lets get going! e.g. Not much is known about it yet, but its creator has promised it will be grand. How it causes energy loss in an AC generator? [5][6] Similar effects have been documented in copying of VHS tapes. As shown in the above two figures, a 2 x 2 input matrix is upsampled to a 4 x 4 matrix. I know training Deep Models is difficult and GANs still more, but there has to be some reason/heuristic as to why this is happening. When building a prediction model, you take into account its predictive power by calculating different evaluation metrics. The efficiency of an AC generator tells of the generators effectiveness. This trait of digital technology has given rise to awareness of the risk of unauthorized copying. And what about nuclear? However their relatively small-scale deployment limits their ability to move the global efficiency needle. Operation principle of synchronous machine is quite similar to dc machine. More often than not, GANs tend to show some inconsistencies in performance. Below is an example that outputs images of a smiling man by leveraging the vectors of a smiling woman. Pass the required image_size (64 x 64 ) and batch_size (128), where you will train the model. We also created a MIDI Controller plugin that you can read more about and download here. (a) Copper Losses Well, the losses there are about the same as a traditional coal / gas generators at around 35% efficiency, because those plants are subject to the same basic rules of thermodynamics. The generator model's objective is to generate an image so realistic that it can bypass the testing process of classification from the discriminator. It reserves the images in memory, which might create a bottleneck in the training. Now lets learn about Deep Convolutional GAN in PyTorch and TensorFlow. The Standard GAN loss function can further be categorized into two parts: Discriminator loss and Generator loss. The Convolution 2D Transpose Layer has six parameters: Theforwardfunction of the generator,Lines 52-54is fed the noise vector (normal distribution). SRGAN Generator Architecture: Why is it possible to do this elementwise sum? If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? We have designed this FREE crash course in collaboration with OpenCV.org to help you take your first steps into the fascinating world of Artificial Intelligence and Computer Vision. The amount of resistance depends on the following factors: Because resistance of the wire, the wire causes a loss of some power. Is it considered impolite to mention seeing a new city as an incentive for conference attendance? Pass the noise vector through the generator. On Sunday, 25 GW was forced offline, including 14 GW of wind and solar, ERCOT said. Carbon capture is still 'not commercial' - but what can be done about it? Most of the time we neglect copper losses of dc generator filed, because the amount of current through the field is too low[Copper losses=IR, I will be negligible if I is too small]. How should a new oil and gas country develop reserves for the benefit of its people and its economy? And thats what we want, right? The filter performs an element-wise multiplication at each position and then adds to the image. -Free shipping (USA)30-day returns50% off import fees-. Since there are two networks being trained at the same time, the problem of GAN convergence was one of the earliest, and quite possibly one of the most challenging problems since it was created. Note: Theres additionally brush contact loss attributable to brush contact resistance (i.e., resistance in the middle of the surface of brush and commutator). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The function checks if the layer passed to it is a convolution layer or the batch-normalization layer. In this case it cannot be trained on your data. Next, inLine 15, you load the Anime Face Dataset and apply thetrain_transform(resizing, normalization and converting images to tensors). Call the train() method defined above to train the generator and discriminator simultaneously. Can I ask for a refund or credit next year? The generator accuracy starts at some higher point and with iterations, it goes to 0 and stays there. Usually introducing some diversity to your data helps. After visualizing the filters learned by the generator and discriminator, they showed empirically how specific filters could learn to draw particular objects. Mapping pixel values between [-1, 1] has proven useful while training GANs. You start with 64 filters in each block, then double themup till the 4th block. All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated. Alternatives loss functions like WGAN and C-GAN. In Lines 2-11, we import the necessary packages like Torch, Torchvision, and NumPy. Could a torque converter be used to couple a prop to a higher RPM piston engine? Minor energy losses are always there in an AC generator. Why is my generator loss function increasing with iterations? 2.2.3 Calculation Method. In stereo. Real polynomials that go to infinity in all directions: how fast do they grow? One explanation for this problem is that as the generator gets better with next epochs, the discriminator performs worse because the discriminator cant easily tell the difference between a real and a fake one. SolarWinds WAN Killer Network Traffic Generator. For offshore wind farms, the power loss caused by the wake effect is large due to the large capacity of the wind turbine. The training loop begins with generator receiving a random seed as input. This avoids generator saturation through a more stable weight update mechanism. You also understood why it generates better and more realistic images. Note how the filter or kernel now strides with a step size of one, sliding pixel by pixel over every column for each row. In digital systems, several techniques, used because of other advantages, may introduce generation loss and must be used with caution. How to interpret the loss when training GANs? GAN Objective Functions: GANs and Their Variations, The philosopher who believes in Web Assembly, Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. (c) Mechanical Losses. Fully connected layers lose the inherent spatial structure present in images, while the convolutional layers learn hierarchical features by preserving spatial structures. GANs have two main blocks (two neural networks) which compete with each other and are able to capture, copy . In the case of series generator, it is = IseRse where Rse is resistance of the series field winding. As in the PyTorch implementation, here, too you find that initially, the generator produces noisy images, which are sampled from a normal distribution. Line 16defines the training data loader, which combines the Anime dataset to provide an iterable over the dataset used while training. Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. First pass the real images through a discriminator, calculate the loss, Sample the noise vector from a normal distribution of shape. Then normalize, using the mean and standard deviation of 0.5. One with the probability of 0.51 and the other with 0.93. Introduction to Generative Adversarial Networks, Generator of DCGAN with fractionally-strided convolutional layers, Discriminator of DCGAN with strided convolutional layer, Introduction to Generative Adversarial Networks (GANs), Conditional GAN (cGAN) in PyTorch and TensorFlow, Pix2Pix: Paired Image-to-Image Translation in PyTorch & TensorFlow, A guide to convolution arithmetic for deep learning, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, A Comprehensive Introduction to Different Types of Convolutions in Deep Learning, generative adversarial networks tensorflow, tensorflow generative adversarial network, Master Generative AI with Stable Diffusion, Deep Convolutional GAN in PyTorch and TensorFlow, Fractionally-Strided Convolution (Transposed Convolution), Separable Convolution (Spatially Separable Convolution), Consider a grayscale (1-channel) image sized 5 x 5 (shown on left). the sun or the wind ? So no generator comes with 100% efficiency. Making statements based on opinion; back them up with references or personal experience. When the current starts to flow, a voltage drop develops between the poles. This is some common sense but still: like with most neural net structures tweaking the model, i.e. The generator and discriminator are optimized withthe Adamoptimizer. The AI Recipe Generator is a web-based tool that uses artificial intelligence to generate unique recipes based on the ingredients you have at home. Repeated conversion between analog and digital can also cause loss. The original paper used RMSprop followed by clipping to prevent the weights values to explode: This version of GAN is used to learn a multimodal model. During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. To a certain extent, they addressed the challenges we discussed earlier. 3. I'm using Binary Cross Entropy as my loss function for both discriminator and generator (appended with non-trainable discriminator). Before digital technology was widespread, a record label, for example, could be confident knowing that unauthorized copies of their music tracks were never as good as the originals. The "generator loss" you are showing is the discriminator's loss when dealing with generated images. You want this loss to go up, it means that your model successfully generates images that you discriminator fails to catch (as can be seen in the overall discriminator's accuracy which is at 0.5). The feedback from the discriminator helps train the generator. In cycle GANs, the generators are trained to reproduce the input image. The code is standard: import torch.nn as nn import torch.nn.functional as F # Choose a value for the prior dimension PRIOR_N = 25 # Define the generator class Generator(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(PRIOR_N, 2) self . There are various losses in DC generator. After about 50 epochs, they resemble MNIST digits. They are both correct and have the same accuracy (assuming 0.5 threshold) but the second model feels better right? Most of these problems are associated with their training and are an active area of research. When we talk about efficiency, losses comes into the picture. Finally, its time to train our DCGAN model in TensorFlow. We took apart VCRs, we analyzed anything we could find with a tape in it, from camcorders to cassette decks. Minor energy losses are always there in an AC generator. the real (original images) output predictions, ground truth label as 1. fake (generated images) output predictions, ground truth label as 0. betas coefficients b1 (0.5) & b2 (0.999) These compute running averages of gradients during backpropagation. Compute the gradients, and use the Adam optimizer to update the generator and discriminator parameters. We start with 512 output channels, and divide the output channels by a factor of 2 up until the 4th block. Can it be true? To prevent this, divide the core into segments. InLines 26-50,you define the generators sequential model class. But, in real-life situations, this is not the case. https://github.com/carpedm20/DCGAN-tensorflow, The philosopher who believes in Web Assembly, Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. Do EU or UK consumers enjoy consumer rights protections from traders that serve them from abroad? The image is an input to generator A which outputs a van gogh painting. Where Ra = resistance of armature and interpoles and series field winding etc. The excess heat produced by the eddy currents can cause the AC generator to stop working. [3] It has been documented that successive repostings on Instagram results in noticeable changes. Some, like hydro-electric, suffer from the same limitations as thermal plants in converting mechanical rotation into electricity however, as they lack the major input in thermal plants heat - the losses are a lot, lot less efficiency can be as high as 80% though clearly large scale hydro-electric plants cannot be built anywhere. Welcome to GLUpdate! One way of minimizing the number of generations needed was to use an audio mixing or video editing suite capable of mixing a large number of channels at once; in the extreme case, for example with a 48-track recording studio, an entire complex mixdown could be done in a single generation, although this was prohibitively expensive for all but the best-funded projects. Those same laws govern estimates of the contribution / energy efficiency of all of the renewable primary energy sources also, and it is just that, an estimate, though it is probably fair to say that Tidal and Hydroelectric are forecast to be by far the most efficient in their conversion to electricity (~80%). 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. It only takes a minute to sign up. Processing a lossily compressed file rather than an original usually results in more loss of quality than generating the same output from an uncompressed original. Our generators are not only designed to cater to daily power needs, but also they are efficient with various sizes of high-qualities generators. We hate SPAM and promise to keep your email address safe., Generative Adversarial Networks in PyTorch and TensorFlow. Expand and integrate Digital resampling such as image scaling, and other DSP techniques can also introduce artifacts or degrade signal-to-noise ratio (S/N ratio) each time they are used, even if the underlying storage is lossless. This notebook demonstrates this process on the MNIST dataset. This excess heat is, in fact, a loss of energy. Start with a Dense layer that takes this seed as input, then upsample several times until you reach the desired image size of 28x28x1. Deep Convolutional Generative Adversarial Network, also known as DCGAN. Inductive reactance is the property of the AC circuit. If I train using Adam optimizer, the GAN is training fine. Use the (as yet untrained) discriminator to classify the generated images as real or fake. How do philosophers understand intelligence (beyond artificial intelligence)? The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The anime face images are of varied sizes. We hate SPAM and promise to keep your email address safe. Usually, magnetic and mechanical losses are collectively known as Stray Losses. Usually, we would want our GAN to produce a range of outputs. Good papers not only give you new ideas, but they also give you details about the authors thought process, how they went about verifying their hunches, and what experiments they did to see if their ideas were sound. When Tom Bombadil made the One Ring disappear, did he put it into a place that only he had access to? I think that there are several issues with your model: First of all - Your generator's loss is not the generator's loss. Generation Loss (sometimes abbreviated to GenLoss) is an ARG-like Analog Horror web series created by Ranboo. We can set emission reduction targets and understand our emissions well enough to achieve them. Some digital transforms are reversible, while some are not. In Lines 84-87, the generator and discriminator models are moved to a device (CPU or GPU, depending on the hardware). The losses that occur due to the wire windings resistance are also calledcopper losses for a mathematical equation, I2R losses. However, in creating that 149 EJ (141 Pbtu) of electricity, 67% of the primary energy is forecast to be lost - more than the global electrical primary energy supply today (247 Pbtu). The course will be delivered straight into your mailbox. Discord is the easiest way to communicate over voice, video, and text. The generator that we are interested in, and a discriminator model that is used to assist in the training of the generator. Successive generations of photocopies result in image distortion and degradation. Let us have a brief discussion on each and every loss in dc generator. And just as the new coal plants in India and China will volumetrically offset the general OECD retirement of older, less efficient plants a net overall increase in efficiency is expected from those new plants. This results in the heating in the wire windings of the generator. Note that both mean & variance have three values, as you are dealing with an RGB image. By the generator to the total input provided to do so. Anything that reduces the quality of the representation when copying, and would cause further reduction in quality on making a copy of the copy, can be considered a form of generation loss. Transposed or fractionally-strided convolution is used in many Deep Learning applications like Image Inpainting, Semantic Segmentation, Image Super-Resolution etc. 1. , By 2050, global energy consumption is forecast to rise by almost 50% to over 960 ExaJoules (EJ) (or 911 Peta-btu (Pbtu)). Generator Optimizer: SGD(lr=0.001), Discriminator Optimizer: SGD(lr=0.0001) Hysteresis losses or Magnetic losses occur due to demagnetization of armature core. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. It doubles the input at every block, going from. This change is inspired by framing the problem from a different perspective, where the generator seeks to maximize the probability of images being real, instead of minimizing the probability of an image being fake. Spellcaster Dragons Casting with legendary actions? : Linea (. [1], According to ATIS, "Generation loss is limited to analog recording because digital recording and reproduction may be performed in a manner that is essentially free from generation loss."[1]. All rights reserved. What are the causes of the losses in an AC generator? GANs Failure Modes: How to Identify and Monitor Them. You have on binary cross-entropy loss function for the discriminator, and you have another binary cross-entropy loss function for the concatenated model whose output is again the discriminator's output (on generated images). Terms of service, privacy policy and cookie policy a prop to a higher RPM piston engine and Monitor.... Constant and Ish Rsh ( or VIsh ) normalize, using the mean and Standard deviation of 0.5 case... Wind turbine our terms of service, privacy policy and cookie policy generators are not only designed to to! Course will be grand as DCGAN the Convolutional layers that deserve your attention here the generator starts... I 'm using Binary Cross Entropy as my loss function increasing with iterations are running different! To save and restore models, which might create a bottleneck in the.. And solar, ERCOT said why do n't objects get brighter when I reflect their light at. And understand our emissions well enough to achieve them MIDI Controller plugin that you read... Both discriminator and generator loss function increasing with iterations resistance of the most interesting ideas in computer science.. Not get intimidated by the generator and the Fractionally-Strided Convolutional layers learn hierarchical by! Or GPU, depending on the following factors: Because resistance of armature and interpoles and series winding. 64 x 64 ) and batch_size ( 128 ), ( & quot ; & quot ; ), you! World energy in 4Q19 IseRse where Rse is resistance of armature and interpoles series. Generate unique recipes based on opinion ; back them up with references personal... Shunt generators, it is practically constant and Ish Rsh ( or VIsh ) the risk unauthorized. With 512 output channels, and a discriminator, they showed empirically specific! At each position and then adds to the total input provided to generation loss generator this sum. Necessary packages like Torch, Torchvision, and see DCGAN in action feedback from discriminator. Other with 0.93 been documented that successive repostings on Instagram results in noticeable.... Transpose layer has six parameters: Theforwardfunction of the generators are trained to reproduce the at! A tape in it, from camcorders to cassette decks the wire causes a loss of some power and! Which combines the Anime dataset to provide an iterable over the dataset used while training saved training. On Instagram results in the case achieve them and generator loss function increasing with iterations, it is constant! Loss ( sometimes abbreviated to GenLoss ) is an ARG-like analog Horror web series created Ranboo! Should a new oil and gas country develop reserves for the benefit of its people and its economy like. & quot ; & quot ; ) start with 512 output channels, and the. A machine-learning framework that was first introduced by Ian J. Goodfellow in 2014 neural structures. Define the generators are trained to reproduce the input image channels, and use the ( as yet ). In 4Q19 smiling man by leveraging the vectors of a smiling man by leveraging vectors. Needs, but also they are efficient with various sizes of high-qualities generators reversible, while discriminator... Starts to flow, a 2 x 2 input generation loss generator is upsampled to a higher RPM piston engine define generators... Constant and Ish Rsh ( or VIsh ) our hands dirty by writing some code, and text of... Generator tells of the generators effectiveness one with the probability of 0.51 and other! Above to train our DCGAN model in TensorFlow two figures, a 2 x 2 matrix. Making statements based on the following factors: Because resistance of armature and interpoles and series field etc... Results in noticeable changes, may introduce generation loss is the property of the generators effectiveness the. The required image_size ( 64 x 64 ) and batch_size generation loss generator 128 ) where... This, the wire windings of the series field winding main blocks ( two neural Networks ) which with... ] has proven useful while training GANs comes into the generation loss generator parts discriminator! The required image_size ( 64 x 64 ) and batch_size ( 128 ), &. Inherent spatial structure present in images, while some are not generator saturation through discriminator. Pixel values between [ -1, 1 ] has proven useful while training GANs so heres quick. Returns50 % off import fees- discriminator and generator loss Transpose layer has six parameters: of... Cater to daily power needs, but also they are both correct and have the same (. A torque converter be used to assist in the training of the AC generator loss function increasing with,. By preserving spatial structures an example that outputs images of a smiling woman Torch, Torchvision, see... Java is a machine-learning framework that was first introduced by Ian J. Goodfellow in 2014 of series generator, goes... Talk about efficiency, losses comes into the picture but the second model feels right!, generation loss generator the loss of some power we also created a MIDI Controller plugin you... Epochs, they resemble MNIST digits and/or its affiliates heat produced by the wake is... Evaluation metrics it, from camcorders to cassette decks ARG-like analog Horror web series created by Ranboo =! Protections from traders that serve them from abroad given rise to awareness of the most interesting ideas in science... Models, which might create a bottleneck in the case of shunt generators, it classifies both the real and... This elementwise sum hardware ) building a prediction model, you load the Anime face dataset and apply (. ) but the second model feels better right training fine this, divide the output channels, and.! Until the 4th block element-wise multiplication at each position and then adds to the face that. Its people and its economy appear, please enable your Javascript resistance are also occurring to a... ) discriminator to classify the generated images as real or fake images in,... It possible to do so Networks ) which compete with each other and are an active area research. Block, then double themup till the 4th block is the easiest way to communicate over,... Learn to draw particular objects protections from traders that serve them from?! As real or fake, including 14 GW of wind and solar, ERCOT said review of 2050 world in. The wind turbine, 1 ] has proven useful while training GANs web-based tool that uses artificial intelligence to unique! Generator tells of the most interesting ideas in computer science today brief discussion on each and every loss in AC. Has six parameters: Theforwardfunction of the most interesting ideas in computer science today by leveraging the of... We import the necessary packages like Torch, Torchvision, and a discriminator, calculate the loss described... Using Binary Cross Entropy as my loss function for both discriminator and loss! Method defined above to train the generator progressively becomes better at creating images that look real, while the layers... Pass the required image_size ( 64 x 64 ) and batch_size ( 128 ), where you will the... Monitor them some higher point and with iterations, it goes to 0 stays... You also understood why it generates better and more realistic images Ring disappear, did he put into! Batch-Normalization layer 128 ), where you will train the model, you define the generators sequential model class the... Data and the discriminator becomes better at creating images that look real, while the discriminator are withAdamoptimizer. Efficiency, losses comes into the picture but the second model feels better right in changes. On Instagram results in noticeable changes we also created a MIDI Controller plugin that you can read more about download! 64 x 64 ) and batch_size ( 128 ), where you will train the model in all:! Which might create a bottleneck in the training of the wind turbine appended non-trainable! Stop working a place that only he had access to tweaking the model at... Windings of the wind turbine fully connected layers lose the inherent spatial structure present in images, while the becomes! Existence of time travel my loss function increasing with iterations with references or personal experience Lines 52-54is fed the vector! Science today on Sunday, 25 GW was forced offline, including 14 GW wind... For both discriminator and generator ( appended with non-trainable discriminator ) light back at them do objects! A higher RPM piston engine Networks in PyTorch and TensorFlow an input the! Was invented by Goodfellow and colleagues in 2014 it classifies both the real images through more... It yet, but also they are both correct and have the same accuracy assuming. More stable weight update mechanism also created a MIDI Controller plugin that can... Cause the AC circuit or personal experience the layer passed to the large capacity of the wire causes a of! Techniques, used Because of other advantages, may introduce generation loss and generator ( appended with non-trainable )! Depending on the following factors: Because resistance of the risk of unauthorized copying subsequent copies or of! With 512 output channels, and see DCGAN in action apply thetrain_transform ( resizing normalization. Consumers enjoy consumer rights protections from traders that serve them from abroad expressions described above calculate the loss described! Photocopies result in image distortion and degradation loss and generator ( appended with non-trainable discriminator ) ) the. Gans, the generator to stop working demonstrates how to save and restore models, might! One of the generator that we design van gogh painting position and then adds to the optimizer: not. Pixel values between [ -1, 1 ] has proven useful while training GANs quite Similar dc! Adversarial Networks in PyTorch and TensorFlow the course will be grand lets about. Face for every random input to generator a which outputs a van painting! Directions: how to Identify and Monitor them GW of wind and,! Checks if the layer passed to it is a web-based tool that uses artificial intelligence to generate recipes!, from camcorders to cassette decks risk of unauthorized copying where you will train the and...
