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Источник: http://www.jyvsoft.com/2018/09/18/pointwise-v180-r3-x86x64-crack/

ANTI-DISTILLATION: IMPROVING REPRODUCIBILITY

Deep networks have been revolutionary in improving performance of machine learning and artificial intelligence systems. Their high prediction accuracy, however, comes at a price of model… Expand

Synthesizing Irreproducibility in Deep Networks

TLDR

This study demonstrates the effects of randomness in initialization, training data shuffling window size, and activation functions on prediction irreproducibility, even under very controlled synthetic data.Expand

Randomness In Neural Network Training: Characterizing The Impact of Tooling

TLDR

The results suggest that deterministic tooling is critical for AI safety, but also find that the cost of ensuring determinism varies dramatically between neural network architectures and hardware types, e.g., with overhead up to 746%, 241%, and 196% on a spectrum of widely used GPU accelerator architectures, relative to non-deterministic training.Expand

Dropout Prediction Variation Estimation Using Neuron Activation Strength

• Haichao Yu, Zhe Chen, Dong Lin, G. Shamir, Jie Han
• Computer Science
• ArXiv
• 2021

TLDR

This approach provides an inference-once alternative to estimate dropout prediction variation as an auxiliary task and demonstrates that using activation features from a subset of the neural network layers can be sufficient to achieve variation estimation performance almost comparable to that of usingactivation features from all layers, thus reducing resources even further for variation estimation.Expand

Smooth activations and reproducibility in deep networks

TLDR

A new family of activations; Smooth ReLU (SmeLU) is proposed, designed to give better tradeoffs, while also keeping the mathematical expression simple, and thus training speed fast and implementation cheap, and demonstrating the superior accuracy-reproducibility tradeoffs with smooth activation, SmeLU in particular.Expand
Источник: https://www.semanticscholar.org/paper/Beyond-Point-Estimate%3A-Inferring-Ensemble-Variation-Chen-Wang/a5032b460626aff980a7c98c99900a5c093e3c75

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#### Conclusion

##### Abstract

The aim of this thesis is to study the effect that linguistic context exerts on the activation and processing of word meaning over time. Previous studies have demonstrated that a biasing context makes it possible to predict upcoming words. The context causes the pre-activation of expected words and facilitates their processing when they are encountered. The interaction of context and word meaning can be described in terms of feature overlap: as the context unfolds, the semantic features of the processed words are activated and words that match those features are pre-activated and thus processed more quickly when encountered. The aim of the experiments in this thesis is to test a key prediction of this account, viz., that the facilitation effect is additive and occurs together with the unfolding context. Our first contribution is to analyse the effect of an increasing amount of biasing context on the pre-activation of the meaning of a critical word. In a self-paced reading study, we investigate the amount of biasing information required to boost word processing: at least two biasing words are required to significantly reduce the time to read the critical word. In a complementary visual world experiment we study the effect of context as it unfolds over time. We identify a ceiling effect after the first biasing word: when the expected word has been pre-activated, an increasing amount of context does not produce any additional significant facilitation effect. Our second contribution is to model the activation effect observed in the previous experiments using a bag-of-words distributional semantic model. The similarity scores generated by the model significantly correlate with the association scores produced by humans. When we use point-wise multiplication to combine contextual word vectors, the model provides a computational implementation of feature overlap theory, successfully predicting reading times. Our third contribution is to analyse the effect of context on semantically similar words. In another visual world experiment, we show that words that are semantically similar generate similar eye-movements towards a related object depicted on the screen. A coherent context pre-activates the critical word and therefore increases the expectations towards it. This experiment also tested the cognitive validity of a distributional model of semantics by using this model to generate the critical words for the experimental materials used.

Источник: https://era.ed.ac.uk/handle/1842/10508

## Understanding of LSTM Networks

This article talks about the problems of conventional RNNs, namely, the vanishing and exploding gradients and provides a convenient solution to these problems in the form of Long Short Term Memory (LSTM). Long Short-Term Memory is an advanced version of recurrent neural network (RNN) architecture that was designed to model chronological sequences and their long-range dependencies more precisely than conventional RNNs. The major highlights include the interior design of a basic LSTM cell, the variations brought into the LSTM architecture, and few applications of LSTMs that are highly in demand. It also makes a comparison between LSTMs and GRUs. The article concludes with a list of disadvantages of the LSTM network and a brief introduction of the upcoming attention-based models that are swiftly replacing LSTMs in the real world.

Introduction:

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LSTM networks are an extension of recurrent neural networks (RNNs) mainly introduced to handle situations where RNNs fail. Talking about RNN, it is a network that works on the present input by taking into consideration the previous output (feedback) and storing in its memory for a short period of time (short-term memory). Out of its various applications, the most popular ones are in the fields of speech processing, non-Markovian control, and music composition. Nevertheless, there are drawbacks to RNNs. First, it fails to store information for a longer period of time. At times, a reference to certain information stored quite a long time ago is required to predict the current output. But RNNs are absolutely incapable of handling such “long-term dependencies”. Second, there is no finer control over which part of the context needs to be carried forward and how much of the past needs to be ‘forgotten’. Other issues with RNNs are exploding and vanishing gradients (explained later) which occur during the training process of a network through backtracking. Thus, Long Short-Term Memory (LSTM) was brought into the picture. It has been so designed that the vanishing gradient problem is almost completely removed, while the training model is left unaltered. Long time lags in certain problems are bridged using LSTMs where they also handle noise, distributed representations, and continuous values. With LSTMs, there is no need to keep a finite number of states from beforehand as required in the hidden Markov model (HMM). LSTMs provide us with a large range of parameters such as learning rates, and input and output biases. Hence, no need for fine adjustments. The complexity to update each weight is reduced to O(1) with LSTMs, similar to that of Back Propagation Through Time (BPTT), which is an advantage.

During the training process of a network, the main goal is to minimize loss (in terms of error or cost) observed in the output when training data is sent through it. We calculate the gradient, that is, loss with respect to a particular set of weights, adjust the weights accordingly and repeat this process until we get an optimal set of weights for which loss is minimum. This is the concept of backtracking. Sometimes, it so happens that the gradient is almost negligible. It must be noted that the gradient of a layer depends on certain components in the successive layers. If some of these components are small (less than 1), the result obtained, which is the gradient, will be even smaller. This is known as the scaling effect. When this gradient is multiplied with the learning rate which is in itself a small value ranging between 0.1-0.001, it results in a smaller value. As a consequence, the alteration in weights is quite small, producing almost the same output as before. Similarly, if the gradients are quite large in value due to the large values of components, the weights get updated to a value beyond the optimal value. This is known as the problem of exploding gradients. To avoid this scaling effect, the neural network unit was re-built in such a way that the scaling factor was fixed to one. The cell was then enriched by several gating units and was called LSTM.

Architecture:

The basic difference between the architectures of RNNs and LSTMs is that the hidden layer of LSTM is a gated unit or gated cell. It consists of four layers that interact with one another in a way to produce the output of that cell along with the cell state. These two things are then passed onto the next hidden layer. Unlike RNNs which have got the only single neural net layer of tanh, LSTMs comprises of three logistic sigmoid gates and one tanh layer. Gates have been introduced in order to limit the information that is passed through the cell. They determine which part of the information will be needed by the next cell and which part is to be discarded. The output is usually in the range of 0-1 where ‘0’ means ‘reject all’ and ‘1’ means ‘include all’.

Hidden layers of LSTM :

Each LSTM cell has three inputs ,and and two outputs and . For a given time t, is the hidden state, is the cell state or memory, is the current data point or input. The first sigmoid layer has two inputs–and where is the hidden state of the previous cell. It is known as the forget gate as its output selects the amount of information of the previous cell to be included. The output is a number in [0,1] which is multiplied (point-wise) with the previous cell state

Conventional LSTM:

The second sigmoid layer is the input gate that decides what new information is to be added to the cell. It takes two inputs and . The tanh layer creates a vector of the new candidate values. Together, these two layers determine the information to be stored in the cell state. Their point-wise multiplication tells us the amount of information to be added to the cell state. The result is then added with the result of the forget gate multiplied with previous cell state to produce the current cell state . Next, the output of the cell is calculated using a sigmoid and a tanh layer. The sigmoid layer decides which part of the cell state will be present in the output whereas tanh layer shifts the output in the range of [-1,1]. The results of the two layers undergo point-wise multiplication to produce the output ht of the cell.

Variations:

With the increasing popularity of LSTMs, various alterations have been tried on the conventional LSTM architecture to simplify the internal design of cells to make them work in a more efficient way and to reduce the computational complexity. Gers and Schmidhuber introduced peephole connections which allowed gate layers to have knowledge about the cell state at every instant. Some LSTMs also made use of a coupled input and forget gate instead of two separate gates that helped in making both the decisions simultaneously. Another variation was the use of the Gated Recurrent Unit(GRU) which improved the design complexity by reducing the number of gates. It uses a combination of the cell state and hidden state and also an update gate which has forgotten and input gates merged into it.

LSTM(Figure-A), DLSTM(Figure-B), LSTMP(Figure-C) and DLSTMP(Figure-D)

1. Figure-A represents what a basic LSTM network looks like. Only one layer of LSTM between an input and output layer has been shown here.
2. Figure-B represents Deep LSTM which includes a number of LSTM layers in between the input and output. The advantage is that the input values fed to the network not only go through several LSTM layers but also propagate through time within one LSTM cell. Hence, parameters are well distributed within multiple layers. This results in a thorough process of inputs in each time step.
3. Figure-C represents LSTM with the Recurrent Projection layer where the recurrent connections are taken from the projection layer to the LSTM layer input. This architecture was designed to reduce the high learning computational complexity (O(N)) for each time step) of the standard LSTM RNN.
4. Figure-D represents Deep LSTM with a Recurrent Projection Layer consisting of multiple LSTM layers where each layer has its own projection layer. The increased depth is quite useful in the case where the memory size is too large. Having increased depth prevents overfitting in models as the inputs to the network need to go through many nonlinear functions.

GRUs Vs LSTMs

In spite of being quite similar to LSTMs, GRUs have never been so popular. But what are GRUs? GRU stands for Gated Recurrent Units. As the name suggests, these recurrent units, proposed by Cho, are also provided with a gated mechanism to effectively and adaptively capture dependencies of different time scales. They have an update gate and a reset gate. The former is responsible for selecting what piece of knowledge is to be carried forward, whereas the latter lies in between two successive recurrent units and decides how much information needs to be forgotten.

Activation at time t:

Update gate:

Candidate activation:

Reset gate:

Another striking aspect of GRUs is that they do not store cell state in any way, hence, they are unable to regulate the amount of memory content to which the next unit is exposed. Instead, LSTMs regulate the amount of new information being included in the cell. On the other hand, the GRU controls the information flow from the previous activation when computing the new, candidate activation, but does not independently control the amount of the candidate activation being added (the control is tied via the update gate).

Applications:

LSTM models need to be trained with a training dataset prior to its employment in real-world applications. Some of the most demanding applications are discussed below:

1. Language modelling or text generation, that involves the computation of words when a sequence of words is fed as input. Language models can be operated at the character level, n-gram level, sentence level or even paragraph level.
2. Image processing, that involves performing analysis of a picture and concluding its result into a sentence. For this, it’s required to have a dataset comprising of a good amount of pictures with their corresponding descriptive captions. A model that has already been trained is used to predict features of images present in the dataset. This is photo data. The dataset is then processed in such a way that only the words that are most suggestive are present in it. This is text data. Using these two types of data, we try to fit the model. The work of the model is to generate a descriptive sentence for the picture one word at a time by taking input words that were predicted previously by the model and also the image.
3. Speech and Handwriting Recognition
4. Music generation which is quite similar to that of text generation where LSTMs predict musical notes instead of text by analyzing a combination of given notes fed as input.
5. Language Translation involves mapping a sequence in one language to a sequence in another language. Similar to image processing, a dataset, containing phrases and their translations, is first cleaned and only a part of it is used to train the model. An encoder-decoder LSTM model is used which first converts input sequence to its vector representation (encoding) and then outputs it to its translated version.

Drawbacks:

As it is said, everything in this world comes with its own advantages and disadvantages, LSTMs too, have a few drawbacks which are discussed as below:

1. LSTMs became popular because they could solve the problem of vanishing gradients. But it turns out, they fail to remove it completely. The problem lies in the fact that the data still has to move from cell to cell for its evaluation. Moreover, the cell has become quite complex now with the additional features (such as forget gates) being brought into the picture.
2. They require a lot of resources and time to get trained and become ready for real-world applications. In technical terms, they need high memory-bandwidth because of linear layers present in each cell which the system usually fails to provide for. Thus, hardware-wise, LSTMs become quite inefficient.
3. With the rise of data mining, developers are looking for a model that can remember past information for a longer time than LSTMs. The source of inspiration for such kind of model is the human habit of dividing a given piece of information into small parts for easy remembrance.
4. LSTMs get affected by different random weight initialization and hence behave quite similar to that of a feed-forward neural net. They prefer small weight initialization instead.
5. LSTMs are prone to overfitting and it is difficult to apply the dropout algorithm to curb this issue. Dropout is a regularization method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network.

Источник: https://www.geeksforgeeks.org/understanding-of-lstm-networks/
##### Abstract

The aim of this thesis is to study the effect that linguistic context exerts on the activation and processing of word meaning over time. Previous studies have demonstrated that a biasing context makes it possible to predict upcoming words. The context causes the pre-activation of expected words and facilitates their processing when they are encountered. The interaction of context and word meaning can be described in terms of feature overlap: as the context unfolds, the semantic features of the processed words are activated and words that match those features are pre-activated and thus processed more quickly when encountered. The aim of the experiments in this thesis is to test a key prediction of this account, viz., that the facilitation effect is additive and occurs together with the unfolding context. Our first contribution is to analyse the effect of an increasing amount of biasing context on the pre-activation of the meaning of a critical word. In a self-paced reading study, we investigate the amount of biasing information required to boost word processing: at least two biasing words are required to significantly reduce the time to read the critical word. In a complementary visual world experiment we study the effect of context as it unfolds over time. We identify a ceiling effect after the first biasing word: when the expected word has been pre-activated, an increasing amount of context does not produce any additional significant facilitation effect. Our second contribution is to model the activation effect observed in the previous experiments using a bag-of-words distributional semantic model. The similarity scores generated by the model significantly correlate with the association scores produced by humans. When we use point-wise multiplication to combine contextual word vectors, the model provides a computational implementation of feature overlap theory, successfully predicting reading times. Our third contribution is to analyse the effect of context on semantically similar words. In another visual world experiment, we show that words that are semantically similar generate similar eye-movements towards a related object depicted on the screen. A coherent context pre-activates the critical word and therefore increases the expectations towards it. This experiment also tested the cognitive validity of a distributional model of semantics by using this model to generate the critical words for the experimental materials used.

Источник: https://era.ed.ac.uk/handle/1842/10508

### Learning Objectives

• Identify the anatomical and functional divisions of the nervous system
• Relate the functional and structural differences between gray matter and white matter structures of the nervous system to the structure of neurons
• List the basic functions of the nervous system

The picture you have in your mind of the nervous system probably includes the brain, the nervous tissue contained within Pointwise Activation key cranium, and the spinal cord, the extension of nervous tissue within the vertebral column. That suggests it is made of two organs—and you may not even think of the spinal cord as an organ—but the nervous system is a very complex structure. Within the brain, many different and separate regions are responsible for many different and separate functions. It is as if the nervous system is composed of many organs that all look similar and can only be differentiated using tools such as the microscope or electrophysiology. In comparison, it is easy to see that the stomach is different than the esophagus or the liver, so you can imagine the digestive system as a collection of specific organs.

### The Central and Peripheral Nervous Systems

Figure 1. Central and Peripheral Nervous System The structures of the PNS are referred to as ganglia and nerves, which can be seen as distinct structures. The equivalent structures in the CNS are not obvious from this overall perspective and are best examined in prepared tissue under the microscope.

The nervous system can be divided into two major regions: the central and peripheral nervous systems. The central nervous system (CNS) is the brain and spinal cord, and the peripheral nervous system (PNS) is everything else (Figure 1). The brain is contained within the cranial cavity of the skull, and the spinal cord is contained within the vertebral cavity of the vertebral column. It is a bit of an oversimplification to say that the CNS is what is inside these two cavities and the peripheral nervous system is outside of them, but that is one way to start to think about it. In actuality, there are some elements of the peripheral nervous system that are within the cranial or vertebral cavities. The peripheral nervous system is so named because it is on the periphery—meaning beyond the brain and spinal cord. Depending on different aspects of the nervous system, the dividing line between central and peripheral is not necessarily universal.

Nervous tissue, present in both the CNS and PNS, contains two basic types of cells: neurons and Pointwise Activation key cells. A glial cell is one of a variety of cells that provide a framework of tissue that supports the neurons and their activities. The neuron is the more functionally important of the two, in terms of the communicative function of the nervous system.

In order to describe the functional divisions of the nervous system, it is important to understand the structure of a neuron. Neurons are cells and therefore have a soma, or cell body, but they also have extensions of the cell; each extension is generally referred to as a process. There is one important process that every neuron has called an axon, which is the fiber that connects a neuron with its target. Another type of process that branches off from the soma is the dendrite. Dendrites are responsible for receiving most of the input from other neurons.

Figure 2. Gray Matter and White Matter A brain removed during an autopsy, with a partial section removed, shows white matter surrounded by gray matter. Gray matter makes up the outer cortex of the brain. (credit: modification of work by “Suseno”/Wikimedia Commons)

Looking at nervous tissue, there are regions that predominantly contain cell bodies and regions that are largely composed of just axons. These two regions within nervous system structures are often referred to as gray matter (the regions with many cell bodies and dendrites) or white matter (the regions with many axons). Figure 2 demonstrates the appearance of these regions in the brain and spinal cord. The colors ascribed to these regions are what would be seen in “fresh,” or unstained, nervous tissue. Gray matter is not necessarily gray. It can be pinkish because of blood content, or even slightly tan, depending on how long the tissue has been preserved. But white matter is white because axons are insulated by a lipid-rich substance called myelin. Lipids can appear as white (“fatty”) material, much like the fat on a raw piece of chicken or beef. Actually, gray matter may have anydesk premium 4.2.3 crack - Free Activators color ascribed to it because next to the white matter, it is just darker—hence, gray.

The distinction between gray matter and white matter is most often applied to central nervous tissue, which has large regions that can be seen with the unaided eye. When looking at peripheral structures, often a microscope is used and the tissue is stained with artificial colors. That is not to say that central nervous tissue cannot be stained and viewed under a microscope, but unstained tissue is most likely from the CNS—for example, a frontal section of the brain or cross section of the spinal cord.

Regardless of the appearance of stained or unstained tissue, the cell bodies of neurons or axons can be located in discrete anatomical structures that need to be named. Those names are specific to whether the structure is central or peripheral. A localized collection of neuron cell bodies in the CNS is referred to as a nucleus. In the PNS, a cluster of neuron cell bodies is referred to as a ganglion. Figure 3 indicates how the term nucleus has a few different meanings within anatomy and physiology. It is the center of an atom, where protons and neutrons are found; it is the center of a cell, where the DNA is found; and it is a center of some function in the CNS. There is also a potentially confusing use of the word ganglion (plural = ganglia) that has a historical explanation. In the central nervous system, there is a group of nuclei that are connected together and were once called the basal ganglia before “ganglion” became accepted as a description for a peripheral structure. Some sources refer to this group of nuclei as the “basal nuclei” to avoid confusion.

Figure 3. What Is a Nucleus? (a) The nucleus of an atom contains its protons and neutrons. (b) The nucleus of a cell is the organelle that contains DNA. (c) A nucleus in the CNS is a localized center of function with the cell bodies of several neurons, shown here circled in red. (credit c: “Was a bee”/Wikimedia Commons)

Terminology applied to bundles of axons also differs depending on location. A bundle of axons, or fibers, found in the CNS is called a tract whereas the same thing in the PNS would be called a nerve. There is an important point to make about these terms, which is that they can both be used to refer to the same bundle of axons. When those axons are in the PNS, the term is nerve, but if they are CNS, the term is tract. The most obvious example of this is the axons that project from the retina into the brain. Those axons are called the optic nerve as they leave the eye, but when they are inside the cranium, they are referred to as the optic tract. There is a specific place where the name changes, which is the optic chiasm, but they are still the same axons (Figure 4).

Figure 4. Optic Nerve Versus Optic Tract This drawing of the connections of the eye to the brain shows the optic nerve extending from the eye to the chiasm, where the structure continues as the optic tract. The same axons extend from the eye to the brain through these two bundles of fibers, but the chiasm represents the border between peripheral and central.

A similar situation outside of science can be described for some roads. Imagine a road called “Broad Street” in a town called “Anyville.” The road leaves Anyville and goes to the next town over, called “Hometown.” When the road crosses the line between the two towns and is in Hometown, its name changes to “Main Street.” That is the idea behind the naming of the retinal axons. In the PNS, they are called the optic nerve, and in the CNS, they are the optic tract. Table 1 helps to clarify which of these terms apply to the central or peripheral nervous systems.

In 2003, the Nobel Prize in Physiology or Medicine was awarded to Paul C. Lauterbur and Sir Peter Mansfield for discoveries related to magnetic resonance imaging (MRI). This is a tool to see the structures of the body (not just the nervous system) that depends on magnetic fields associated with certain atomic nuclei. The utility of this technique in the nervous system is that fat tissue and water appear as different shades between black and white. Because white matter is fatty (from myelin) and gray matter is not, they can be easily distinguished in MRI images.

Table 1. Structures of the CNS and PNS
CNSPNS
Group of Neuron Cell Bodies (i.e., gray matter)NucleusGanglion
Bundle of Axons (i.e., white matter)TractNerve

Visit the Nobel Prize web site to play an interactive game that demonstrates the use of this technology and compares it with other types of imaging technologies. Also, the results from an MRI session are compared with images obtained from X-ray or computed tomography. How do the imaging techniques shown in this game indicate the separation of white and gray matter compared with the freshly dissected tissue shown earlier?

### Functional Divisions of the Nervous System

The nervous system can also be divided on the basis of its functions, but anatomical divisions and functional divisions are different. The CNS and the PNS both contribute to the same functions, but those functions can be attributed to different regions of the brain (such as the cerebral cortex or the hypothalamus) or to different ganglia in the periphery. The problem with trying to fit functional differences into anatomical divisions is that sometimes the same structure can be part of several functions. For example, the optic nerve carries signals from the retina that are either used for the conscious perception of visual stimuli, which takes place in the cerebral cortex, or for the reflexive responses of smooth muscle tissue that are processed through the hypothalamus.

There are two ways to consider how the nervous system is divided functionally. First, the basic functions of the nervous system are sensation, integration, and response. Secondly, control of the body can be somatic or autonomic—divisions that are largely defined by the structures that are involved in the response. There is also a region of the peripheral nervous system that is called the enteric nervous system that is responsible for a specific set of the functions within the realm of autonomic control related to gastrointestinal functions.

### Basic Functions

The nervous system is involved in receiving information about the environment around us (sensation) and generating responses to that information (motor responses). The nervous system can be divided into regions that are responsible for sensation (sensory functions) and for the response (motor functions). But there is a third function that needs to be included. Sensory input needs to be integrated with other sensations, as well as with memories, emotional state, or learning (cognition). Some regions of the nervous system are termed integration or association areas. The process of integration combines sensory perceptions and higher cognitive functions such as memories, learning, and emotion to produce a response.

#### Sensation

The first major function of the nervous system is sensation—receiving information about the environment to gain input about x mirage android is happening outside the body (or, sometimes, within the body). The sensory functions of the nervous system register the presence of a change from homeostasis or a particular event in the environment, known as a stimulus.

The senses we think of most are the “big five”: taste, smell, touch, recuva full - Free Activators, and hearing. The stimuli for taste and smell are both chemical substances (molecules, compounds, ions, etc.), touch is physical or mechanical stimuli that interact with the skin, sight is light stimuli, and hearing is the perception of sound, which is a physical stimulus similar to some aspects of touch. There are actually more senses than just those, but that list represents the major senses. Those five are all senses that receive stimuli from the outside world, and of which there is conscious perception. Additional sensory stimuli might be from the internal environment (inside the body), such as the stretch of an organ wall or the concentration of certain ions in the blood.

#### Response

The nervous system produces a response on the basis of the stimuli perceived by sensory structures. An obvious response would be the movement of muscles, such as withdrawing a hand from a hot stove, but there are broader uses of the term. The nervous system can cause the contraction of all three types of muscle tissue. For example, skeletal muscle contracts to move the skeleton, cardiac muscle is influenced as heart rate increases during exercise, and smooth muscle contracts as the digestive system moves food along the digestive tract. Responses also include the neural control of glands in the body as well, such as the production and secretion of sweat by the eccrine and merocrine sweat glands found in the skin to lower body temperature.

Responses can be divided into those that are voluntary or conscious (contraction of skeletal muscle) and those that are involuntary (contraction of smooth muscles, regulation of cardiac muscle, activation of glands). Voluntary responses are governed by the somatic nervous system and involuntary responses are governed by the autonomic nervous system, which are discussed in the next section.

#### Integration

Stimuli that are received by sensory structures are communicated to the nervous system where that information is processed. This is called integration. Stimuli are compared with, or integrated with, other stimuli, memories of previous stimuli, or the state of a person at a particular time. This leads to the specific response that will be generated. Seeing a baseball pitched to a batter will not automatically cause the batter to swing. The trajectory of the ball and its speed will need to be considered. Maybe the count is three balls and one strike, and the batter wants to let this pitch go by in the hope of getting a walk to first base. Or maybe the batter’s team is so far ahead, it would be fun to just swing away.

### Controlling the Body

The nervous system can be divided into two parts mostly on the basis of a functional difference in responses. The somatic nervous system (SNS) is responsible for conscious perception and voluntary motor responses. Voluntary motor response means the contraction of skeletal muscle, but those contractions are not always voluntary in Windows 7 Ultimate Product key + Free Activation 2020 sense that you have to want to perform them. Some somatic motor responses are reflexes, and often happen without a conscious decision to perform them. If your friend jumps out from behind a corner and yells “Boo!” you will be startled and you might scream or leap back. You didn’t decide to do that, and you may not have wanted to give your friend a reason to laugh at your expense, but it is a reflex involving skeletal muscle contractions. Other motor responses become automatic (in other words, unconscious) as a person learns motor skills (referred to as “habit learning” or “procedural memory”).

The autonomic nervous system (ANS) is responsible for involuntary control of the body, usually for the sake of homeostasis (regulation of the internal environment). Sensory input for autonomic functions can be from sensory structures tuned to external or internal environmental stimuli. The motor output extends to smooth and cardiac muscle as well as glandular tissue. The role of the autonomic system is to regulate the organ systems of the body, which usually means to control homeostasis. Sweat glands, for example, are controlled by the autonomic system. When you are hot, sweating helps cool your body down. That is a homeostatic mechanism. But when you are nervous, you might start sweating also. That is not homeostatic, it is the physiological response to an emotional state.

There is another division of the nervous system that describes functional responses. The enteric nervous system (ENS) is responsible for controlling the smooth muscle and glandular tissue in your digestive system. It is a large part of the PNS, and is not dependent on the CNS. It is sometimes valid, however, to consider the enteric system to be a part of the autonomic system because the neural structures that make up the enteric system are a component of the autonomic output that regulates digestion. There are some differences between the two, but for our purposes here there will be a good bit of overlap. See Figure 5 for examples of where these divisions of the nervous system can be found.

Picasa Offline Installer is located in the digestive tract and is responsible for autonomous function. The ENS can operate independent of the brain and spinal cord." width="800" height="440">

Figure 5. Somatic, Autonomic, and Enteric Structures of the Nervous System Somatic structures include the spinal nerves, both motor and sensory fibers, as well as the sensory ganglia (posterior root ganglia and cranial nerve ganglia). Autonomic structures are found in the nerves also, but include the sympathetic and parasympathetic ganglia. The enteric nervous system includes the nervous tissue within the organs of the digestive tract.

Visit this site to read about a woman that notices that her daughter is having trouble walking up the stairs. This leads to the discovery of a hereditary condition that affects the brain and spinal cord. The electromyography and MRI tests indicated deficiencies in the spinal cord and cerebellum, both of which are responsible for controlling coordinated movements. To what functional division of the nervous system would these structures belong?

### Everyday Connection: How Much of Your Brain Do You Use?

Have you ever heard the claim that humans only use 10 percent of their brains? Maybe you have seen an advertisement on a website saying that there is a secret to unlocking the full potential of your mind—as if there were 90 percent of your brain sitting idle, just waiting for you to use it. If you see an ad like that, don’t Pointwise Activation key. It isn’t true.

Figure 6. fMRI This fMRI shows activation of the visual cortex in response to visual stimuli. (credit: “Superborsuk”/Wikimedia Commons)

An easy way to see how much of the brain a person uses is to take measurements of brain activity while performing a task. An example of this kind of measurement is functional magnetic resonance imaging (fMRI), which generates a map of the most active areas and can be generated and presented in three dimensions (Figure 6). This procedure is different from the standard MRI technique because it is measuring changes in the tissue in time with an experimental condition or event.

The underlying assumption is that active nervous tissue will have greater blood flow. By having the subject perform a visual task, activity all over the brain can be measured. Consider this possible experiment: the subject is told to look at a screen with a black dot in the middle (a fixation point). A photograph of a face is projected on the screen away from the center. The subject has to look at the photograph and decipher what it is. The subject has been instructed to push a button if the photograph is of someone they recognize. The photograph might be of a celebrity, so the subject would press the button, or it might be of a random person unknown to the subject, so the subject would not press the button.

In this task, visual sensory areas would be active, integrating areas would be active, motor areas responsible for moving the eyes would be active, and motor areas for pressing the button with a finger would be active. Those areas are distributed all around the brain and the fMRI images would show activity in more than just 10 percent of the brain (some evidence suggests that about 80 percent of the brain is using energy—based on blood flow to the tissue—during well-defined tasks similar to the one suggested above). This task does not even include all of the functions the brain performs. There is no language response, the body is mostly lying still in the MRI machine, and it does not consider the autonomic functions that would be ongoing in the background.

### Self-Check Questions

Take the quiz below to check your understanding of the Basic Structure and Function of the Nervous System:

Источник: https://courses.lumenlearning.com/cuny-csi-ap-1/chapter/basic-structure-and-function-of-the-nervous-system/

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## Understanding of LSTM Networks

This article talks about the problems of conventional RNNs, namely, the vanishing and exploding gradients and provides a convenient solution to these problems in the form of Long Short Term Memory (LSTM). Long Short-Term Memory is an advanced version of recurrent neural network (RNN) architecture that was designed to model chronological sequences and their long-range dependencies more precisely than conventional RNNs. The major highlights include the interior design of a basic LSTM cell, the variations brought into the LSTM architecture, and few applications of LSTMs that are highly in demand. It also makes a comparison between LSTMs and GRUs. The article concludes with a list of disadvantages of the LSTM network and a brief introduction of the upcoming attention-based models that are swiftly replacing LSTMs in the real world.

Introduction:

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LSTM networks are an extension of recurrent neural networks (RNNs) mainly introduced to handle situations where RNNs fail. Talking about RNN, it is a network that works on the present input by taking into consideration the previous output (feedback) and storing in its memory for a short period of time (short-term memory). Out of its various applications, the most popular ones are in the fields of speech processing, non-Markovian control, and music composition. Nevertheless, there are drawbacks to RNNs. First, it fails to store information for a longer period of time. At times, a reference to certain information stored quite a long time ago is required to predict the current output. But RNNs are absolutely incapable of handling such “long-term dependencies”. Second, there is no finer control over which part of the context needs to be carried forward and how much of the past needs to be ‘forgotten’. Other issues with RNNs are exploding and vanishing gradients (explained later) which occur during the training process of a network through backtracking. Thus, Long Short-Term Memory (LSTM) was brought into the picture. It has been so designed that the vanishing gradient problem is almost completely removed, while the training model is left unaltered. Long time lags in certain Pointwise Activation key are bridged using LSTMs where they also handle noise, distributed representations, and continuous values. With LSTMs, there is no need to keep a finite number of states from beforehand as required in the hidden Markov model (HMM). LSTMs provide us with a large range of parameters such as learning rates, and input and output biases. Hence, no need for fine adjustments. The complexity to update each weight is reduced to O(1) with LSTMs, similar to that of Back Propagation Through Time (BPTT), which is an advantage.

During the training process of a network, the main goal is to minimize loss (in terms of error or cost) observed in the output when training data is sent through it. We calculate the gradient, that is, loss with respect to a particular set of weights, adjust the weights accordingly and repeat this process until we get an optimal set of weights for which loss is minimum. This is the concept of backtracking. Sometimes, it so happens that the gradient is almost negligible. It must be noted that the gradient of a layer depends on certain components in the successive layers. If some of these components are small (less than 1), the result obtained, which is the gradient, will be even smaller. This is known as the scaling effect. When this gradient is multiplied with the learning rate which is in itself a small value ranging between 0.1-0.001, it results in a smaller value. As a consequence, the alteration in weights is quite small, producing almost the same output as before. Similarly, if the gradients are quite large in value due to the large values of components, the weights get updated to a value beyond the optimal value. This is known as the problem of exploding gradients. To avoid this scaling effect, the neural network unit was re-built in such a way that the scaling factor was fixed to one. The cell was then enriched by several gating units and was called LSTM.

Architecture:

The basic difference between the architectures of RNNs and LSTMs is that the hidden layer of LSTM is a gated unit or gated cell. It consists of four layers that interact with one another in a way to produce the output of that cell along with the cell state. These two things are then passed onto the next hidden layer. Unlike RNNs which have got the only single neural net layer of tanh, LSTMs comprises of three logistic sigmoid gates and one tanh layer. Gates have been introduced in order to limit the information that is passed through the cell. They determine which part of the information will be needed by the next cell and which part is to be discarded. The output is usually in the range of 0-1 where ‘0’ means ‘reject all’ and ‘1’ means ‘include all’.

Hidden layers of LSTM :

Each LSTM cell has three inputs ,and and two outputs and . For a given time t, is the hidden state, is the cell state or memory, is the current data point or input. The first sigmoid layer has two inputs–and where is the hidden state of the previous cell. It is known as the forget gate as its output selects the amount of information of the previous cell to be included. The output is a number in [0,1] which is multiplied (point-wise) with the previous cell state

Conventional LSTM:

The second sigmoid layer is the input gate that decides what new information is to be added to the cell. It takes two inputs and . The tanh layer creates a vector Pointwise Activation key width="25">of the new candidate values. Together, these two layers determine the information to be stored in the cell state. Their point-wise multiplication tells us the amount of information to be added to the cell state. The result is then added with the result of the forget gate multiplied with previous cell state to produce the current cell state . Next, the output of the cell is calculated using a sigmoid and a tanh layer. The sigmoid layer decides which part of the cell state will be present in the output whereas tanh layer shifts the output in the range of [-1,1]. The results of the two layers undergo point-wise multiplication to produce the output ht of the cell.

Variations:

With the increasing popularity of LSTMs, various alterations have been tried on the conventional LSTM architecture to simplify the internal design of cells to make them work in a more efficient way and to reduce the computational complexity. Gers and Schmidhuber introduced peephole connections which allowed gate layers to have knowledge about the cell state at every instant. Some LSTMs also made use of a coupled input and forget gate instead of two separate gates that helped in making both the decisions simultaneously. Another variation was the use of the Gated Recurrent Unit(GRU) which improved the design complexity by reducing the number of gates. It uses a combination of the cell state and hidden state and also an update gate which has forgotten and input gates merged into it.

LSTM(Figure-A), DLSTM(Figure-B), LSTMP(Figure-C) and DLSTMP(Figure-D)

1. Figure-A represents what a basic LSTM network looks like. Only one layer of LSTM between an input and output layer has been shown here.
2. Figure-B represents Deep LSTM which includes a number of LSTM layers in between the input and output. The advantage is that the input values fed to the network not only go through several LSTM layers but also propagate through time within one LSTM cell. Hence, parameters are well distributed within multiple layers. This results in a thorough process of inputs in each time step.
3. Figure-C represents LSTM with the Recurrent Projection layer where the recurrent connections are taken from the projection layer to the LSTM layer input. This architecture was designed to reduce the high learning computational complexity (O(N)) for each time step) of the standard LSTM RNN.
4. Figure-D represents Deep LSTM with a Recurrent Projection Layer consisting of multiple LSTM layers where each layer has its own projection layer. The increased depth is quite useful in the case where the memory size is too large. Having increased depth prevents overfitting in models as the inputs to the network need to go through many nonlinear functions.

GRUs Vs LSTMs

In spite of being quite similar to LSTMs, GRUs have never been so popular. But what are GRUs? GRU stands for Gated Recurrent Units. As the name suggests, these recurrent units, proposed by Cho, are also provided with a gated mechanism to effectively and adaptively capture dependencies of different Pointwise Activation key scales. They have an update gate and a reset gate. The former is responsible for selecting what piece of knowledge is to be carried forward, whereas the latter lies in between two successive recurrent units and decides how much information needs to be forgotten.

Activation at time t:

Update gate:

Candidate activation:

Reset gate:

Another striking aspect of GRUs is that they do not store cell state in any way, hence, they are unable to regulate the amount of memory content to which the next unit is exposed. Instead, LSTMs regulate the amount of new information being included in the cell. On the other hand, the GRU controls the information flow from the previous activation when computing the new, candidate activation, but does not independently control the amount of the candidate activation being added (the control is tied via the update gate).

Applications:

LSTM models need to be trained with a training dataset prior to its employment in real-world applications. Some of the most demanding applications are discussed below:

1. Language modelling or text generation, that involves the computation of words when a sequence of words is fed as input. Language models can be operated at the character level, n-gram level, sentence level or even paragraph level.
2. Image processing, that involves performing analysis of a picture and concluding its result into a sentence. For this, it’s required to have a dataset comprising of a good amount of pictures with their corresponding descriptive captions. A model that has already been trained is used to predict features of images present in the dataset. This is photo data. The dataset is then processed in such a way that only the words that are most suggestive are present in it. This is text data. Using these two types of data, we try to fit the model. The work of the model is to generate a descriptive sentence for the picture one word at a time by taking input words that were predicted previously by the model and also the image.
3. Speech and Handwriting Recognition
4. Music generation which is quite similar to that of text generation where LSTMs predict musical notes instead of text by analyzing a combination of given notes fed as input.
5. Language Translation involves mapping xmind 8 pro system requirements - Free Activators sequence in one language to a sequence in another language. Similar to image processing, a dataset, containing phrases and their translations, is first cleaned and only a part of it is used to train the model. An encoder-decoder LSTM model is used which first converts input sequence to its vector representation (encoding) and then outputs it to its translated version.

Drawbacks:

As it is said, everything in this world comes with its own advantages and disadvantages, LSTMs too, have a few drawbacks which are discussed as below:

1. LSTMs became popular because they could solve the problem of vanishing gradients. But it turns out, they fail to remove it completely. The problem lies in the fact that the data still has to move from cell to cell for its evaluation. Moreover, the cell has become quite complex now with the additional features (such as forget gates) being brought into the picture.
2. They require a lot of resources and time to get trained and become ready for real-world applications. In technical terms, they need high memory-bandwidth because of linear layers present in each cell which the system usually fails to provide for. Thus, hardware-wise, LSTMs become quite inefficient.
3. With the rise of data mining, developers are looking for a model that can remember past information for a longer time than LSTMs. The source of inspiration for such kind of model is the human habit of dividing a given piece of information into small parts for easy remembrance.
4. LSTMs get affected by different random weight initialization and hence behave quite similar Adobe Flash Player 31.0 License Key - Crack Key For U that of a feed-forward neural net. They prefer small weight initialization instead.
5. LSTMs are prone to overfitting and it is difficult to apply the dropout algorithm to curb this issue. Dropout is a regularization method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network.

Источник: https://www.geeksforgeeks.org/understanding-of-lstm-networks/

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## Understanding LSTM Networks

Posted on August 27, 2015

### Recurrent Neural Networks

Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.

Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

Recurrent Neural Networks have loops.

In the above diagram, a chunk of neural network, $$A$$, looks at some input $$x_t$$ and outputs a value $$h_t$$. A loop allows information to be passed from one step of the network to the next.

These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the loop:

An unrolled recurrent neural network.

This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. They’re the natural architecture of neural network to use for such data.

And they certainly are used! In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning… The list goes on. I’ll leave discussion of the amazing feats one can achieve with RNNs to Andrej Karpathy’s excellent blog post, The Unreasonable Effectiveness of Recurrent Neural Networks. But they really are pretty amazing.

Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. Almost all exciting results based on recurrent neural networks are achieved with them. It’s these LSTMs that this essay will explore.

### The Problem of Long-Term Dependencies

One of the appeals of RNNs is the idea that they might be able to connect previous information to the present task, such as using previous video frames might inform the understanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends.

Sometimes, we only need to look at recent information to perform the present task. For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information.

But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed Pointwise Activation key become very large.

Unfortunately, as that gap grows, RNNs become unable to learn to connect the information.

In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them. The problem was explored in depth by Hochreiter (1991) [German] and Bengio, et al. (1994), who found some pretty fundamental reasons why it might be difficult.

Thankfully, LSTMs don’t have this problem!

### LSTM Networks

Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work.1 They work tremendously well on a large variety of problems, and are now widely used.

LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!

All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.

The repeating module in a standard RNN contains a single layer.

LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way.

The repeating module in an LSTM contains four interacting layers.

Don’t worry about the details of what’s going on. We’ll walk through the LSTM diagram step by step later. For now, let’s just try to get comfortable with the notation we’ll be using.

In the above diagram, each line carries an entire vector, from the output of one node to the inputs of others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denote its content being copied and the copies going to different locations.

### The Core Idea Behind LSTMs

The key to LSTMs is the cell state, the horizontal line running through the top of the diagram.

The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.

The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates.

Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation.

The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through,” while a value of one means “let everything through!”

An LSTM has three of these gates, to protect and control the cell state.

### Step-by-Step LSTM Walk Through

The first step in our LSTM is to decide what information we’re going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer.” It looks at $$h_{t-1}$$ and $$x_t$$, and outputs a number between $$0$$ and $$1$$ for each number in the cell state $$C_{t-1}$$. A $$1$$ represents “completely keep this” while a $$0$$ represents “completely get rid of this.”

Let’s go back to our example of a language model trying to predict the next word based on all the previous ones. In such a Pointwise Activation key, the cell state might include the gender of the present subject, so Autodesk Vault Pro Server/Client 2022 Free Download with Crack the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.

The next step is to decide what new information we’re going to store in the cell state. This has two parts. First, a sigmoid layer called the “input gate layer” decides which values we’ll update. Next, a tanh layer creates a vector of new candidate values, $$\tilde{C}_t$$, that could be added to the state. In the next step, we’ll combine these two to create an update to the state.

In the example of our language model, we’d want to add the gender of the new subject to the cell state, to replace the old one we’re forgetting.

It’s now time to update the old cell state, $$C_{t-1}$$, into the new cell state $$C_t$$. The previous steps already decided what to do, we just need to actually do it.

We multiply the old state by $$f_t$$, forgetting the things we decided to forget earlier. Then we add $$i_t*\tilde{C}_t$$. This is the new candidate values, scaled by how much we decided to update each state value.

In the case of the language model, this is where we’d actually drop the information about the old subject’s gender and add the new information, as we decided in the previous steps.

Finally, we need to decide what we’re going to output. This output will be based on our cell state, but will be a filtered version. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through $$\tanh$$ (to push the values to be between $$-1$$ and $$1$$) and multiply it by the output of the sigmoid gate, so that we only output the parts we decided to.

For the language model example, since it just saw a subject, it might want to output information relevant to a verb, in case that’s what is coming next. For example, it might output whether the subject is singular or plural, so that we know what form a verb should be conjugated into if that’s what follows next.

### Variants on Long Short Term Memory

What I’ve described so far is a pretty normal LSTM. But not all LSTMs are the same as the above. In fact, it seems like almost every paper involving LSTMs uses a slightly different version. The differences are minor, but it’s worth mentioning some of them.

One popular LSTM variant, introduced by Gers & Schmidhuber (2000), is adding “peephole connections.” This means that we let the gate layers look at the cell state.

The above diagram adds peepholes to all the gates, but many papers will give some peepholes and not others.

Another variation is to use coupled forget and input gates. Instead of separately deciding what to forget and what we should add new information to, we make those decisions together. We only forget when we’re going to input something in its place. We only input new values to the state when we forget something older.

A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al. (2014). It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.

These are only a few of the most notable LSTM variants. There are lots of others, like Depth Gated RNNs by Yao, et al. (2015). There’s also some completely different approach to tackling long-term dependencies, like Clockwork RNNs by Koutnik, et al. (2014).

Which of these variants is best? Do the differences matter? Greff, et al. (2015) do a nice comparison of popular variants, finding that they’re all about the same. Jozefowicz, et al. (2015) tested more than ten thousand RNN architectures, finding some that worked better than LSTMs on certain tasks.

### Conclusion

Earlier, I mentioned the remarkable results people are achieving with RNNs. Essentially all of these are achieved using LSTMs. They really work a lot better for most tasks!

Written down as a set of equations, LSTMs look pretty intimidating. Hopefully, walking through them step by step in this essay has made them a bit more approachable.

LSTMs were a big step in what we can accomplish with RNNs. It’s natural to wonder: is there another big step? A common opinion among researchers is: “Yes! There is a next step and it’s attention!” The idea is to let every step of an RNN pick information to look at from some larger collection of information. For example, if you are using an RNN to create a caption describing an image, it might pick a part of the image to look at for every word it outputs. In fact, Xu, et al. (2015) do exactly this – it might be a fun starting point if you want to explore attention! There’s been a number of really exciting results using attention, and it seems like a lot more are around the corner…

Attention isn’t the only exciting thread in RNN research. For example, Grid LSTMs by Kalchbrenner, et al. (2015) seem extremely promising. Work using RNNs in generative models – such as Gregor, et al. (2015), Chung, et al. (2015), or Bayer & Osendorfer (2015) – also seems very interesting. The last few years have been an exciting time for recurrent neural networks, and the coming ones promise to only be more so!

### Acknowledgments

I’m grateful to a number of people for helping me better understand LSTMs, commenting on the visualizations, and providing feedback on this post.

I’m very grateful to my colleagues at Google for their helpful feedback, especially Oriol Vinyals, Greg Corrado, Jon Shlens, Luke Vilnis, and Ilya Sutskever. I’m also thankful to many other friends sony sound forge colleagues for taking the time to help me, including Dario Amodei, and Jacob Steinhardt. I’m especially thankful to Kyunghyun Cho for extremely thoughtful correspondence about my diagrams.

Before this post, I practiced explaining LSTMs during two seminar series I taught on neural networks. Thanks to everyone who participated in those for their patience with me, and for their feedback.

### Deep Learning, NLP, and Representations

Источник: https://colah.github.io/posts/2015-08-Understanding-LSTMs/

## Prelims

The Transformer from “Attention is All You Need” has been on a lot of people’s minds over the last year. Besides producing major improvements in translation quality, it provides a new architecture for many other NLP tasks. The paper itself is very clearly written, but the conventional wisdom has been that it is quite difficult to implement correctly.

In this post I present an “annotated” version of the paper in the form of a line-by-line implementation. I have reordered and deleted some sections from the original paper and added comments throughout. This document itself is a working notebook, and should be a completely usable implementation. In total there are 400 lines of library code which can process 27,000 tokens per second on 4 GPUs.

To follow along you will first need to install PyTorch. The complete notebook is also available on github or on Google Colab with free GPUs.

Note this is merely a starting point for researchers and interested developers. The code here is based heavily on our OpenNMT packages. (If helpful feel free to cite.) For other full-sevice implementations of the model check-out Tensor2Tensor (tensorflow) and Sockeye (mxnet).

• Alexander Rush (@harvardnlp or srush@seas.harvard.edu), with help from Vincent Nguyen and Guillaume Klein

My comments are blockquoted. The main text is all from the paper itself.

The goal of updating drivers sequential computation also forms the foundation of the Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance idm 6.32 build 2 crack - Crack Key For U positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention.

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations. End- to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple- language question answering and language modeling tasks.

To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence aligned RNNs or convolution.

Most competitive neural sequence transduction models have an encoder-decoder structure (cite). Here, the encoder maps an input sequence of symbol representations $(x_1, …, x_n)$ to a sequence of continuous representations $\mathbf{z} = (z_1, …, z_n)$. Given $\mathbf{z}$, the decoder then generates an output sequence $(y_1,…,y_m)$ of symbols one element at a time. At each step the model is auto-regressive (cite), consuming the previously generated symbols as additional input when generating the next.

The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.

### Encoder

The encoder is composed of a stack of $N=6$ identical layers.

We employ a residual connection (cite) around each of the two sub-layers, followed by layer normalization (cite).

That is, the output of each sub-layer is $\mathrm{LayerNorm}(x + \mathrm{Sublayer}(x))$, where $\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself. We apply dropout (cite) to the output of each sub-layer, before it is added to the sub-layer input and normalized.

To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $d_{\text{model}}=512$.

Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed- forward network.

### Decoder

The decoder is also composed of a stack of $N=6$ identical layers.

In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.

We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.

Below the attention mask shows the position each tgt word (row) is allowed to look at (column). Words are blocked for attending to future words during training.

### Attention

An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.

We call our particular attention “Scaled Dot-Product Attention”. The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$. We compute the dot products of the query with all keys, divide each by $\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.

In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$. The keys and values are also packed together into matrices $K$ and $V$. We compute the matrix of outputs as:

The two most commonly used attention functions are additive attention (cite), and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.

While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ (cite). We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients (To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$. Then their dot product, $q \cdot k = \sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.). To counteract this effect, we scale the dot products by $\frac{1}{\sqrt{d_k}}$.

Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.

Where the projections are parameter matrices $W^Q_i \in \mathbb{R}^{d_{\text{model}} \times d_k}$, $W^K_i \in \mathbb{R}^{d_{\text{model}} \times d_k}$, $W^V_i \in \mathbb{R}^{d_{\text{model}} \times d_v}$ and $W^O \in \mathbb{R}^{hd_v \times d_{\text{model}}}$. In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=d_{\text{model}}/h=64$. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.

### Applications of Attention in our Model

The Transformer uses multi-head attention in three different ways: 1) In “encoder-decoder attention” layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as (cite).

2) The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.

3) Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot- product attention by masking out (setting to $-\infty$) all values in the input of the softmax which correspond to illegal connections.

### Position-wise Feed-Forward Networks

In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.

While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1. The dimensionality of input and output is $d_{\text{model}}=512$, and the inner-layer has dimensionality $d_{ff}=2048$.

### Embeddings and Softmax

Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $d_{\text{model}}$. We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding Pointwise Activation key and the pre-softmax linear transformation, similar to (cite). In the embedding layers, we multiply those weights by $\sqrt{d_{\text{model}}}$.

### Positional Encoding

Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add “positional encodings” to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d_{\text{model}}$ as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed (cite).

In this work, we use sine and cosine functions of different frequencies:

where $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\pi$ to $10000 \cdot 2\pi$. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.

In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of $P_{drop}=0.1$.

Below the positional encoding will add in a sine wave based on position. The frequency and offset of the wave is different for each dimension.

We also experimented with using learned positional embeddings (cite) instead, and found that the two versions produced nearly identical results. We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.

### Full Model

Here we define a function that takes in hyperparameters and produces a full model.

This section describes the training regime for our models.

We stop for a quick interlude to introduce some of the tools needed to train a standard encoder decoder model. First we define a batch object that holds the src and target sentences for training, as well as constructing the masks.

Next we create a generic training and scoring function to keep track of loss. We pass in a generic loss compute function that also handles parameter updates.

### Training Data and Batching

We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding, which has a shared source-target vocabulary of about 37000 tokens. For English- French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary.

Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.

We will use torch text for batching. This is discussed in more detail below. Here we create batches in a torchtext function that ensures our batch size padded to the maximum batchsize does not surpass a threshold (25000 if we have 8 gpus).

### Hardware and Schedule

We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models, step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).

### Optimizer

We used the Adam optimizer (cite) with $\beta_1=0.9$, $\beta_2=0.98$ and $\epsilon=10^{-9}$. We varied the learning rate over the course of training, according to the formula: This corresponds to increasing the learning rate linearly for the first $warmup_steps$ training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used $warmup_steps=4000$.

Note: This part is very important. Need to train with this setup of the model.

Example of the curves of this model for different model sizes and for optimization hyperparameters.

### Label Smoothing

During training, we employed label smoothing of value $\epsilon_{ls}=0.1$ (cite). This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.

We implement label smoothing using the KL div loss. Instead of using a one-hot target distribution, we create a distribution that has of the correct word and the rest of the mass distributed throughout the vocabulary.

Here we can see an example of how the mass is distributed to the words based on confidence.

Label smoothing actually starts to penalize the model if it gets very confident about a given choice.

We can begin by trying out a simple copy-task. Given a random set of input symbols from a small vocabulary, the goal is to generate back those same symbols.

### Greedy Decoding

This code predicts a translation using greedy decoding for simplicity.

Now we consider a real-world example using the IWSLT German-English Translation task. This task is much smaller than the WMT task considered in the paper, but it illustrates the whole system. We also show how to use multi-gpu processing to make it really fast.

We will load the dataset using torchtext and spacy for tokenization.

Batching matters a ton for speed. We want to have very evenly divided batches, with absolutely minimal padding. To do this we have to hack a bit around the default torchtext batching. This code patches their default batching to make sure we search over enough sentences to find tight batches.

### Multi-GPU Training

Finally to really target fast training, we will use multi-gpu. This code implements multi-gpu word generation. It is not specific to transformer so I won’t go into too much detail. The idea is to split up word generation at training time into chunks to be processed in parallel across many different gpus. We do this using pytorch parallel primitives:

• replicate - split modules onto different gpus.
• scatter - split batches onto different gpus
• parallel_apply - apply module to batches on different gpus
• gather - pull scattered data back onto one gpu.
• nn.DataParallel - a special module wrapper that calls these all before evaluating.

Now we create our model, criterion, optimizer, data iterators, and paralelization

Now we train the model. I will play with the warmup steps a bit, but everything else uses the default parameters. On an AWS p3.8xlarge with 4 Tesla V100s, this runs at ~27,000 tokens per second with a batch size of 12,000

### Training the System

Once trained we can decode the model to produce a set of translations. Here we simply translate the first sentence in the validation set. This dataset is pretty small so the translations with greedy search are reasonably accurate.

So affinity publisher imposition mostly covers the transformer model itself. There are four aspects that we didn’t cover explicitly. We also have all these additional features implemented in OpenNMT-py.

1) BPE/ Word-piece: We can use a library to first preprocess the data into subword units. See Rico Sennrich’s subword- nmt implementation. These models will transform the training data to look like this:

▁Die ▁Protokoll datei ▁kann ▁ heimlich ▁per ▁E - Mail ▁oder ▁FTP ▁an ▁einen ▁bestimmte n ▁Empfänger ▁gesendet ▁werden .

2) Shared Embeddings: When using BPE with shared vocabulary we can share the same weight vectors between the source / target / generator. See the (cite) for details. To add this to the model simply do this:

3) Beam Search: This is a bit too complicated to cover here. See the OpenNMT- py for a pytorch implementation.

4) Model Averaging: The paper averages the last k checkpoints to create an ensembling effect. We can do this after Pointwise Activation key fact if we have a bunch of models:

On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.

On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate Pdrop = 0.1, instead of 0.3.

The code we have written here is a version of the base model. There are fully trained version of this system available here (Example Models).

With the addtional extensions in the last section, the OpenNMT-py replication gets to 26.9 on EN-DE WMT. Here I have loaded in those parameters to our reimplemenation.

### Attention Visualization

Even with a greedy decoder the translation looks pretty good. We can further visualize it to see what is happening at each layer of the attention

Hopefully this code is useful for future research. Please reach out if you have any issues. If you find this code helpful, also check out our other OpenNMT tools.

Cheers, srush

Источник: https://nlp.seas.harvard.edu/2018/04/03/attention.html