#Selu activation function

[ingoampt]

Activation function progress in deep learning, Relu, Elu, Selu, Geli , mish, etc - include table and graphs - day 24

Activation Function Formula Comparison Why (Problem and Solution) Mathematical Explanation and Proof Sigmoid \(\sigma(z) = \frac{1}{1 + e^{-z}}\) – Non-zero-centered output – Saturates for large values, leading to vanishing gradients Problem: Vanishing gradients for large positive or negative inputs, slowing down learning in deep networks. Solution: ReLU was introduced to avoid the saturation…

[eurekakinginc]

"[D] State Of The Art Activation Function: GELU, SELU, ELU, ReLU and more. With visualization of the activation functions and their derivatives."- Detail: https://ift.tt/30Qj3jS level and above: Probably skip at least the two first headers, to ReLU)I recently did a long-form post explaining and visualizing the various activation functions. The math is not that complicated, but knowing the ups and downs of each of these activation functions, or just knowledge of their existence, could prove its worth.Any feedback is appreciated. As I'm sharing what I learn, I create for other people to learn as well. This is not any advanced topic, but it does provide an overview of SOTA activation functions - and to this extent, the plan is to make similar posts for more advanced topics in the future.. Caption by permalip. Posted By: www.eurekaking.com

[anthrochristianramsey]

SELU activation function?

[fbreschi]

State Of The Art Activation Functions Explained: GELU, SELU, ELU, ReLU and more

http://bit.ly/2pff2rU

[jackson38toh]

On markets and marts

Q: I’m curious about why the short form of “market” is “mart” and not “mark.” Was the usage influenced by Kmart?

A: No, “mart” appeared in the Middle Ages, hundreds of years before the S.S. Kresge Company opened its first Kmart store in 1962. And it wasn’t a shortening of “market” either, at least not in English. The two terms came into English separately from different sources, though they’re  etymologically related.

“Mart,” which showed up in the early 1400s, comes from Middle Dutch, where marct and its colloquial form mart were derived from the Old Dutch markat. The Dutch words ultimately come from mercātus, classical Latin for market or fair

“Market,” which first appeared in the early 900s, was borrowed from either medieval Latin, Germanic, or French, but it too ultimately comes from mercātus.

When “market” arrived in Old English, it meant a “meeting or gathering together of people for the purchase and sale of provisions or livestock, publicly displayed, at a fixed time and place,” according to the Oxford English Dictionary.

The earliest OED example is from a document, dated 963, in the Anglo-Saxon Chronicle, a collection of Old English writing from the 800s to the 1100s: “Ic wille þæt markete beo in þe selue tun” (“I will be at that market in the same town”).

In, the 1200s, “market” came to mean an “open space or covered building in which vendors gather to display provisions (esp. from stalls or booths), livestock, etc., for sale,” Oxford says.

The dictionary’s first example is from a sermon written around 1275 in the Kentish dialect: “So ha kam into þe Marcatte so he fond werkmen þet were idel” (“So he came into the market and found workmen that were idle”).

Over the years, “market” has taken on many other senses, including a geographical area for commerce, 1615 (as in “the French market for silk”); the state of commercial activity, 1776 (“the market for wool is weak”); short for “stock market,” 1814; in the compound “supermarket,” 1931; and the operation of supply and demand, 1970 (“the market has many virtues”). Dates are from the first Oxford citations; examples are ours.

When “mart” showed up in Middle English, it referred to a ���regular gathering of people for the purpose of buying and selling,” according to the OED. The dictionary’s first example is from “The Libelle of Englyshe Polycye,” an anonymous political poem written around 1436:

“And wee to martis of Braban charged bene Wyth Englyssh clothe” (“And we carried a load of good English cloth to the marts of Braban [in the Low Countries]”). From Political Poems and Songs Relating to English History (1861), edited by Thomas Wright.

In the late 16th century, the OED says, “mart” came to mean “any public place for buying and selling, as a marketplace, market hall, etc.” The dictionary’s earliest example, expanded here, is from Churchyards Challenge (1593), a collection of prose and prose by the English author and soldier Thomas Churchyard:

“As nothing could, escape the reach of arts / Schollers in scholes, and merchantes in their marts / Can ply their thrift, so they that maketh gold, / By giftes of grace, haue cunning treble fold.”

Today, “mart” usually refers to “a shop or stall carrying on trade of a specified kind (as shoe mart, etc.),” the OED says, adding, “This latter use is particularly prevalent in the names of retail businesses, esp. in N. Amer.”

It’s hard to tell from the dictionary’s citations when “mart” first referred to a single retail store rather than a place housing various vendors. The first clear example, which we’ve expanded, is from the Dec. 17, 1831, issue of a London weekly, the Mirror of Literature, Amusement, and Instruction:

“It’s good-bye to Wellingtons and Cossacks, Ladies’ double channels, Gentlemen’s stout calf, and ditto ditto. They’ve all been sold off under prime cost, and the old Shoe Mart is disposed of, goodwill and fixtures, for ever and ever.” (From a fictional account of the sale of a family’s shoe store in London.)

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from Blog – Grammarphobia https://www.grammarphobia.com/blog/2018/09/market-mart.html

[eurekakinginc]

"[D] SELUs don't actually solve the dying ReLU problem"- Detail: One frequently mentioned problem with ReLUs is that they can get stuck outputting nothing but 0s when their input shifts such that every value is negative. SELUs [1] claim to solve this problem.However, there is another way that activation functions can stop being useful to the network: when they degenerate to a linear function. This can happen with ReLUs, SELUs and some other activation functions when their input shifts such that every value is positive. To demonstrate this I made a simple toy network.The task is to approximate the ReLU function itself with the function f(x * a + b) * c + d, where x is the input, a, b, c and d are learned scalar values and f is an activation function. Values for x are uniformly chosen from the range [-0.5, 0.5].If we start with a = 1 and b = 0.5 then all inputs to f will be positive. For many starting points of c and d this will still converge when ReLU is used for f. But for c = 1 and d = -0.5 all common piecewise activation functions will fail, including SELU and ELU.However, there is a potential activation function that does not exhibit that problem, that I don't see being talked about a lot: Softplus, defined as log(exp(x) + 1). Its derivative is strictly monotonically increasing and therefor non-linear in every sub range. Using softplus in place of f in the toy example allows it to converge from any starting point. [proof pending]In the following images you can see the learned function at different numbers of iterations. The starting point a = 1, b = 0.5, c = 1 and d = -0.5 was used. All use Adam optimizer with a learning rate of 0.1 and default values for alpha and beta. The mean absolute difference is minimized. Tensorflow 1.14.0 was used.ReLUSELUSoftplusIn practice the inputs to activation functions may follow a long tail distribution making this very unlikely when the loss function is fixed. But for some problems, like adversarial networks, where the loss function itself is learned, this might not be the case.There are even situations where SELU fails to converge whereas ReLU and ELU do. The following images use the starting point a = 1, b = 0.5, c = 1 and d = 0. Again, all initial inputs to the activation function are positive. As we can see this does not necessarily mean that it is stuck.ReLU. The slight curve at 0 is a result of under-sampling the function.SELU. Note how the initial increase in gradient below 0 creates an insurmountable wall of increased loss that gradient descent can't overcome.ELU. By having a monotonic gradient it does not have the same problem as SELU.SoftplusAlternative title for this post: SELU considered harmful. (I mean no offense to the authors, their paper is truly insightful and you should definitely read it!)[1] https://ift.tt/2sJ8lvq. Caption by relgukxilef. Posted By: www.eurekaking.com

[fbreschi]

State Of The Art Activation Functions Explained: GELU, SELU, ELU, ReLU and more

http://bit.ly/2pidtcJ

[eurekakinginc]

"[D] Activation function that preserves mean, variance and covariance? (Similar to SELU)"- Detail: Given the success of SELUs with standardized data, I’m wondering if there is an equivalent for whitened data. I.e. is there an activation function that preserves the mean, the variance and the covariance between each variable? I don’t know if it’d be useful, but the data I have for my FFNN has very high covariance between a lot of the variables, so I figure whitening could be useful, and maybe preserving it across layers could be too? I think the main advantage of SELUs was that the gradient magnitude remained somewhat constant, so I don’t imagine this would be nearly as useful, but I’m wondering if anyone has looked into it.. Caption by deltasheep. Posted By: www.eurekaking.com

[eurekakinginc]

"[P] Keras - Accessing feature indices from custom objective"- Detail: Hi all.I would like to create a custom loss function that uses a feature as part of the calculation. More specifically - I just need the mean of the feature across the batch/set, and include that in a calculation for my custom loss.So if I have 1000 features, in in my custom loss I would like to be able to know the mean value of feature #10, in the batch/set that was used to give y_pred. I know I can use wrappers to pass custom data, but passing a vector of all instances of feature #10 is pointless, because I don't know which subset of indices have been used in that batch/set.I found this example on Stack Overflow which seems quite close to what I'm after, but I don't fully understand how to make it work for my situation, partially because my Keras layout seems have a slightly different style.Stack overflow thread: https://ift.tt/2wbtK6u of my code/model:model = Sequential() model.add(Dense(80, kernel_initializer='uniform',input_dim=NCOMPONENTS)) model.add(Dropout(0.2)) model.add(Activation('selu')) model.add(BatchNormalization()) model.add(Dense(40, kernel_initializer='uniform')) model.add(Dropout(0.2)) model.add(Activation('selu')) model.add(BatchNormalization()) model.add(Dense(10, kernel_initializer='uniform')) model.add(Dropout(0.2)) model.add(Activation('selu')) model.add(BatchNormalization()) model.add(Dense(2, kernel_initializer='uniform')) model.add(Activation('softmax')) adam = optimizers.Adam(lr=0.000005, beta_1=0.9, beta_2=0.999, decay=0.0) model.compile(loss='binary_crossentropy', optimizer=adam, metrics=[ single_class_precision(1)]) history = model.fit(X_train, Y_train, epochs=25000, batch_size=512, verbose=1, shuffle=True, validation_split=0.3,class_weight={0:1, 1:2.5},callbacks=callbacks_list) Any help would be appreciated.. Caption by Zman420. Posted By: www.eurekaking.com