optim | S3 Lambda Imagemin win | Cloud Functions library
kandi X-RAY | optim Summary
kandi X-RAY | optim Summary
Automagitically optimize your images on S3 with the magic of AWS Lambda. Optim is a super-simple Lambda function that can listen to an S3 bucket for uploads, and runs everything it can through imagemin.
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License : Strong Copyleft (CC BY-SA 4.0)
Copyimport torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
trainloader = [(torch.Tensor(np.random.randn(1000, 5)), torch.Tensor([1.0] * 1000))] * 1000
model = nn.Sequential(nn.Linear(5, 2500), nn.Linear(2500, 1500)using Optim
# Initialize some parameters
coeffs = rand(10)
rho = 0.1
ys = rand(10)
xs = rand(10,10)
Wmat = rand(10,10)
sige=0.5
# Construct likelihood with parameters fixed at pre-defined values
function like_sar2(β::Vector{Float64},ρ=rhCommunity Discussions
Trending Discussions on optim
QUESTION
In C++20, we got the capability to sleep on atomic variables, waiting for their value to change.
We do so by using the std::atomic::wait method.
Unfortunately, while wait has been standardized, wait_for and wait_until are not. Meaning that we cannot sleep on an atomic variable with a timeout.
Sleeping on an atomic variable is anyway implemented behind the scenes with WaitOnAddress on Windows and the futex system call on Linux.
Working around the above problem (no way to sleep on an atomic variable with a timeout), I could pass the memory address of an std::atomic to WaitOnAddress on Windows and it will (kinda) work with no UB, as the function gets void* as a parameter, and it's valid to cast std::atomic to void*
On Linux, it is unclear whether it's ok to mix std::atomic with futex. futex gets either a uint32_t* or a int32_t* (depending which manual you read), and casting std::atomic to u/int* is UB. On the other hand, the manual says
The uaddr argument points to the futex word. On all platforms, futexes are four-byte integers that must be aligned on a four- byte boundary. The operation to perform on the futex is specified in the futex_op argument; val is a value whose meaning and purpose depends on futex_op.
Hinting that alignas(4) std::atomic should work, and it doesn't matter which integer type is it is as long as the type has the size of 4 bytes and the alignment of 4.
Also, I have seen many places where this trick of combining atomics and futexes is implemented, including boost and TBB.
So what is the best way to sleep on an atomic variable with a timeout in a non UB way? Do we have to implement our own atomic class with OS primitives to achieve it correctly?
(Solutions like mixing atomics and condition variables exist, but sub-optimal)
...ANSWER
Answered 2021-Jun-15 at 20:48You shouldn't necessarily have to implement a full custom atomic API, it should actually be safe to simply pull out a pointer to the underlying data from the atomic and pass it to the system.
Since std::atomic does not offer some equivalent of native_handle like other synchronization primitives offer, you're going to be stuck doing some implementation-specific hacks to try to get it to interface with the native API.
For the most part, it's reasonably safe to assume that first member of these types in implementations will be the same as the T type -- at least for integral values [1]. This is an assurance that will make it possible to extract out this value.
... and casting
std::atomictou/int*is UB
This isn't actually the case.
std::atomic is guaranteed by the standard to be Standard-Layout Type. One helpful but often esoteric properties of standard layout types is that it is safe to reinterpret_cast a T to a value or reference of the first sub-object (e.g. the first member of the std::atomic).
As long as we can guarantee that the std::atomic contains only the u/int as a member (or at least, as its first member), then it's completely safe to extract out the type in this manner:
QUESTION
Here is the question:
...ANSWER
Answered 2021-Jun-15 at 16:06You have to assign the number of elements of the array to return (2 in this case) to what the argument returnSize points at (*returnSize) to tell the judge system how large the array you returned is.
QUESTION
ANSWER
Answered 2021-Mar-18 at 15:40You need to define a different surrogate posterior. In Tensorflow's Bayesian linear regression example https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_Regression.ipynb#scrollTo=VwzbWw3_CQ2z
you have the posterior mean field as such
QUESTION
I am trying to use my own train step in with Keras by creating a class that inherits from Model. It seems that the training works correctly but the evaluate function always returns 0 on the loss even if I send to it the train data, which have a big loss value during the training. I can't share my code but was able to reproduce using the example form the Keras api in https://keras.io/guides/customizing_what_happens_in_fit/ I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.
The code:
...ANSWER
Answered 2021-Jun-12 at 17:27As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.
QUESTION
I want to optimize my query to use CTE and some windows functions for better improvement. I am updating existing rows with specified data from other row but they have the same ID number. The code is for MS SQL.
LinkTable:
...ANSWER
Answered 2021-Jun-15 at 10:51Your code is basically fine, but I would add some filters:
QUESTION
I have a problem about not accessing GPU in PyCharm and I use NVIDIA as GPU.
I installed tensorflow-gpu in Python Interpreter of Setting part in Pycharm and then I run the code but I still cannot access it.
I wonder if I should use CUDA library? How can I fix it?
Here is my code snippet which is shown below.
...ANSWER
Answered 2021-Jun-14 at 11:14I fixed my issue.
Here are the steps of solving that issue.
1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads
2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download
3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}
4 ) Then run the .py code in PyCharm and it perceives GPU at last.
QUESTION
I am trying to understand the example with incorrect sync code from The Go Memory Model.
...Double-checked locking is an attempt to avoid the overhead of synchronization. For example, the twoprint program might be incorrectly written as:
ANSWER
Answered 2021-Jun-14 at 19:18According to the Go memory model:
There are no guarantees that one goroutine will see the operations performed by another goroutine unless there is an explicit synchronization between the two using channels, mutex. etc.
In your example: the fact that a goroutines sees done=true does not imply it will see a set. This is only guaranteed if there is explicit synchronization between the goroutines.
The sync.Once probably offers such synchronization, so that's why you have not observed this behavior. There is still a memory race, and on a different platform with a different implementation of sync.Once, things may change.
QUESTION
Is there an easy way to get a solution where there is a constraint on the maximum value of a derivative of a polynomial function f(x), for a certain range of values for x?
Like was answered to this question, curve_fit from scipy.optimize can deal with constraints on the individual coefficients, like in the following example:
ANSWER
Answered 2021-Jun-15 at 12:34The curve_fit method doesn't support additional constraints. However, you could implement a non-linear least-squares problem
QUESTION
I try to find the optimal number of topics in the LDA algorithm for my database. For this purpose I try to use the package "ldatuning". After the implementation of the LDA algorithm with the "gibbs" method I try to use the function:
Griffiths2004(models, control) The arguments should be: models An object of class "LDA control A named list of the control parameters for estimation or an object of class "LDAcontrol".
I used it like that:
...ANSWER
Answered 2021-Jun-15 at 11:13The problem probably lies in how you pass the control parameter list to the Griffiths2004 function.
In the Griffiths2004 function, the parameters are addressed as in a list using control$param. However, lda_5@control returns an S4 object where the parameters should be addressed with control@param. (An S4 object is an advanced class in R, but the only important difference for this application is, that we address objects in these lists with @ instead of $)
You can see that lda@control is an S4 object when calling it:
QUESTION
Assume I have a list containing 5 vectors filled with integers between 1 and d, where d can be any integer
...ANSWER
Answered 2021-Jun-15 at 10:35You could use vapply to do this (assuming you want a vector of integers):
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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Install optim
Clone this repo
Run npm install
Fill in AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY in .env to a set of credentials that can create Lambda functions (alternatively have these already in your environment)
Create an IAM role for Optim to use. It needs the following permissions on all the S3 buckets you want to use (allowing these operations on ARN * is easiest to start with): getObject putObject putObjectAcl
Find the ARN for this role. It looks something like arn:aws:iam::1234567890:role/rolename.
Fill in AWS_ROLE_ARN in .env
Run npm run deploy
Hurrah, your Lambda function is now deployed! It'll be created with the name optim-production unless you changed values in .env
You can now hook this function up to any S3 bucket you like in the management console. Easiest way is to follow AWS's guide
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