thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

aaronpolhamus

n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_husky
n02096294 4 Australian_terrier
n02102040 5 English_springer
n02066245 6 grey_whale
n02509815 7 lesser_panda
n02124075 8 Egyptian_cat
n02417914 9 ibex
n02123394 10 Persian_cat

daniel-j-h

update-alternatives --install "/usr/bin/ld" "ld" "/usr/bin/ld.gold" 20
update-alternatives --install "/usr/bin/ld" "ld" "/usr/bin/ld.bfd" 10

update-alternatives --config ld

ld --version
GNU gold

export CPP=cpp-5 gcc-5 g++-5
env CXXFLAGS='-march=native -flto -fuse-linker-plugin' cmake .. -DCMAKE_BUILD_TYPE=Release

patrickward

avconv -i inbound.mp4 -vsync 1 -r 0.1 -an -y 'outbound-folder/video-clip-%4d.jpg'

YusukeSuzuki

#include <algorithm>
#include <vector>
#include <iostream>

int
main(int argc, char* argv[])
{
  using namespace std;

	vector<double> v1 = {1.1,2.2,3.3};

MarkVaughn

function getURLParameter(name) {
    return decodeURIComponent(
        (RegExp('[?|&]'+name + '=' + '(.+?)(&|$)').exec(location.search)||[null,null])[1]
    );
}
function setURLParameter(name,value){
	var search;
	if(getURLParameter(name)){
		search =location.search.replace(new RegExp('([?|&]'+name + '=)' + '(.+?)(&|$)'),"$1"+encodeURIComponent(value)+"$3"); 
	}else if(location.search.length){