GTX980 + Core i7 -6700K @ 4.00GHz + ubuntu 16.04 chainer
alexnet Average Forward: 51.7668122186 ms Average Backward: 122.44968462 ms Average Total: 174.216496838 ms
googlenet Average Forward: 234.798005846 ms
GTX980 + Core i7 -6700K @ 4.00GHz + ubuntu 16.04 chainer
alexnet Average Forward: 51.7668122186 ms Average Backward: 122.44968462 ms Average Total: 174.216496838 ms
googlenet Average Forward: 234.798005846 ms
import theano.tensor as T | |
from theano import function | |
x = T.dscalar('x') | |
z = x**2 | |
gz = T.grad(z, x) | |
f = function([x], gz) | |
print(f(12.2)) |
#!/bin/bash | |
python test.py -m "text message" |
#!/bin/bash | |
python test.py -m "text message" |
class SVM(object): | |
def __init__(self,n_in,c0=1,c1=1,loss = RAMP_LOSS,gamma = 0.5): | |
self.dim = n_in | |
self.w = np.zeros(n_in) | |
self.b = np.zeros(1) | |
self.c0 = c0 | |
self.c1 = c1 | |
self.gamma = gamma | |
self.x = None | |
self.y = None |
#include <Eigen/Core> | |
#include <Eigen/Sparse> | |
using namespace std; | |
using namespace Eigen; | |
void sinitialize(VectorXd &s,double mu,unsigned int sizex,VectorXd &x) | |
{ | |
for (unsigned int i=0;i<sizex;i++) | |
if (x[i]!=0)s[i] = mu/x[i]; | |
else s[i]=0.1; |
dot_globl :min_caml_read_int | |
label :min_caml_read_int | |
read at | |
sll at, at, 8 | |
read at | |
sll at, at, 8 | |
read at | |
sll at, at, 8 | |
read at | |
add v0, at, zero |
let rec create_2dmatrix a b c d = | |
let matrix = Array.create 2 (Array.create 0 0.0) in | |
matrix.(0)<-Array.create 2 0.0; | |
matrix.(1)<-Array.create 2 0.0; | |
let mx_0 = matrix.(0) in | |
mx_0.(0)<- a; | |
mx_0.(1)<- b; | |
let mx_1 = matrix.(1) in | |
mx_1.(0)<- c; | |
mx_1.(1)<- d; |