zhongwen

eval = val
iter = imginst
image_list = "imagenet/val.lst"
image_bin = "val.bin"
#mean_r=123.68
#mean_g=116.779
#mean_b=103.939
mean_value = 103.939,116.779,123.68
#no random crop and mirror in test
iter = end

zhongwen

{
 "metadata": {
  "name": "cuBLAS-no-PCA"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

zhongwen

zhongwen

# ~/.tmux.conf
#
# See the following files:
#
#   /opt/local/share/doc/tmux/t-williams.conf
#   /opt/local/share/doc/tmux/screen-keys.conf
#   /opt/local/share/doc/tmux/vim-keys.conf
#
# URLs to read:
#

zhongwen

class OnlineLearner(object):
    def __init__(self, **kwargs):
        self.last_misses = 0.
        self.iratio = 0.
        self.it = 1.
        self.l = kwargs["l"]
        self.max_ratio = -np.inf
        self.threshold = 500.

    def hinge_loss(self, vector, cls, weight):

zhongwen

static inline float euclidean_baseline_float(const int n, const float* x, const float* y){
	float result = 0.f;
	for(int i = 0; i < n; ++i){
		const float num = x[i] - y[i];
		result += num * num;
	}
	return result;
}

static inline float euclidean_intrinsic_float(int n, const float* x, const float* y){