kris-singh

# Path of eigen checked out from devlopement branch
cmake_minimum_required(VERSION 3.4)
project(Test)
set(CMAKE_CXX_FLAGS "-std=c++11")
include_directories(/Users/kris/Desktop/kmean/eigen-git-mirror)
add_executable(main eigen_example.cpp)


#include <algorithm>
#include <cmath>

kris-singh

from joblib import Parallel, delayed
import Queue
import os

# Define number of GPUs available
N_GPU = 4

# Put indices in queue
q = Queue.Queue(maxsize=N_GPU)
for i in range(N_GPU):

kris-singh

#include <torch/torch.h>
#include <iostream>
#include <ATen/Parallel.h>
#include <ATen/Aten.h>

using namespace at;
// using namespace torch;

void submodular_select(Tensor candidate_points, Tensor features_done, Tensor features)
{

kris-singh

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms


def backward_hook_function(grad_out):
    print(grad_out.shape)
    print(grad_out.norm())
    # print("grad_norm", grad_in.norm())

kris-singh

// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#include "cpu/vision.h"


template <typename scalar_t>
at::Tensor soft_nms_cpu_kernel(const at::Tensor& dets,
                          at::Tensor& scores,
                          const float threshold) {
  AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
  AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");

kris-singh

class SelectLoss:
    """
        Selection based on Loss values of samples.
        No need of rejection sampling.
    """

    def __init__(self, X, Y, fwd_batch_size, batch_size, _,  loss):
        """
        :param loss: loss function
        :param x_train: training dataN

kris-singh

class SelectLoss:
    """
        Selection based on Loss values of samples.
        No need of rejection sampling.
    """

    def __init__(self, X, Y, fwd_batch_size, batch_size, _,  loss):
        """
        :param loss: loss function
        :param x_train: training dataN

kris-singh

from __future__ import division
from __future__ import print_function
import numpy as np
import six
import argparse
import keras
import gzip
import tensorflow as tf
import pickle
import pandas as pd

kris-singh

#include <mlpack/core.hpp>

#include <mlpack/core/optimizers/rmsprop/rmsprop.hpp>
#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>
#include <mlpack/methods/ann/rbm/spike_slab_rbm_policy.hpp>
#include <mlpack/methods/ann/rbm.hpp>
#include <mlpack/methods/ann/rbm/binary_rbm_policy.hpp>
#include <mlpack/methods/softmax_regression/softmax_regression.hpp>
#include <mlpack/core/optimizers/minibatch_sgd/minibatch_sgd.hpp>
#include <mlpack/core/optimizers/sgd/sgd.hpp>

kris-singh

#include <mlpack/core.hpp>

#include <mlpack/core/optimizers/rmsprop/rmsprop.hpp>
#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>
#include <mlpack/methods/ann/rbm/spike_slab_rbm_policy.hpp>
#include <mlpack/methods/ann/rbm.hpp>
#include <mlpack/methods/ann/rbm/binary_rbm_policy.hpp>
#include <mlpack/methods/softmax_regression/softmax_regression.hpp>
#include <mlpack/core/optimizers/minibatch_sgd/minibatch_sgd.hpp>
#include <mlpack/core/optimizers/sgd/sgd.hpp>