suvojit-0x55aa

import torch
import torch.nn as nn

class LabelSmoothing(nn.Module):
    """
    NLL loss with label smoothing.
    """
    def __init__(self, smoothing=0.0):
        """
        Constructor for the LabelSmoothing module.

suvojit-0x55aa

def gauss_map(size_x, size_y=None, sigma_x=5, sigma_y=None):    
    if size_y == None:
        size_y = size_x
    if sigma_y == None:
        sigma_y = sigma_x
    
    assert isinstance(size_x, int)
    assert isinstance(size_y, int)
    
    x0 = size_x // 2

suvojit-0x55aa

from __future__ import division, print_function

import concurrent.futures as cf
from glob import glob
import logging
import os
from time import time, sleep

import boto3

suvojit-0x55aa

import numpy as np
import csv
import matplotlib.pyplot as plt


#fix random seed for reproducibility
np.random.seed(1)

#Read Dataset
iris = open('iris.csv','r')

suvojit-0x55aa

import javax.naming.OperationNotSupportedException;
import java.lang.Exception;
import java.lang.Math;
import java.lang.String;
import java.lang.System;
import java.util.Scanner;

class Matrix
{
    private int mat[][];

suvojit-0x55aa

__author__ = 'Suvojit Manna'

try:
    p = float(raw_input())
    r = float(raw_input())
    t = int(raw_input())
    ans = p
    r /= 12*100
    for i in xrange(1, t):
        ans *= 1 + r