johnarevalo

#include <math.h>
#include<iostream>

using namespace std;
int main(void)
{
    cout<<"PARCIAL 1"<<endl<<endl;
    const int N = 10;
    int horas[]     = { 240,  230,  250,  250,  200,  240,  250,  250,  241,  245};
    int riesgo[]    = {   1,    0,    1,    0,    1,    0,    0,    0,    1,    1};

johnarevalo

from bs4 import BeautifulSoup
import requests

issns = ['1063-6706', '0129-0657', '0162-8828', '1089-778X', '2162-237X', '0962-2802',
         '1566-2535', '0278-0062', '1556-603X', '0005-1098', '1063-6560', '1541-1672',
         '0031-3203', '0020-0255', '0004-3702', '0950-7051', '0893-6080', '0957-4174',
         '1539-2791', '1063-6536', '1556-6072', '0018-9286', '1384-5810', '0888-613X',
         '1935-3812', '1568-4539', '1524-9050', '1041-4347', '1532-4435', '2157-6904',
         '0925-2312', '0952-1976', '1051-8215', '0169-7439', '0933-3657', '0165-0114',
         '0884-8173', '1472-6947', '0956-5515', '1866-9956', '1558-7916', '1949-3045',

johnarevalo

##VGG16 model for Blocks

This is the Blocks model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Keras model.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

johnarevalo

import numpy
import theano
from collections import OrderedDict
from fuel.datasets import IndexableDataset
from fuel.streams import DataStream
from fuel.transformers import Mapping
from fuel.schemes import ShuffledScheme

from blocks import initialization
from blocks.bricks.lookup import LookupTable

johnarevalo

import numpy
from theano import tensor
from fuel.streams import DataStream
from fuel.schemes import ShuffledScheme
from fuel.datasets.iris import Iris
from fuel.transformers import Mapping
from blocks.bricks import Linear, Softmax
from blocks.bricks.cost import BinaryCrossEntropy, MisclassificationRate
from blocks.initialization import Uniform, Constant
from blocks.graph import ComputationGraph

johnarevalo

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

johnarevalo

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

johnarevalo

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries"
   ]
  },
  {

johnarevalo