chicm

import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;

public class Solution {
    static class Feature {
        public int Qi;
        public int Qj;

chicm

import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;

public class Solution {

    public static void main(String[] args) {
        Scanner in = new Scanner(System.in);

chicm

import sys
import math
import random

def polygon_intersection(subject_polygon, clip_polygon):
    def inside(p):
        return (cp2[0] - cp1[0]) * (p[1] - cp1[1]) > (cp2[1] - cp1[1]) * (p[0] - cp1[0])

    def compute_intersection():
        dc = [cp1[0] - cp2[0], cp1[1] - cp2[1]]

chicm

import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;

public class Solution {
    static class Action {
        public int op;
        public int u;

chicm

#include <bits/stdc++.h>
#define abs(x) ((x) < 0?-(x):(x))
#define uint unsigned int
using namespace std;

int gcd(int x, int y) {
   if(x > y) swap(x,y);
   while(x > 0) {
      int z =x;
      x =y%x;

chicm

##VGG16 model for Keras

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

It has been obtained by directly converting the Caffe model provived by the authors.

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

chicm

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

chicm

FROM: http://benanne.github.io/2015/03/17/plankton.html

Meta-Tricks:
	- Use %10 for validation with STRATIFIED SAMPLING (my mistake)
	- Cyclic Pooling 
	- Leaky ReLU = max(x, a*x) learned a
		- reduces overfitting with a ~= 1/3
	- Orthogonal initialization http://arxiv.org/pdf/1312.6120v3.pdf
	- Use larger weight decay for larger models since otherwise some layers might diverge

chicm

FROM: http://benanne.github.io/2015/03/17/plankton.html

Meta-Tricks:
	- Use %10 for validation with STRATIFIED SAMPLING (my mistake)
	- Cyclic Pooling 
	- Leaky ReLU = max(x, a*x) learned a
		- reduces overfitting with a ~= 1/3
	- Orthogonal initialization http://arxiv.org/pdf/1312.6120v3.pdf
	- Use larger weight decay for larger models since otherwise some layers might diverge

chicm

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):