yusugomori

from keras.optimizers import Optimizer
from keras.legacy import interfaces
from keras import backend as K
import tensorflow as tf


class Adabound(Optimizer):
    def __init__(self, lr=0.001,
                 beta_1=0.9, beta_2=0.999,
                 gamma=0.001,

yusugomori

import os
import subprocess
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optimizers
from torch.autograd import Variable

yusugomori

const seedrandom = require('seedrandom');
const print = require('./utils').print;
const math = require('./math');
const LSTM = require('./lstm');

let rng = seedrandom(1234);

function main() {
  const TRAIN_NUM = 30;  // time sequence
  const TEST_NUM = 10;

yusugomori

const math = require('./math');


class LSTM {
  constructor(nIn, nHidden, nOut, learningRate, activation = math.fn.tanh, rng = Math.random) {
    this.nIn = nIn;
    this.nHidden = nHidden;
    this.nOut = nOut;
    this.learningRate = learningRate;
    this.activation = activation;

yusugomori

const print = require('./utils').print;
const math = require('./math');
const seedrandom = require('seedrandom');
const RNN = require('./rnn');

let rng = seedrandom(1234);


function main() {

yusugomori

const math = require('./math');

class RNN {
  constructor(nIn, nHidden, nOut, truncatedTime = 3, learningRate = 0.1, activation = math.fn.tanh, rng = Math.random) {
    this.nIn = nIn;
    this.nHidden = nHidden;
    this.nOut = nOut;
    this.truncatedTime = truncatedTime;
    this.learningRate = learningRate;
    this.activation = activation;

yusugomori

package DeepLearning;

import java.util.Random;
import java.util.List;
import java.util.ArrayList;

public class Dropout {
    public int N;
    public int n_in;
    public int[] hidden_layer_sizes;

yusugomori

package DeepLearning;

import java.util.Random;
import java.util.function.DoubleFunction;
import static DeepLearning.utils.*;

public class HiddenLayer {
    public int N;
    public int n_in;
    public int n_out;

yusugomori

package DeepLearning;

public class LogisticRegression {
    public int N;
    public int n_in;
    public int n_out;
    public double[][] W;
    public double[] b;

    public LogisticRegression(int N, int n_in, int n_out) {

yusugomori

package DeepLearning;

import java.util.Random;

public class utils {
    public static double uniform(double min, double max, Random rng) {
        return rng.nextDouble() * (max - min) + min;
    }

    public static int binomial(int n, double p, Random rng) {