JaeDukSeo

Project Title

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Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

JaeDukSeo

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

JaeDukSeo

Project Name

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Installation

TODO: Describe the installation process

Usage

JaeDukSeo

# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
# Output layer is linear with L2 cost (regression model)
# Hidden layer activation is tanh

import numpy as np

n_epochs = 100
n_samples = 100
n_in = 10
n_hidden = 5

JaeDukSeo

#
#   fashion_mnist_theano.py
#       date. 10/2/2017
#
#   REM: I read the article for stopping development of "THEANO".
#        The deep learning framework stimulated me and made me write codes. 
#        I'd like to say thank you to Theano supporting team.
#

import os

JaeDukSeo

from sklearn import datasets  
from sklearn.model_selection import train_test_split  
from sklearn.model_selection import cross_val_score

X, y = datasets.make_classification(n_samples=10000, n_features=20,  
                                    n_informative=2, n_redundant=10,
                                    random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,  
                                                    random_state=42)

JaeDukSeo

# code: https://github.com/dmlc/xgboost/blob/master/demo/gpu_acceleration/cover_type.py
import xgboost as xgb
import numpy as np
from sklearn.datasets import fetch_covtype
from sklearn.model_selection import train_test_split
import time

# Fetch dataset using sklearn
cov = fetch_covtype()
X = cov.data

JaeDukSeo

import numpy as np
np.set_printoptions(2)

ground_truth_prob = np.random.rand(10)
number_of_levers_count = np.zeros(10)
agents_prob = np.zeros(10)

num_episode = 4000
e = 0.33

JaeDukSeo

class SOM_Layer(): 

    def __init__(self,m,n,dim,num_epoch,learning_rate_som ,radius_factor ,gaussian_std):
        
        self.m = m
        self.n = n
        self.dim = dim
        self.gaussian_std = gaussian_std
        self.num_epoch = num_epoch
        self.map = tf.Variable(tf.random_normal(shape=[m*n,dim],stddev=0.05))

JaeDukSeo

class SOM_Layer(): 

    def __init__(self,m,n,dim,num_epoch,learning_rate_som ,radius_factor ,gaussian_std):
        
        self.m = m
        self.n = n
        self.dim = dim
        self.gaussian_std = gaussian_std
        self.num_epoch = num_epoch
        self.map = tf.Variable(tf.random_normal(shape=[m*n,dim],stddev=0.05))