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JaeDukSeo
/ README-Template.md
Created
March 11, 2017 01:58
— forked from PurpleBooth/README-Template.md
A template to make good README.md
JaeDukSeo
/ README-Template.md
Created
March 11, 2017 01:58
— forked from PurpleBooth/README-Template.md
A template to make good README.md
JaeDukSeo
/ README.md
Created
April 28, 2017 04:05
— forked from zenorocha/README.md
A template for Github READMEs (Markdown) + Sublime Snippet
JaeDukSeo
/ nn.py
Created
October 30, 2017 00:08
— forked from ottokart/nn.py
3-layer neural network example with dropout in 2nd layer
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| # 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
Created
November 11, 2017 17:02
— forked from tomokishii/fashion_mnist_theano.py
fashion_mist_theano.py - one another mnist (fashion mnist) classification code using Theano. (Goodbye Theano!)
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| # | |
| # 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
/ cpu_xgboost.py
Created
June 14, 2018 00:08
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| 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) |
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| # 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 |
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| 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 |
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| 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)) |
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| 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)) |
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