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charleslparker / run_gpu_test.py
Created August 17, 2022 16:44
Practical Tensorflow Test For GPU
import os
import sys
URL = "https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip"
def run_gpu_test(use_cuda):
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
if not use_cuda:
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import org.junit.Test;
@charleslparker
charleslparker / data_links
Created July 11, 2019 10:25
DutchMLSchool Training Links

Services and Algorithms

This section describes each of the services compared in the throwdown and the algorithms/models used.

BigML

Decision trees, both single and bagged.

@charleslparker
charleslparker / analyze-features.json
Last active June 2, 2016 16:12
Custom Feature Analyzer
{
"name": "Custom feature analyzer",
"description": "Find the best features for modeling using a greedy algorithm",
"kind": "script",
"source_code": "analyze-features.whizzml",
"inputs": [
{
"name": "dataset-id",
"type": "dataset-id",
@charleslparker
charleslparker / best-first.whizzml
Last active May 25, 2016 14:15
Best-first feature selection via WhizzML
;; Get feature names given ids
(define (feature-names dataset-id ids)
(let (fields (get (fetch dataset-id) "fields"))
(map (lambda (id) (get-in fields [id "name"])) ids)))
(define (create-k-folds dataset-id k-folds)
(let (k-fold-fn (lambda (x) (create-dataset
{"origin_dataset" dataset-id
"row_offset" x
"row_step" k-folds
@charleslparker
charleslparker / gradient-boosting.whizzml
Last active May 6, 2016 12:59
A vanilla implementation of gradient boosting in WhizzML
;; This is a vanilla implementation of gradient boosting. The main
;; function is at the bottom of the script, where it explains the
;; algorithm in some detail.
;; A constant added to the generated field names to let us know that
;; we generated them
(define boost-id "__bmlboost")
;; The names of the fields contain ground truth - if there are k
;; classes, this is k coluns, one for each class. If the true class
#!/bin/bash
# Set credentials for BigML and for the US Census
BIGML_USERNAME=bigml
BIGML_API_KEY=****
CENSUS_API_KEY=****
BIGML_AUTH="username=$BIGML_USERNAME;api_key=$BIGML_API_KEY"
# Download demographic data into files. Have to do it this way
# because the census API doesn't seem to allow returns of more