Challenge Information
- Advent of Cyber Day 14
- THM link here
Explanation
Today’s topic is about Machine learning which I have no idea. There are alot of theory so make sure to read those yourself.
Question 1: What is the other term given for Artificial Intelligence or the subset of AI meant to teach computers how humans think or nature works?
The answer is Machine Learning
Question 2: What ML structure aims to mimic the process of natural selection and evolution?
The answer is Genetic Algorithm
Question 3: What is the name of the learning style that makes use of labelled data to train an ML structure?
The answer is supervised learning
Question 4: What is the name of the layer between the Input and Output layers of a Neural Network?
The answer is Hidden Layer
Question 5: What is the name of the process used to provide feedback to the Neural Network on how close its prediction was?
The answer is Back-Propagation
Question 6: What is the value of the flag you received after achieving more than 90% accuracy on your submitted predictions?
To get the prediction, modify the detector.py script into the script below.
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#These are the imports that we need for our Neural Network
#Numpy is a powerful array and matrix library used to format our data
import numpy as np
#Pandas is a machine learning library that also allows for reading and formatting data structures
import pandas as pd
#This will be used to split our data
from sklearn.model_selection import train_test_split
#This is used to normalize our data
from sklearn.preprocessing import StandardScaler
#This is used to encode our text data to integers
from sklearn.preprocessing import LabelEncoder
#This is our Multi-Layer Perceptron Neural Network
from sklearn.neural_network import MLPClassifier
#These are the colour labels that we will convert to int
colours = ["Red", "Blue", "Green", "Yellow", "Pink", "Purple", "Orange"]
#Read the training and testing data files
training_data = pd.read_csv("training_dataset.csv")
training_data.head()
testing_data = pd.read_csv("testing_dataset.csv")
testing_data.head()
#The Neural Network cannot take Strings as input, therefore we will encode the strings as integers
encoder = LabelEncoder()
encoder.fit(training_data["Colour Scheme"])
training_data["Colour Scheme"] = encoder.transform(training_data["Colour Scheme"])
testing_data["Colour Scheme"] = encoder.transform(testing_data["Colour Scheme"])
#Read our training data from the CSV file.
#First we read the data we will train on
X = np.asanyarray(training_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
#Now we read the labels of our training data
y = np.asanyarray(training_data['Defective'].astype('int'))
#Read our testing data
test_X = np.asanyarray(testing_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
###### INSERT DATASET SPLIT CODE HERE ######
train_X, validate_X, train_y, validate_y = train_test_split(X, y, test_size=0.2)
#Now we need to split our training dataset here
print ("Sample of our data:")
print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
###### INSERT NORMALISATION CODE HERE ######
scaler = StandardScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)
validate_X = scaler.transform(validate_X)
test_X = scaler.transform(test_X)
#Now we normalize our dataset
print ("Sampe of our data after normalization:")
print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
##### INSERT CLASSIFIER CODE HERE ######
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(15, 2), max_iter=10000)
print ("Starting to training our Neural Network")
###### INSERT CLASSIFIER TRAINING CODE HERE ######
clf.fit(train_X, train_y)
###### INSERT CLASSIFIER VALIDATION PREDICTION CODE HERE #######
y_predicted = clf.predict(validate_X)
#This function tests how well your Neural Network performs with the validation dataset
count_correct = 0
count_incorrect = 0
for x in range(len(y_predicted)):
if (y_predicted[x] == validate_y[x]):
count_correct += 1
else:
count_incorrect += 1
print ("Training has been completed, validating neural network now....")
print ("Total Correct:\t\t" + str(count_correct))
print ("Total Incorrect:\t" + str(count_incorrect))
accuracy = ((count_correct * 1.0) / (1.0 * (count_correct + count_incorrect)))
print ("Network Accuracy:\t" + str(accuracy * 100) + "%")
print ("Now we will predict the testing dataset for which we don't have the answers for...")
###### INSERT CLASSIFIER TESTING PREDICTION CODE HERE ######
y_test_predictions = clf.predict(test_X)
#This function will save your predictions to a textfile that can be uploaded for scoring
print ("Saving predictions to a file")
output = open("predictions.txt", 'w')
for value in y_test_predictions:
output.write(str(value) + "\n")
print ("Predictions are saved, this file can now be uploaded to verify your Neural Network")
output.close()
After modifying it, upload the result into the given link and you should have a flag.
flag:THM{Neural.Networks.are.Neat!}
Things I learned from the challenge
- Machine Learning