sbugrov

# -*- coding: utf-8 -*-

# Sergei Bugrov
# 7-9-17
#
# Downloads all available books in English language in .txt format from http://www.gutenberg.org,
# unpacks them from .zip archives, saves them to ../books/ folder, and deletes .zip files.
#
# usage : python gutenberg.py
#

sbugrov

vector <float> sigmoid (const vector <float>& m1) {
    
    /*  Returns the value of the sigmoid function f(x) = 1/(1 + e^-x).
        Input: m1, a vector.
        Output: 1/(1 + e^-x) for every element of the input matrix m1.
    */
    
    const unsigned long VECTOR_SIZE = m1.size();
    vector <float> output (VECTOR_SIZE);
    

sbugrov

//
//  main.cpp
//  mlperceptron
//
//  Created by Sergei Bugrov on 7/1/17.
//  Copyright © 2017 Sergei Bugrov. All rights reserved.
//

#include <iostream>
#include <vector>

sbugrov

int main(int argc, const char * argv[]) {    
    for (unsigned i = 0; i != 50; ++i) {        
        vector<float> pred = sigmoid(dot(X, W, 4, 4, 1 ) );
        vector<float> pred_error = y - pred;       
        vector<float> pred_delta = pred_error * sigmoid_d(pred);       
        vector<float> W_delta = dot(transpose( &X[0], 4, 4 ), pred_delta, 4, 4, 1);      
        W = W + W_delta;
    };
    return 0;
}

sbugrov

//
//  nn.cpp
//  
//  To compile: g++ -o nn nn.cpp -std=c++11
//  To run: ./nn
//  Created by Sergei Bugrov on 4/20/18.
//  Copyright © 2017 Sergei Bugrov. All rights reserved.
//  Download dataset from: https://drive.google.com/file/d/1OdtwXHf_-2T0aS9HLBnxU3o-72mklCZY/view?usp=sharing

#include <iostream>

sbugrov

//
//  onehiddenlayerperceptron.cu
//  onehiddenlayerperceptron
//
//  Created by Sergei Bugrov on 8/21/17.
//  Copyright © 2017 Sergei Bugrov. All rights reserved.
//

#include <stdio.h>

sbugrov

__global__ void kFit(	const float* X, const int X_w, const int X_h, const float* y, const int y_w, float* l1, const int l1_w, float* l_1_d, float* pred, float* pred_d, float* W0, float* W1, float* buffer) {
	for (unsigned i = 0; i < 50; ++i) {
        dSigmoid(dDot(X, W0, l1, X_h, X_w, l1_w), l1, X_h, l1_w);
        dSigmoid(dDot(l1, W1, pred, X_h, l1_w, y_w), pred, X_h, y_w);
        dMartixByMatrixElementwise(dMartixSubstractMatrix(y, pred, pred_d, X_h, y_w), dSigmoid_d(pred, buffer, X_h, y_w), pred_d, X_h, y_w );
        dMartixByMatrixElementwise(dDot_m1_m2T(pred_d, W1, l_1_d, X_h, y_w, l1_w), dSigmoid_d(l1, buffer, X_h, l1_w), l_1_d, X_h, l1_w);
        dDot_m1T_m2( l1, pred_d, W1, X_h, l1_w, y_w );
        dDot_m1T_m2( X, l_1_d, W0, X_h, X_w, l1_w );
    }
}

sbugrov

// Updating the parameters
W3 = W3 - lr * dW3;
W2 = W2 - lr * dW2;
W1 = W1 - lr * dW1;

sbugrov

// dW1 = X.T * dz1
vector<float> dW1 = dot(transpose( &b_X[0], BATCH_SIZE, 784 ), dz1, 784, BATCH_SIZE, 128);

sbugrov

// dz1 = dz2 * W2.T * relu'(a1)
vector<float> dz1 = dot(dz2, transpose( &W2[0], 128, 64 ), BATCH_SIZE, 64, 128) * reluPrime(a1);