alecGraves

What?

Python, Keras, and Tensorflow have made neural networks easy and accessable to everyone. I personally have had a lot of trouble finding a nice and easy guide detailing how to set up all three on a system. This guide contains simple, step-by-step instructions on how to install these three things.

1. Anaconda

Anaconda is a python package manager that does a lot of stuff for you. It contains many packages including pip, numpy, scipy, etc.

Step one is to install it. Do that by going here, downloading the package for your OS, and installing it.

alecGraves

echo -n | openssl s_client -connect services.gradle.org:443 | \
   sed -ne '/-BEGIN CERTIFICATE-/,/-END CERTIFICATE-/p' > /tmp/cert.crt && \
sudo keytool -import -trustcacerts -keystore /usr/lib/jvm/default-java/jre/lib/security/cacerts \
   -keyalg rsa -storepass changeit -noprompt -alias mycert -file /tmp/cert.crt && \
rm /tmp/cert.crt

alecGraves

import keras.backend as K

def FScore2(y_true, y_pred):
    '''
    The F score, beta=2
    '''
    B2 = K.variable(4)
    OnePlusB2 = K.variable(5)
    pred = K.round(y_pred)
    tp = K.sum(K.cast(K.less(K.abs(pred - K.clip(y_true, .5, 1.)), 0.01), 'float32'), -1)

alecGraves

struct Student { 
	int n, c[9], g[9];
	void SetGrades(int *C, char *G, int N){
		for (int i(0); i < N; ++i){
			AddGrade(C[i], G[i]);
		}
	}
	void AddGrade(int C, char G){
		c[n] = C;
		g[n++] = G;

alecGraves

#include <cstdio>
class Rational{
    int n, d; //numerator, denominator
    int gcf(int x, int y, int z){ return y&&(z=x%y) ? gcf(y,z,0) : y;}
    void r(){int f=gcf(n, d, 0); n/=f; d/=f;} //reduce
public:
    Rational operator+(Rational R){return Rational(n*R.d+d*R.n, d*R.d);}
    Rational operator-(Rational R){return Rational(n*R.d-d*R.n, d*R.d);}
    Rational operator*(Rational R){return Rational(n*R.n, d*R.d);}
    Rational operator/(Rational R){return Rational(n*R.d, d*R.n);}

alecGraves

;TI-84 Plus CE Include File

;Various Parts Contributed by
;- BrandonW
;- calc84
;- MateoConLechuga
;- Runer112
;- tr1p1ea
;- Kerm Martian
;- Texas Instruments (ti83plus.inc)

alecGraves

function net = resnet(input_shape)
%RESNET Creates a resnet model with imput_size
n = imageInputLayer(input_shape,'Name','input');
n = layerGraph(n);
n = bottleneck(256, ['bottleneck_' char(string(1))], n)

end

function graph = bottleneck(depth, name, graph)
head = graph.Layers(end).Name;

alecGraves

const int MPIN = 2; //motor pwm out pin
const int DPIN = A0; // voltage distance sensor pin

float steady_state_error;
float voltage;
float target;
float accumulate;

void setup()
{

alecGraves

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import initializers
slim = tf.contrib.slim

'''
============================================================================
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
============================================================================
Based on the paper: https://arxiv.org/pdf/1606.02147.pdf
'''

alecGraves

import tensorflow as tf

def prelu(x, scope, decoder=False):
    '''
    Performs the parametric relu operation. This implementation is based on:
    https://stackoverflow.com/questions/39975676/how-to-implement-prelu-activation-in-tensorflow
    For the decoder portion, prelu becomes just a normal prelu
    INPUTS:
    - x(Tensor): a 4D Tensor that undergoes prelu
    - scope(str): the string to name your prelu operation's alpha variable.