Deep Learning from first principles in Python, R and Octave – Part 1

“You don’t perceive objects as they are. You perceive them as you are.”
“Your interpretation of physical objects has everything to do with the historical trajectory of your brain – and little to do with the objects themselves.”
“The brain generates its own reality, even before it receives information coming in from the eyes and the other senses. This is known as the internal model”

                          David Eagleman - The Brain: The Story of You

This is the first in the series of posts, I intend to write on Deep Learning. This post is inspired by the Deep Learning Specialization by Prof Andrew Ng on Coursera and Neural Networks for Machine Learning by Prof Geoffrey Hinton also on Coursera. In this post I implement Logistic regression with a 2 layer Neural Network i.e. a Neural Network that just has an input layer and an output layer and with no hidden layer.I am certain that any self-respecting Deep Learning/Neural Network would consider a Neural Network without hidden layers as no Neural Network at all!

This 2 layer network is implemented in Python, R and Octave languages. I have included Octave, into the mix, as Octave is a close cousin of Matlab. These implementations in Python, R and Octave are equivalent vectorized implementations. So, if you are familiar in any one of the languages, you should be able to look at the corresponding code in the other two. You can download this R Markdown file and Octave code from DeepLearning -Part 1

Check out my video presentation which discusses the derivations in detail
1. Elements of Neural Networks and Deep Le- Part 1
2. Elements of Neural Networks and Deep Learning – Part 2

To start with, Logistic Regression is performed using sklearn’s logistic regression package for the cancer data set also from sklearn. This is shown below

1. Logistic Regression

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification, make_blobs

from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap
(X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)
X_train, X_test, y_train, y_test = train_test_split(X_cancer, y_cancer,
random_state = 0)
# Call the Logisitic Regression function
clf = LogisticRegression().fit(X_train, y_train)
print('Accuracy of Logistic regression classifier on training set: {:.2f}'
.format(clf.score(X_train, y_train)))
print('Accuracy of Logistic regression classifier on test set: {:.2f}'
.format(clf.score(X_test, y_test)))
## Accuracy of Logistic regression classifier on training set: 0.96
## Accuracy of Logistic regression classifier on test set: 0.96

To check on other classification algorithms, check my post Practical Machine Learning with R and Python – Part 2.

Checkout my book ‘Deep Learning from first principles: Second Edition – In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($14.99) and in kindle version($9.99/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python:Second Edition- Machine Learning in stereo” available in Amazon in paperback($10.99) and Kindle($7.99/Rs449) versions. This book is ideal for a quick reference of the various ML functions and associated measurements in both R and Python which are essential to delve deep into Deep Learning.

2. Logistic Regression as a 2 layer Neural Network

In the following section Logistic Regression is implemented as a 2 layer Neural Network in Python, R and Octave. The same cancer data set from sklearn will be used to train and test the Neural Network in Python, R and Octave. This can be represented diagrammatically as below

The cancer data set has 30 input features, and the target variable ‘output’ is either 0 or 1. Hence the sigmoid activation function will be used in the output layer for classification.

This simple 2 layer Neural Network is shown below
At the input layer there are 30 features and the corresponding weights of these inputs which are initialized to small random values.
$Z= w_{1}x_{1} +w_{2}x_{2} +..+ w_{30}x_{30} + b$
where ‘b’ is the bias term

The Activation function is the sigmoid function which is $a= 1/(1+e^{-z})$
The Loss, when the sigmoid function is used in the output layer, is given by
$L=-(ylog(a) + (1-y)log(1-a))$ (1)

Forward propagation

In forward propagation cycle of the Neural Network the output Z and the output of activation function, the sigmoid function, is first computed. Then using the output ‘y’ for the given features, the ‘Loss’ is computed using equation (1) above.

Backward propagation

The backward propagation cycle determines how the ‘Loss’ is impacted for small variations from the previous layers upto the input layer. In other words, backward propagation computes the changes in the weights at the input layer, which will minimize the loss. Several cycles of gradient descent are performed in the path of steepest descent to find the local minima. In other words the set of weights and biases, at the input layer, which will result in the lowest loss is computed by gradient descent. The weights at the input layer are decreased by a parameter known as the ‘learning rate’. Too big a ‘learning rate’ can overshoot the local minima, and too small a ‘learning rate’ can take a long time to reach the local minima. This is done for ‘m’ training examples.

Chain rule of differentiation
Let y=f(u)
and u=g(x) then
$\partial y/\partial x = \partial y/\partial u * \partial u/\partial x$

Derivative of sigmoid
$\sigma=1/(1+e^{-z})$
Let $x= 1 + e^{-z}$  then
$\sigma = 1/x$
$\partial \sigma/\partial x = -1/x^{2}$
$\partial x/\partial z = -e^{-z}$
Using the chain rule of differentiation we get
$\partial \sigma/\partial z = \partial \sigma/\partial x * \partial x/\partial z$
$=-1/(1+e^{-z})^{2}* -e^{-z} = e^{-z}/(1+e^{-z})^{2}$
Therefore $\partial \sigma/\partial z = \sigma(1-\sigma)$        -(2)

The 3 equations for the 2 layer Neural Network representation of Logistic Regression are
$L=-(y*log(a) + (1-y)*log(1-a))$      -(a)
$a=1/(1+e^{-Z})$      -(b)
$Z= w_{1}x_{1} +w_{2}x_{2} +...+ w_{30}x_{30} +b = Z = \sum_{i} w_{i}*x_{i} + b$ -(c)

The back propagation step requires the computation of $dL/dw_{i}$ and $dL/db_{i}$. In the case of regression it would be $dE/dw_{i}$ and $dE/db_{i}$ where dE is the Mean Squared Error function.
Computing the derivatives for back propagation we have
$dL/da = -(y/a + (1-y)/(1-a))$          -(d)
because $d/dx(logx) = 1/x$
Also from equation (2) we get
$da/dZ = a (1-a)$                                  – (e)
By chain rule
$\partial L/\partial Z = \partial L/\partial a * \partial a/\partial Z$
therefore substituting the results of (d) & (e) we get
$\partial L/\partial Z = -(y/a + (1-y)/(1-a)) * a(1-a) = a-y$         (f)
Finally
$\partial L/\partial w_{i}= \partial L/\partial a * \partial a/\partial Z * \partial Z/\partial w_{i}$                                                           -(g)
$\partial Z/\partial w_{i} = x_{i}$            – (h)
and from (f) we have  $\partial L/\partial Z =a-y$
Therefore  (g) reduces to
$\partial L/\partial w_{i} = x_{i}* (a-y)$ -(i)
Also
$\partial L/\partial b = \partial L/\partial a * \partial a/\partial Z * \partial Z/\partial b$ -(j)
Since
$\partial Z/\partial b = 1$ and using (f) in (j)
$\partial L/\partial b = a-y$

The gradient computes the weights at the input layer and the corresponding bias by using the values
of $dw_{i}$ and $db$
$w_{i} := w_{i} -\alpha * dw_{i}$
$b := b -\alpha * db$
I found the computation graph representation in the book Deep Learning: Ian Goodfellow, Yoshua Bengio, Aaron Courville, very useful to visualize and also compute the backward propagation. For the 2 layer Neural Network of Logistic Regression the computation graph is shown below

3. Neural Network for Logistic Regression -Python code (vectorized)

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

# Define the sigmoid function
def sigmoid(z):
a=1/(1+np.exp(-z))
return a

# Initialize
def initialize(dim):
w = np.zeros(dim).reshape(dim,1)
b = 0
return w

# Compute the loss
def computeLoss(numTraining,Y,A):
loss=-1/numTraining *np.sum(Y*np.log(A) + (1-Y)*(np.log(1-A)))
return(loss)

# Execute the forward propagation
def forwardPropagation(w,b,X,Y):
# Compute Z
Z=np.dot(w.T,X)+b
# Determine the number of training samples
numTraining=float(len(X))
# Compute the output of the sigmoid activation function
A=sigmoid(Z)
#Compute the loss
loss = computeLoss(numTraining,Y,A)
# Compute the gradients dZ, dw and db
dZ=A-Y
dw=1/numTraining*np.dot(X,dZ.T)
db=1/numTraining*np.sum(dZ)

# Return the results as a dictionary
"db": db}
loss = np.squeeze(loss)

def gradientDescent(w, b, X, Y, numIerations, learningRate):
losses=[]
idx =[]
# Iterate
for i in range(numIerations):
#Get the derivates
w = w-learningRate*dw
b = b-learningRate*db

# Store the loss
if i % 100 == 0:
idx.append(i)
losses.append(loss)
params = {"w": w,
"b": b}
"db": db}

# Predict the output for a training set
def predict(w,b,X):
size=X.shape[1]
yPredicted=np.zeros((1,size))
Z=np.dot(w.T,X)
# Compute the sigmoid
A=sigmoid(Z)
for i in range(A.shape[1]):
#If the value is > 0.5 then set as 1
if(A[0][i] > 0.5):
yPredicted[0][i]=1
else:
# Else set as 0
yPredicted[0][i]=0

return yPredicted

#Normalize the data
def normalize(x):
x_norm = None
x_norm = np.linalg.norm(x,axis=1,keepdims=True)
x= x/x_norm
return x

# Run the 2 layer Neural Network on the cancer data set

(X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)
# Create train and test sets
X_train, X_test, y_train, y_test = train_test_split(X_cancer, y_cancer,
random_state = 0)
# Normalize the data for better performance
X_train1=normalize(X_train)

# Create weight vectors of zeros. The size is the number of features in the data set=30
w=np.zeros((X_train.shape[1],1))
#w=np.zeros((30,1))
b=0

#Normalize the training data so that gradient descent performs better
X_train1=normalize(X_train)
#Transpose X_train so that we have a matrix as (features, numSamples)
X_train2=X_train1.T

# Reshape to remove the rank 1 array and then transpose
y_train1=y_train.reshape(len(y_train),1)
y_train2=y_train1.T

# Run gradient descent for 4000 times and compute the weights
w = parameters["w"]
b = parameters["b"]

# Normalize X_test
X_test1=normalize(X_test)
#Transpose X_train so that we have a matrix as (features, numSamples)
X_test2=X_test1.T

#Reshape y_test
y_test1=y_test.reshape(len(y_test),1)
y_test2=y_test1.T

# Predict the values for
yPredictionTest = predict(w, b, X_test2)
yPredictionTrain = predict(w, b, X_train2)

# Print the accuracy
print("train accuracy: {} %".format(100 - np.mean(np.abs(yPredictionTrain - y_train2)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(yPredictionTest - y_test)) * 100))

# Plot the Costs vs the number of iterations
fig1=plt.plot(idx,costs)
fig1=plt.title("Gradient descent-Cost vs No of iterations")
fig1=plt.xlabel("No of iterations")
fig1=plt.ylabel("Cost")
fig1.figure.savefig("fig1", bbox_inches='tight')
## train accuracy: 90.3755868545 %
## test accuracy: 89.5104895105 %

Note: It can be seen that the Accuracy on the training and test set is 90.37% and 89.51%. This is comparatively poorer than the 96% which the logistic regression of sklearn achieves! But this is mainly because of the absence of hidden layers which is the real power of neural networks.

4. Neural Network for Logistic Regression -R code (vectorized)

source("RFunctions-1.R")
# Define the sigmoid function
sigmoid <- function(z){
a <- 1/(1+ exp(-z))
a
}

# Compute the loss
computeLoss <- function(numTraining,Y,A){
loss <- -1/numTraining* sum(Y*log(A) + (1-Y)*log(1-A))
return(loss)
}

# Compute forward propagation
forwardPropagation <- function(w,b,X,Y){
# Compute Z
Z <- t(w) %*% X +b
#Set the number of samples
numTraining <- ncol(X)
# Compute the activation function
A=sigmoid(Z)

#Compute the loss
loss <- computeLoss(numTraining,Y,A)

# Compute the gradients dZ, dw and db
dZ<-A-Y
dw<-1/numTraining * X %*% t(dZ)
db<-1/numTraining*sum(dZ)

fwdProp <- list("loss" = loss, "dw" = dw, "db" = db)
return(fwdProp)
}

# Perform one cycle of Gradient descent
gradientDescent <- function(w, b, X, Y, numIerations, learningRate){
losses <- NULL
idx <- NULL
# Loop through the number of iterations
for(i in 1:numIerations){
fwdProp <-forwardPropagation(w,b,X,Y)
#Get the derivatives
dw <- fwdProp$dw db <- fwdProp$db
w = w-learningRate*dw
b = b-learningRate*db
l <- fwdProp$loss # Stoe the loss if(i %% 100 == 0){ idx <- c(idx,i) losses <- c(losses,l) } } # Return the weights and losses gradDescnt <- list("w"=w,"b"=b,"dw"=dw,"db"=db,"losses"=losses,"idx"=idx) return(gradDescnt) } # Compute the predicted value for input predict <- function(w,b,X){ m=dim(X)[2] # Create a ector of 0's yPredicted=matrix(rep(0,m),nrow=1,ncol=m) Z <- t(w) %*% X +b # Compute sigmoid A=sigmoid(Z) for(i in 1:dim(A)[2]){ # If A > 0.5 set value as 1 if(A[1,i] > 0.5) yPredicted[1,i]=1 else # Else set as 0 yPredicted[1,i]=0 } return(yPredicted) } # Normalize the matrix normalize <- function(x){ #Create the norm of the matrix.Perform the Frobenius norm of the matrix n<-as.matrix(sqrt(rowSums(x^2))) #Sweep by rows by norm. Note '1' in the function which performing on every row normalized<-sweep(x, 1, n, FUN="/") return(normalized) } # Run the 2 layer Neural Network on the cancer data set # Read the data (from sklearn) cancer <- read.csv("cancer.csv") # Rename the target variable names(cancer) <- c(seq(1,30),"output") # Split as training and test sets train_idx <- trainTestSplit(cancer,trainPercent=75,seed=5) train <- cancer[train_idx, ] test <- cancer[-train_idx, ] # Set the features X_train <-train[,1:30] y_train <- train[,31] X_test <- test[,1:30] y_test <- test[,31] # Create a matrix of 0's with the number of features w <-matrix(rep(0,dim(X_train)[2])) b <-0 X_train1 <- normalize(X_train) X_train2=t(X_train1) # Reshape then transpose y_train1=as.matrix(y_train) y_train2=t(y_train1) # Perform gradient descent gradDescent= gradientDescent(w, b, X_train2, y_train2, numIerations=3000, learningRate=0.77) # Normalize X_test X_test1=normalize(X_test) #Transpose X_train so that we have a matrix as (features, numSamples) X_test2=t(X_test1) #Reshape y_test and take transpose y_test1=as.matrix(y_test) y_test2=t(y_test1) # Use the values of the weights generated from Gradient Descent yPredictionTest = predict(gradDescent$w, gradDescent$b, X_test2) yPredictionTrain = predict(gradDescent$w, gradDescent$b, X_train2) sprintf("Train accuracy: %f",(100 - mean(abs(yPredictionTrain - y_train2)) * 100)) ## [1] "Train accuracy: 90.845070" sprintf("test accuracy: %f",(100 - mean(abs(yPredictionTest - y_test)) * 100)) ## [1] "test accuracy: 87.323944" df <-data.frame(gradDescent$idx, gradDescent\$losses)
names(df) <- c("iterations","losses")
ggplot(df,aes(x=iterations,y=losses)) + geom_point() + geom_line(col="blue") +
ggtitle("Gradient Descent - Losses vs No of Iterations") +
xlab("No of iterations") + ylab("Losses")

4. Neural Network for Logistic Regression -Octave code (vectorized)

 1; # Define sigmoid function function a = sigmoid(z) a = 1 ./ (1+ exp(-z)); end # Compute the loss function loss=computeLoss(numtraining,Y,A) loss = -1/numtraining * sum((Y .* log(A)) + (1-Y) .* log(1-A)); end
 # Perform forward propagation function [loss,dw,db,dZ] = forwardPropagation(w,b,X,Y) % Compute Z Z = w' * X + b; numtraining = size(X)(1,2); # Compute sigmoid A = sigmoid(Z);
 #Compute loss. Note this is element wise product loss =computeLoss(numtraining,Y,A); # Compute the gradients dZ, dw and db dZ = A-Y; dw = 1/numtraining* X * dZ'; db =1/numtraining*sum(dZ);

end
 # Compute Gradient Descent function [w,b,dw,db,losses,index]=gradientDescent(w, b, X, Y, numIerations, learningRate) #Initialize losses and idx losses=[]; index=[]; # Loop through the number of iterations for i=1:numIerations, [loss,dw,db,dZ] = forwardPropagation(w,b,X,Y); # Perform Gradient descent w = w - learningRate*dw; b = b - learningRate*db; if(mod(i,100) ==0) # Append index and loss index = [index i]; losses = [losses loss]; endif

end
end
 # Determine the predicted value for dataset function yPredicted = predict(w,b,X) m = size(X)(1,2); yPredicted=zeros(1,m); # Compute Z Z = w' * X + b; # Compute sigmoid A = sigmoid(Z); for i=1:size(X)(1,2), # Set predicted as 1 if A > 0,5 if(A(1,i) >= 0.5) yPredicted(1,i)=1; else yPredicted(1,i)=0; endif end end
 # Normalize by dividing each value by the sum of squares function normalized = normalize(x) # Compute Frobenius norm. Square the elements, sum rows and then find square root a = sqrt(sum(x .^ 2,2)); # Perform element wise division normalized = x ./ a; end
 # Split into train and test sets function [X_train,y_train,X_test,y_test] = trainTestSplit(dataset,trainPercent) # Create a random index ix = randperm(length(dataset)); # Split into training trainSize = floor(trainPercent/100 * length(dataset)); train=dataset(ix(1:trainSize),:); # And test test=dataset(ix(trainSize+1:length(dataset)),:); X_train = train(:,1:30); y_train = train(:,31); X_test = test(:,1:30); y_test = test(:,31); end

 cancer=csvread("cancer.csv"); [X_train,y_train,X_test,y_test] = trainTestSplit(cancer,75); w=zeros(size(X_train)(1,2),1); b=0; X_train1=normalize(X_train); X_train2=X_train1'; y_train1=y_train'; [w1,b1,dw,db,losses,idx]=gradientDescent(w, b, X_train2, y_train1, numIerations=3000, learningRate=0.75); # Normalize X_test X_test1=normalize(X_test); #Transpose X_train so that we have a matrix as (features, numSamples) X_test2=X_test1'; y_test1=y_test'; # Use the values of the weights generated from Gradient Descent yPredictionTest = predict(w1, b1, X_test2); yPredictionTrain = predict(w1, b1, X_train2); 

 trainAccuracy=100-mean(abs(yPredictionTrain - y_train1))*100 testAccuracy=100- mean(abs(yPredictionTest - y_test1))*100 trainAccuracy = 90.845 testAccuracy = 89.510 graphics_toolkit('gnuplot') plot(idx,losses); title ('Gradient descent- Cost vs No of iterations'); xlabel ("No of iterations"); ylabel ("Cost");

Conclusion
This post starts with a simple 2 layer Neural Network implementation of Logistic Regression. Clearly the performance of this simple Neural Network is comparatively poor to the highly optimized sklearn’s Logistic Regression. This is because the above neural network did not have any hidden layers. Deep Learning & Neural Networks achieve extraordinary performance because of the presence of deep hidden layers

The Deep Learning journey has begun… Don’t miss the bus!
Stay tuned for more interesting posts in Deep Learning!!

To see all posts check Index of posts

28 thoughts on “Deep Learning from first principles in Python, R and Octave – Part 1”

1. Dan says:

This is really well done. Nice to open up the black box and show everything all the way down to reminding us of the chain rule from high school!

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2. eafpres says:

Hi–I’m having trouble due to the “source” command; where can I find the “RFunctions-1.R”?

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3. One more challenge I’m having. I’m not sure the data are in the correct structure. When I download the file there are 32 columns. The first is an ID, and the second is a string value for the diagnosis, and the rest are predictors. The code seems to think there are 31 columns and the last is the outcome. Can you clarify for me? Thanks again.

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1. I sorted it out. Converted the outcome to binary, put in last column, and removed 1st 2 and it runs as per your examples.

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4. Franco says:

Hi!
It seams the normalization is calculated column wise, if so can anyone explain why?

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1. Franco – All the features are in columns. Normalization tries to make the values between 0 – 1 for all features and hence is column wise. Gradient Descent performs better when features are normalized. Ganesh

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1. Franco says:

I was making a mistake, calculating a L2 norm column wise… Now I got it…
Thank you Ganesh,
Franco

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