Deep Learning from first principles in Python, R and Octave – Part 7

Artificial Intelligence is the new electricity. – Prof Andrew Ng

Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI. – Yann LeCun, March 14, 2016 (Facebook)

Introduction

In this post ‘Deep Learning from first principles with Python, R and Octave-Part 7’, I implement optimization methods used in Stochastic Gradient Descent (SGD) to speed up the convergence. Specifically I discuss and implement the following gradient descent optimization techniques

a.Vanilla Stochastic Gradient Descent
b.Learning rate decay
c. Momentum method
d. RMSProp
e. Adaptive Moment Estimation (Adam)

This post, further enhances my generic L-Layer Deep Learning Network implementations in vectorized Python, R and Octave to also include the Stochastic Gradient Descent optimization techniques. You can clone/download the code from Github at DeepLearning-Part7

You can view my video presentation on Gradient Descent Optimization in Neural Networks 7

Incidentally, a good discussion of the various optimizations methods used in Stochastic Gradient Optimization techniques can be seen at Sebastian Ruder’s blog

Note: In the vectorized Python, R and Octave implementations below only a 1024 random training samples were used. This was to reduce the computation time. You are free to use the entire data set (60000 training data) for the computation.

This post is largely based of on Prof Andrew Ng’s Deep Learning Specialization. All the above optimization techniques for Stochastic Gradient Descent are based on the technique of exponentially weighted average method. So for example if we had some time series data $\theta_{1},\theta_{2},\theta_{3}... \theta_{t}$ then we we can represent the exponentially average value at time ‘t’ as a sequence of the the previous value $v_{t-1}$ and $\theta_{t}$ as shown below
$v_{t} = \beta v_{t-1} + (1-\beta)\theta_{t}$

Here $v_{t}$ represent the average of the data set over $\frac {1}{1-\beta}$ By choosing different values of $\beta$ , we can average over a larger or smaller number of the data points.
We can write the equations as follows
$v_{t} = \beta v_{t-1} + (1-\beta)\theta_{t}$
$v_{t-1} = \beta v_{t-2} + (1-\beta)\theta_{t-1}$
$v_{t-2} = \beta v_{t-3} + (1-\beta)\theta_{t-2}$
and
$v_{t-k} = \beta v_{t-(k+1))} + (1-\beta)\theta_{t-k}$
By substitution we have
$v_{t} = (1-\beta)\theta_{t} + \beta v_{t-1}$
$v_{t} = (1-\beta)\theta_{t} + \beta ((1-\beta)\theta_{t-1}) + \beta v_{t-2}$
$v_{t} = (1-\beta)\theta_{t} + \beta ((1-\beta)\theta_{t-1}) + \beta ((1-\beta)\theta_{t-2}+ \beta v_{t-3} )$

Hence it can be seen that the $v_{t}$ is the weighted sum over the previous values $\theta_{k}$ , which is an exponentially decaying function.

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 ($18.99) and in kindle version($9.99/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python- Machine Learning in stereo” available in Amazon in paperback($9.99) and Kindle($6.99) 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.

1.1a. Stochastic Gradient Descent (Vanilla) – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read the training data
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of 1024 random numbers.
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
# Set the layer dimensions  
layersDimensions=[784, 15,9,10] 
# Perform SGD with regular gradient descent
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="gd",
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig1.png")

1.1b. Stochastic Gradient Descent (Vanilla) – R

source("mnist.R")
source("DLfunctions7.R")
#Load and read MNIST data
load_mnist() 
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
# Set layer dimensions
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with regular gradient descent
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                            hiddenActivationFunc='tanh',
                            outputActivationFunc="softmax",
                            learningRate = 0.05,
                            optimizer="gd",
                            mini_batch_size = 512, 
                            num_epochs = 5000, 
                            print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs no of epochs") + xlab("No of epochss") + ylab("Cost")

1.1c. Stochastic Gradient Descent (Vanilla) – Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with regular gradient descent
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.005,
 lrDecay=true, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="gd",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);

plotCostVsEpochs(5000,costs);

2.1. Stochastic Gradient Descent with Learning rate decay

Since in Stochastic Gradient Descent,with each epoch, we use slight different samples, the gradient descent algorithm, oscillates across the ravines and wanders around the minima, when a fixed learning rate is used. In this technique of ‘learning rate decay’ the learning rate is slowly decreased with the number of epochs and becomes smaller and smaller, so that gradient descent can take smaller steps towards the minima.

There are several techniques employed in learning rate decay

a) Exponential decay: $\alpha = decayRate^{epochNum} *\alpha_{0}$
b) 1/t decay : $\alpha = \frac{\alpha_{0}}{1 + decayRate*epochNum}$
c) $\alpha = \frac {decayRate}{\sqrt(epochNum)}*\alpha_{0}$

In my implementation I have used the ‘exponential decay’. The code snippet for Python is shown below

if lrDecay == True:
   learningRate = np.power(decayRate,(num_epochs/1000)) * learningRate

2.1a. Stochastic Gradient Descent with Learning rate decay – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read the MNIST data
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
# Set layer dimensions
layersDimensions=[784, 15,9,10] 
# Perform SGD with learning rate decay
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",
                                   learningRate = 0.01 , lrDecay=True, decayRate=0.9999,
                                   optimizer="gd",
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig2.png")

2.1b. Stochastic Gradient Descent with Learning rate decay – R

source("mnist.R")
source("DLfunctions7.R")
# Read and load MNIST
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
# Set layer dimensions
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with Learning rate decay
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.05,
                                  lrDecay=TRUE,
                                  decayRate=0.9999,
                                  optimizer="gd",
                                  mini_batch_size = 512, 
                                  num_epochs = 5000, 
                                  print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

2.1c. Stochastic Gradient Descent with Learning rate decay – Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with regular Learning rate decay
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.01,
 lrDecay=true, 
 decayRate=0.999,
 lambd=0,
 keep_prob=1,
 optimizer="gd",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);
plotCostVsEpochs(5000,costs)

3.1. Stochastic Gradient Descent with Momentum

Stochastic Gradient Descent with Momentum uses the exponentially weighted average method discusses above and more generally moves faster into the ravine than across it. The equations are
$v_{dW}^l = \beta v_{dW}^l + (1-\beta)dW^{l}$
$v_{db}^l = \beta v_{db}^l + (1-\beta)db^{l}$
$W^{l} = W^{l} - \alpha v_{dW}^l$
$b^{l} = b^{l} - \alpha v_{db}^l$ where
$v_{dW}$ and $v_{db}$ are the momentum terms which are exponentially weighted with the corresponding gradients ‘dW’ and ‘db’ at the corresponding layer ‘l’ The code snippet for Stochastic Gradient Descent with momentum in R is shown below

# Perform Gradient Descent with momentum
# Input : Weights and biases
#       : beta
#       : gradients
#       : learning rate
#       : outputActivationFunc - Activation function at hidden layer sigmoid/softmax
#output : Updated weights after 1 iteration
gradientDescentWithMomentum  <- function(parameters, gradients,v, beta, learningRate,outputActivationFunc="sigmoid"){

    L = length(parameters)/2 # number of layers in the neural network    
    # Update rule for each parameter. Use a for loop.
    for(l in 1:(L-1)){
        # Compute velocities
        # v['dWk'] = beta *v['dWk'] + (1-beta)*dWk
        v[[paste("dW",l, sep="")]] = beta*v[[paste("dW",l, sep="")]] + 
                   (1-beta) * gradients[[paste('dW',l,sep="")]]
        v[[paste("db",l, sep="")]] = beta*v[[paste("db",l, sep="")]] + 
            (1-beta) * gradients[[paste('db',l,sep="")]]
        
        parameters[[paste("W",l,sep="")]] = parameters[[paste("W",l,sep="")]] -
            learningRate* v[[paste("dW",l, sep="")]] 
        parameters[[paste("b",l,sep="")]] = parameters[[paste("b",l,sep="")]] -
            learningRate* v[[paste("db",l, sep="")]] 
    }    
    # Compute for the Lth layer
    if(outputActivationFunc=="sigmoid"){
        v[[paste("dW",L, sep="")]] = beta*v[[paste("dW",L, sep="")]] + 
            (1-beta) * gradients[[paste('dW',L,sep="")]]
        v[[paste("db",L, sep="")]] = beta*v[[paste("db",L, sep="")]] + 
            (1-beta) * gradients[[paste('db',L,sep="")]]
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate* v[[paste("dW",l, sep="")]]  
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate* v[[paste("db",l, sep="")]]
        
    }else if (outputActivationFunc=="softmax"){
        v[[paste("dW",L, sep="")]] = beta*v[[paste("dW",L, sep="")]] + 
            (1-beta) * t(gradients[[paste('dW',L,sep="")]])
        v[[paste("db",L, sep="")]] = beta*v[[paste("db",L, sep="")]] + 
            (1-beta) * t(gradients[[paste('db',L,sep="")]])       
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate* t(gradients[[paste("dW",L,sep="")]])
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate* t(gradients[[paste("db",L,sep="")]])
    }
    return(parameters)
}

3.1a. Stochastic Gradient Descent with Momentum- Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
# Read and load data
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
layersDimensions=[784, 15,9,10] 
# Perform SGD with momentum
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="momentum", beta=0.9,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig3.png")

3.1b. Stochastic Gradient Descent with Momentum- R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with momentum
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.05,
                                  optimizer="momentum",
                                  beta=0.9,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000, 
                                  print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

3.1c. Stochastic Gradient Descent with Momentum- Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 60K
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with Momentum
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.01,
 lrDecay=false, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="momentum",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);

plotCostVsEpochs(5000,costs)

4.1. Stochastic Gradient Descent with RMSProp

Stochastic Gradient Descent with RMSProp tries to move faster towards the minima while dampening the oscillations across the ravine.
The equations are

$s_{dW}^l = \beta_{1} s_{dW}^l + (1-\beta_{1})(dW^{l})^{2}$
$s_{db}^l = \beta_{1} s_{db}^l + (1-\beta_{1})(db^{l})^2$
$W^{l} = W^{l} - \frac {\alpha s_{dW}^l}{\sqrt (s_{dW}^l + \epsilon) }$
$b^{l} = b^{l} - \frac {\alpha s_{db}^l}{\sqrt (s_{db}^l + \epsilon) }$
where $s_{dW}$ and $s_{db}$ are the RMSProp terms which are exponentially weighted with the corresponding gradients ‘dW’ and ‘db’ at the corresponding layer ‘l’

The code snippet in Octave is shown below

# Update parameters with RMSProp
# Input : parameters
#       : gradients
#       : s
#       : beta
#       : learningRate
#       : 
#output : Updated parameters RMSProp
function [weights biases] = gradientDescentWithRMSProp(weights, biases,gradsDW,gradsDB, sdW, sdB, beta1, epsilon, learningRate,outputActivationFunc="sigmoid")
    L = size(weights)(2); # number of layers in the neural network
    # Update rule for each parameter. 
    for l=1:(L-1)
        sdW{l} =  beta1*sdW{l} + (1 -beta1) * gradsDW{l} .* gradsDW{l};
        sdB{l} =  beta1*sdB{l} + (1 -beta1) * gradsDB{l} .* gradsDB{l};
        weights{l} = weights{l} - learningRate* gradsDW{l} ./ sqrt(sdW{l} + epsilon); 
        biases{l} = biases{l} -  learningRate* gradsDB{l} ./ sqrt(sdB{l} + epsilon);
    endfor
  
    if (strcmp(outputActivationFunc,"sigmoid"))
        sdW{L} =  beta1*sdW{L} + (1 -beta1) * gradsDW{L} .* gradsDW{L};
        sdB{L} =  beta1*sdB{L} + (1 -beta1) * gradsDB{L} .* gradsDB{L};
        weights{L} = weights{L} -learningRate* gradsDW{L} ./ sqrt(sdW{L} +epsilon); 
        biases{L} = biases{L} -learningRate* gradsDB{L} ./ sqrt(sdB{L} + epsilon);
     elseif (strcmp(outputActivationFunc,"softmax"))
        sdW{L} =  beta1*sdW{L} + (1 -beta1) * gradsDW{L}' .* gradsDW{L}';
        sdB{L} =  beta1*sdB{L} + (1 -beta1) * gradsDB{L}' .* gradsDB{L}';
        weights{L} = weights{L} -learningRate* gradsDW{L}' ./ sqrt(sdW{L} +epsilon); 
        biases{L} = biases{L} -learningRate* gradsDB{L}' ./ sqrt(sdB{L} + epsilon);
     endif   
end

4.1a. Stochastic Gradient Descent with RMSProp – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read and load MNIST
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

print("X1=",X1.shape)
print("y1=",Y1.shape)

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
  
layersDimensions=[784, 15,9,10] 
# Use SGD with RMSProp
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="rmsprop", beta1=0.7, epsilon=1e-8,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig4.png")

4.1b. Stochastic Gradient Descent with RMSProp – R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
#Perform SGD with RMSProp
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.001,
                                  optimizer="rmsprop",
                                  beta1=0.9,
                                  epsilon=10^-8,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000 , 
                                  print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

4.1c. Stochastic Gradient Descent with RMSProp – Octave

source("DL7functions.m")
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
#Perform SGD with RMSProp
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.005,
 lrDecay=false, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="rmsprop",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=1,
 mini_batch_size = 512, 
 num_epochs = 5000);
plotCostVsEpochs(5000,costs)

5.1. Stochastic Gradient Descent with Adam

Adaptive Moment Estimate is a combination of the momentum (1st moment) and RMSProp(2nd moment). The equations for Adam are below
$v_{dW}^l = \beta_{1} v_{dW}^l + (1-\beta_{1})dW^{l}$
$v_{db}^l = \beta_{1} v_{db}^l + (1-\beta_{1})db^{l}$
The bias corrections for the 1st moment
$vCorrected_{dW}^l= \frac {v_{dW}^l}{1 - \beta_{1}^{t}}$
$vCorrected_{db}^l= \frac {v_{db}^l}{1 - \beta_{1}^{t}}$

Similarly the moving average for the 2nd moment- RMSProp
$s_{dW}^l = \beta_{2} s_{dW}^l + (1-\beta_{2})(dW^{l})^2$
$s_{db}^l = \beta_{2} s_{db}^l + (1-\beta_{2})(db^{l})^2$
The bias corrections for the 2nd moment
$sCorrected_{dW}^l= \frac {s_{dW}^l}{1 - \beta_{2}^{t}}$
$sCorrected_{db}^l= \frac {s_{db}^l}{1 - \beta_{2}^{t}}$

The Adam Gradient Descent is given by
$W^{l} = W^{l} - \frac {\alpha vCorrected_{dW}^l}{\sqrt (s_{dW}^l + \epsilon) }$
$b^{l} = b^{l} - \frac {\alpha vCorrected_{db}^l}{\sqrt (s_{db}^l + \epsilon) }$
The code snippet of Adam in R is included below

# Perform Gradient Descent with Adam
# Input : Weights and biases
#       : beta1
#       : epsilon
#       : gradients
#       : learning rate
#       : outputActivationFunc - Activation function at hidden layer sigmoid/softmax
#output : Updated weights after 1 iteration
gradientDescentWithAdam  <- function(parameters, gradients,v, s, t, 
                        beta1=0.9, beta2=0.999, epsilon=10^-8, learningRate=0.1,outputActivationFunc="sigmoid"){
    
    L = length(parameters)/2 # number of layers in the neural network
    v_corrected <- list()
    s_corrected <- list()
    # Update rule for each parameter. Use a for loop.
    for(l in 1:(L-1)){
        # v['dWk'] = beta *v['dWk'] + (1-beta)*dWk
        v[[paste("dW",l, sep="")]] = beta1*v[[paste("dW",l, sep="")]] + 
            (1-beta1) * gradients[[paste('dW',l,sep="")]]
        v[[paste("db",l, sep="")]] = beta1*v[[paste("db",l, sep="")]] + 
            (1-beta1) * gradients[[paste('db',l,sep="")]]
        
        
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",l, sep="")]] = v[[paste("dW",l, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",l, sep="")]] = v[[paste("db",l, sep="")]]/(1-beta1^t)
               
        # Element wise multiply of gradients
        s[[paste("dW",l, sep="")]] = beta2*s[[paste("dW",l, sep="")]] + 
            (1-beta2) * gradients[[paste('dW',l,sep="")]] * gradients[[paste('dW',l,sep="")]]
        s[[paste("db",l, sep="")]] = beta2*s[[paste("db",l, sep="")]] + 
            (1-beta2) * gradients[[paste('db',l,sep="")]] * gradients[[paste('db',l,sep="")]]
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",l, sep="")]] = s[[paste("dW",l, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",l, sep="")]] = s[[paste("db",l, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",l, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",l, sep="")]]+epsilon)        
                
        parameters[[paste("W",l,sep="")]] = parameters[[paste("W",l,sep="")]] -
            learningRate * v_corrected[[paste("dW",l, sep="")]]/d1
        parameters[[paste("b",l,sep="")]] = parameters[[paste("b",l,sep="")]] -
            learningRate*v_corrected[[paste("db",l, sep="")]]/d2
    }    
    # Compute for the Lth layer
    if(outputActivationFunc=="sigmoid"){
        v[[paste("dW",L, sep="")]] = beta1*v[[paste("dW",L, sep="")]] + 
            (1-beta1) * gradients[[paste('dW',L,sep="")]]
        v[[paste("db",L, sep="")]] = beta1*v[[paste("db",L, sep="")]] + 
            (1-beta1) * gradients[[paste('db',L,sep="")]]
                
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",L, sep="")]] = v[[paste("dW",L, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",L, sep="")]] = v[[paste("db",L, sep="")]]/(1-beta1^t)
                
        # Element wise multiply of gradients
        s[[paste("dW",L, sep="")]] = beta2*s[[paste("dW",L, sep="")]] + 
            (1-beta2) * gradients[[paste('dW',L,sep="")]] * gradients[[paste('dW',L,sep="")]]
        s[[paste("db",L, sep="")]] = beta2*s[[paste("db",L, sep="")]] + 
            (1-beta2) * gradients[[paste('db',L,sep="")]] * gradients[[paste('db',L,sep="")]]
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",L, sep="")]] = s[[paste("dW",L, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",L, sep="")]] = s[[paste("db",L, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",L, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",L, sep="")]]+epsilon)  
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate * v_corrected[[paste("dW",L, sep="")]]/d1
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate*v_corrected[[paste("db",L, sep="")]]/d2
        
    }else if (outputActivationFunc=="softmax"){
        v[[paste("dW",L, sep="")]] = beta1*v[[paste("dW",L, sep="")]] + 
            (1-beta1) * t(gradients[[paste('dW',L,sep="")]])
        v[[paste("db",L, sep="")]] = beta1*v[[paste("db",L, sep="")]] + 
            (1-beta1) * t(gradients[[paste('db',L,sep="")]])
                
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",L, sep="")]] = v[[paste("dW",L, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",L, sep="")]] = v[[paste("db",L, sep="")]]/(1-beta1^t)        
        
        # Element wise multiply of gradients
        s[[paste("dW",L, sep="")]] = beta2*s[[paste("dW",L, sep="")]] + 
            (1-beta2) * t(gradients[[paste('dW',L,sep="")]]) * t(gradients[[paste('dW',L,sep="")]])
        s[[paste("db",L, sep="")]] = beta2*s[[paste("db",L, sep="")]] + 
            (1-beta2) * t(gradients[[paste('db',L,sep="")]]) * t(gradients[[paste('db',L,sep="")]])
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",L, sep="")]] = s[[paste("dW",L, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",L, sep="")]] = s[[paste("db",L, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",L, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",L, sep="")]]+epsilon) 
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate * v_corrected[[paste("dW",L, sep="")]]/d1
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate*v_corrected[[paste("db",L, sep="")]]/d2
    }
    return(parameters)
}

5.1a. Stochastic Gradient Descent with Adam – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
print(len(training))
#for i in range(len(training)):
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T


# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
layersDimensions=[784, 15,9,10] 
#Perform SGD with Adam optimization
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="adam", beta1=0.9, beta2=0.9, epsilon = 1e-8,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True, figure="fig5.png")

5.1b. Stochastic Gradient Descent with Adam – R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
#Perform SGD with Adam
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.005,
                                  optimizer="adam",
                                  beta1=0.7,
                                  beta2=0.9,
                                  epsilon=10^-8,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000 , 
                                  print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

5.1c. Stochastic Gradient Descent with Adam – Octave

source("DL7functions.m")
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);
# Set layer dimensions
layersDimensions=[784, 15, 9, 10];

# Note the high value for epsilon. 
#Otherwise GD with Adam does not seem to converge   
# Perform SGD with Adam         
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
                       hiddenActivationFunc='relu', 
                       outputActivationFunc="softmax",
                       learningRate = 0.1,
                       lrDecay=false, 
                       decayRate=1,
                       lambd=0,
                       keep_prob=1,
                       optimizer="adam",
                       beta=0.9,
                       beta1=0.9,
                       beta2=0.9,
                       epsilon=100,
                       mini_batch_size = 512, 
                       num_epochs = 5000);
plotCostVsEpochs(5000,costs)

Conclusion: In this post I discuss and implement several Stochastic Gradient Descent optimization methods. The implementation of these methods enhance my already existing generic L-Layer Deep Learning Network implementation in vectorized Python, R and Octave, which I had discussed in the previous post in this series on Deep Learning from first principles in Python, R and Octave. Check it out, if you haven’t already. As already mentioned the code for this post can be cloned/forked from Github at DeepLearning-Part7

Watch this space! I’ll be back!

Also see
1.My book ‘Practical Machine Learning with R and Python’ on Amazon
2. Deep Learning from first principles in Python, R and Octave – Part 3
3. Experiments with deblurring using OpenCV
3. Design Principles of Scalable, Distributed Systems
4. Natural language processing: What would Shakespeare say?
5. yorkr crashes the IPL party! – Part 3!
6. cricketr flexes new muscles: The final analysis

To see all post click Index of posts

Deep Learning from first principles in Python, R and Octave – Part 6

“Today you are You, that is truer than true. There is no one alive who is Youer than You.”
Dr. Seuss

“Explanations exist; they have existed for all time; there is always a well-known solution to every human problem — neat, plausible, and wrong.”
H L Mencken

Introduction

In this 6th instalment of ‘Deep Learning from first principles in Python, R and Octave-Part6’, I look at a couple of different initialization techniques used in Deep Learning, L2 regularization and the ‘dropout’ method. Specifically, I implement “He initialization” & “Xavier Initialization”. My earlier posts in this series of Deep Learning included

1. Part 1 – In the 1st part, I implemented logistic regression as a simple 2 layer Neural Network
2. Part 2 – In part 2, implemented the most basic of Neural Networks, with just 1 hidden layer, and any number of activation units in that hidden layer. The implementation was in vectorized Python, R and Octave
3. Part 3 -In part 3, I derive the equations and also implement a L-Layer Deep Learning network with either the relu, tanh or sigmoid activation function in Python, R and Octave. The output activation unit was a sigmoid function for logistic classification
4. Part 4 – This part looks at multi-class classification, and I derive the Jacobian of a Softmax function and implement a simple problem to perform multi-class classification.
5. Part 5 – In the 5th part, I extend the L-Layer Deep Learning network implemented in Part 3, to include the Softmax classification. I also use this L-layer implementation to classify MNIST handwritten digits with Python, R and Octave.

The code in Python, R and Octave are identical, and just take into account some of the minor idiosyncrasies of the individual language. In this post, I implement different initialization techniques (random, He, Xavier), L2 regularization and finally dropout. Hence my generic L-Layer Deep Learning network includes these additional enhancements for enabling/disabling initialization methods, regularization or dropout in the algorithm. It already included sigmoid & softmax output activation for binary and multi-class classification, besides allowing relu, tanh and sigmoid activation for hidden units.

A video presentation of regularization and initialization techniques can be also be viewed in Neural Networks 6

This R Markdown file and the code for Python, R and Octave can be cloned/downloaded from Github at DeepLearning-Part6

You can download the PDF version of this book from Github at https://github.com/tvganesh/DeepLearningBook-2ndEd

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.

1. Initialization techniques

The usual initialization technique is to generate Gaussian or uniform random numbers and multiply it by a small value like 0.01. Two techniques which are used to speed up convergence is the He initialization or Xavier. These initialization techniques enable gradient descent to converge faster.

1.1 a Default initialization – Python

This technique just initializes the weights to small random values based on Gaussian or uniform distribution

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network with random initialization
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid",learningRate = 0.6, num_iterations = 9000, initType="default", print_cost = True,figure="fig1.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig2.png")

1.1 b He initialization – Python

‘He’ initialization attributed to He et al, multiplies the random weights by
$\sqrt{\frac{2}{dimension\ of\ previous\ layer}}$

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network with He  initialization
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid", learningRate =0.6,    num_iterations = 10000,initType="He",print_cost = True,                           figure="fig3.png")

plt.clf()
plt.close()
# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig4.png")

1.1 c Xavier initialization – Python

Xavier initialization multiply the random weights by
$\sqrt{\frac{1}{dimension\ of\ previous\ layer}}$

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]
 
# Train a L layer Deep Learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid",
                            learningRate = 0.6,num_iterations = 10000, initType="Xavier",print_cost = True,
                            figure="fig5.png")

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig6.png")

1.2a Default initialization – R

source("DLfunctions61.R")
#Load the data
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
#Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 8000, 
                            initType="default",
                            print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,8000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",lr=0.5)

1.2b He initialization – R

The code for ‘He’ initilaization in R is included below

# He Initialization model for L layers
# Input : List of units in each layer
# Returns: Initial weights and biases matrices for all layers
# He initilization multiplies the random numbers with sqrt(2/layerDimensions[previouslayer])
HeInitializeDeepModel <- function(layerDimensions){
    set.seed(2)
    
    # Initialize empty list
    layerParams <- list()
    
    # Note the Weight matrix at layer 'l' is a matrix of size (l,l-1)
    # The Bias is a vectors of size (l,1)
    
    # Loop through the layer dimension from 1.. L
    # Indices in R start from 1
    for(l in 2:length(layersDimensions)){
        # Initialize a matrix of small random numbers of size l x l-1
        # Create random numbers of size  l x l-1
        w=rnorm(layersDimensions[l]*layersDimensions[l-1])
        
        # Create a weight matrix of size l x l-1 with this initial weights and
        # Add to list W1,W2... WL
        # He initialization - Divide by sqrt(2/layerDimensions[previous layer])
        layerParams[[paste('W',l-1,sep="")]] = matrix(w,nrow=layersDimensions[l],
                                                      ncol=layersDimensions[l-1])*sqrt(2/layersDimensions[l-1])
        layerParams[[paste('b',l-1,sep="")]] = matrix(rep(0,layersDimensions[l]),
                                                      nrow=layersDimensions[l],ncol=1)
    }
    return(layerParams)
}

The code in R below uses He initialization to learn the data

source("DLfunctions61.R")
# Load the data
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
# Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 9000, 
                            initType="He",
                            print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5,lr=0.5)

1.2c Xavier initialization – R

## Xav initialization 
# Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 9000, 
                            initType="Xav",
                            print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5)

1.3a Default initialization – Octave

source("DL61functions.m")
# Read the data
data=csvread("circles.csv");

X=data(:,1:2);
Y=data(:,3);
# Set the layer dimensions
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0, 
                               keep_prob=1,
                               numIterations = 10000,
                               initType="default");
# Plot cost vs iterations
plotCostVsIterations(10000,costs)  
#Plot decision boundary                            
plotDecisionBoundary(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu")

1.3b He initialization – Octave

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
# Set the layer dimensions
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0, 
                               keep_prob=1,
                               numIterations = 8000,
                               initType="He");
plotCostVsIterations(8000,costs)   
#Plot decision boundary                              
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

1.3c Xavier initialization – Octave

The code snippet for Xavier initialization in Octave is shown below

source("DL61functions.m")
# Xavier Initialization for L layers
# Input : List of units in each layer
# Returns: Initial weights and biases matrices for all layers
function [W b] = XavInitializeDeepModel(layerDimensions)
    rand ("seed", 3);
    # note the Weight matrix at layer 'l' is a matrix of size (l,l-1)
    # The Bias is a vectors of size (l,1)
    
    # Loop through the layer dimension from 1.. L
    # Create cell arrays for Weights and biases

    for l =2:size(layerDimensions)(2)
         W{l-1} = rand(layerDimensions(l),layerDimensions(l-1))* sqrt(1/layerDimensions(l-1)); #  Multiply by .01 
         b{l-1} = zeros(layerDimensions(l),1);       
   
    endfor
end

The Octave code below uses Xavier initialization

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
#Set layer dimensions
layersDimensions = [2 11 1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='relu',
outputActivationFunc="sigmoid",
learningRate = 0.5,
lambd=0,
keep_prob=1,
numIterations = 8000,
initType="Xav");

plotCostVsIterations(8000,costs)
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

2.1a Regularization : Circles data – Python

The cross entropy cost for Logistic classification is given as $J = \frac{1}{m}\sum_{i=1}^{m}y^{i}log((a^{L})^{(i)}) - (1-y^{i})log((a^{L})^{(i)})$ The regularized L2 cost is given by $J = \frac{1}{m}\sum_{i=1}^{m}y^{i}log((a^{L})^{(i)}) - (1-y^{i})log((a^{L})^{(i)}) + \frac{\lambda}{2m}\sum \sum \sum W_{kj}^{l}$

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu',  
                               outputActivationFunc="sigmoid",learningRate = 0.6, lambd=0.1, num_iterations = 9000, 
                               initType="default", print_cost = True,figure="fig7.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig8.png")


plt.clf()
plt.close()
#Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T,keep_prob=0.9), train_X, train_Y,str(2.2),"fig8.png",)

2.1 b Regularization: Spiral data – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.clf()
plt.close() 
#Set layer dimensions 
layersDimensions = [2,100,3]
y1=y.reshape(-1,1).T
# Train a deep learning network
parameters = L_Layer_DeepModel(X.T, y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",
                           learningRate = 1,lambd=1e-3, num_iterations = 5000, print_cost = True,figure="fig9.png")

plt.clf()
plt.close()  
W1=parameters['W1']
b1=parameters['b1']
W2=parameters['W2']
b2=parameters['b2']
plot_decision_boundary1(X, y1,W1,b1,W2,b2,figure2="fig10.png")

2.2a Regularization: Circles data – R

source("DLfunctions61.R")
#Load data
df=read.csv("circles.csv",header=FALSE)
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
#Set layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            lambd=0.1,
                            numIterations = 9000, 
                            initType="default",
                            print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5)

2.2b Regularization:Spiral data – R

# Read the spiral dataset
#Load the data
source("DLfunctions61.R")
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)
layersDimensions = c(2, 100, 3)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
hiddenActivationFunc='relu',
outputActivationFunc="softmax",
learningRate = 0.5,
lambd=0.01,
numIterations = 9000,
print_cost = True)
print_cost = True)

parameters<-retvals$parameters
plotDecisionBoundary1(Z,parameters)

2.3a Regularization: Circles data – Octave

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0.2,
                               keep_prob=1,
                               numIterations = 8000,
                               initType="default");

plotCostVsIterations(8000,costs)  
#Plot decision boundary                              
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

2.3b Regularization:Spiral data 2 – Octave

source("DL61functions.m")
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 100 3]
# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="softmax",
                               learningRate = 0.6,
                               lambd=0.2,
                               keep_prob=1,
                               numIterations = 10000);
                              
plotCostVsIterations(10000,costs)
#Plot decision boundary
plotDecisionBoundary1(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

3.1 a Dropout: Circles data – Python

The ‘dropout’ regularization technique was used with great effectiveness, to prevent overfitting by Alex Krizhevsky, Ilya Sutskever and Prof Geoffrey E. Hinton in the Imagenet classification with Deep Convolutional Neural Networks

The technique of dropout works by dropping a random set of activation units in each hidden layer, based on a ‘keep_prob’ criteria in the forward propagation cycle. Here is the code for Octave. A ‘dropoutMat’ is created for each layer which specifies which units to drop Note: The same ‘dropoutMat has to be used which computing the gradients in the backward propagation cycle. Hence the dropout matrices are stored in a cell array.

 for l =1:L-1  
    ...      
    D=rand(size(A)(1),size(A)(2));
    D = (D < keep_prob) ;
    # Zero out some hidden units
    A= A .* D;    
    # Divide by keep_prob to keep the expected value of A the same                                  
    A = A ./ keep_prob; 
    # Store D in a dropoutMat cell array
    dropoutMat{l}=D;
    ...
 endfor

In the backward propagation cycle we have

    for l =(L-1):-1:1
          ...
          D = dropoutMat{l};  
          # Zero out the dAl based on same dropout matrix       
          dAl= dAl .* D;   
          # Divide by keep_prob to maintain the expected value                                       
          dAl = dAl ./ keep_prob;
          ...
    endfor

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu',  
                               outputActivationFunc="sigmoid",learningRate = 0.6, keep_prob=0.7, num_iterations = 9000, 
                               initType="default", print_cost = True,figure="fig11.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T,keep_prob=0.7), train_X, train_Y,str(0.6),figure1="fig12.png")

3.1b Dropout: Spiral data – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
# Create an input data set - Taken from CS231n Convolutional Neural networks,
# http://cs231n.github.io/neural-networks-case-study/
               

N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.clf()
plt.close()  
layersDimensions = [2,100,3]
y1=y.reshape(-1,1).T
# Train a deep learning network
parameters = L_Layer_DeepModel(X.T, y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",
                           learningRate = 1,keep_prob=0.9, num_iterations = 5000, print_cost = True,figure="fig13.png")

plt.clf()
plt.close()  
W1=parameters['W1']
b1=parameters['b1']
W2=parameters['W2']
b2=parameters['b2']
#Plot decision boundary
plot_decision_boundary1(X, y1,W1,b1,W2,b2,figure2="fig14.png")

3.2a Dropout: Circles data – R

source("DLfunctions61.R")
#Load data
df=read.csv("circles.csv",header=FALSE)
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 

x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            keep_prob=0.8,
                            numIterations = 9000, 
                            initType="default",
                            print_cost = True)

# Plot the decision boundary
plotDecisionBoundary(z,retvals,keep_prob=0.6, hiddenActivationFunc="relu",0.5)

3.2b Dropout: Spiral data – R

# Read the spiral dataset
source("DLfunctions61.R")
# Load data
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)

# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="softmax",
                            learningRate = 0.1,
                            keep_prob=0.90,
                            numIterations = 9000, 
                            print_cost = True)

parameters<-retvals$parameters
#Plot decision boundary
plotDecisionBoundary1(Z,parameters)

3.3a Dropout: Circles data – Octave

data=csvread("circles.csv");

X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0,
                               keep_prob=0.8,
                               numIterations = 10000,
                               initType="default");
plotCostVsIterations(10000,costs) 
#Plot decision boundary
plotDecisionBoundary1(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu")

3.3b Dropout Spiral data – Octave

source("DL61functions.m")
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);

layersDimensions = [numFeats numHidden  numOutput];  
# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="softmax",
                               learningRate = 0.1,
                               lambd=0,
                               keep_prob=0.8,
                               numIterations = 10000); 

plotCostVsIterations(10000,costs)    
#Plot decision boundary                            
plotDecisionBoundary1(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu")

Note: The Python, R and Octave code can be cloned/downloaded from Github at DeepLearning-Part6
Conclusion
This post further enhances my earlier L-Layer generic implementation of a Deep Learning network to include options for initialization techniques, L2 regularization or dropout regularization

References
1. Deep Learning Specialization
2. Neural Networks for Machine Learning

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
3. My book ‘Practical Machine Learning with R and Python’ on Amazon
4. Simulating a Web Joint in Android
5. Inswinger: yorkr swings into International T20s
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9. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon