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Rock Or A Mine Prediction
TensorFlow Tutorial
You have been hired by US navy to create a model, that can detect the difference between a mine and
a rock.
A naval mine is a self-contained
explosive device placed in water
to damage or destroy surface
ships or submarines.

Rock Or Mine Prediction
We are going to identify whether the obstacle is a Rock or a Mine on the basis of various parameters

Rock Or A Mine Prediction
In order to train the model, we will use Sonar dataset. The dataset looks like this:
TensorFlow Tutorial
The label associated with each record contains the letter
"R" if the object is a rock and "M" if it is a mine
It contains 208 patterns obtained by bouncing sonar signals off a metal
cylinder and a rock at various angles and under various conditions.

How To Create This Model?
Let’s see how we will implement this model

How To Create This Model?
TensorFlow Tutorial
Start
Read the
Dataset
Define features
and labels
Divide the dataset into two
parts for training and testing
TensorFlow data structure for
holding features, labels etc..
Implement the model
Train the model
Reduce MSE (actual output –
desired output)
End
Repeat the process to
decrease the loss
Pre-processing of dataset
Make prediction on the test
data
TensorFlow Tutorial
Encode The Dependent
variable

To Create This Model We Will Use
TensorFlow
Let’s understand TensorFlow first, but before that let’s look at what are tensors?

What Are Tensors?
 Tensors are the standard way of representing data in TensorFlow (deep learning).
 Tensors are multidimensional arrays, an extension of two-dimensional tables (matrices) to data
with higher dimension.
Tensor of
dimension[1]
Tensor of
dimensions[2]
Tensor of
dimensions[3]
TensorFlow Tutorial

Tensors Rank
TensorFlow Tutorial
Rank Math Entity Python Example
0 Scalar (magnitude
only)
s = 483
1 Vector (magnitude
and direction)
v = [1.1, 2.2, 3.3]
2 Matrix (table of
numbers)
m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
3 3-Tensor (cube of
numbers)
t =
[[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18
]]]
n n-Tensor (you get
the idea)
....

Tensor Data Types
In addition to dimensionality Tensors have different data types as well, you can assign any one of
these data types to a Tensor
TensorFlow Tutorial

What Is TensorFlow?
Now, is the time explore TensorFlow.

What Is TensorFlow?
 TensorFlow is a Python library used to implement deep networks.
 In TensorFlow, computation is approached as a dataflow graph.
3.2 -1.4 5.1 …
-1.0 -2 2.4 …
… … … …
… … … …
Tensor Flow
Matmul
W X
Add
Relu
B
Computational
Graph
Functions
Tensors TensorFlow Tutorial

TensorFlow Code-Basics
Let’s understand the fundamentals of TensorFlow

TensorFlow Code-Basics
TensorFlow core programs consists of two discrete sections:
Building a computational graph Running a computational graph
A computational graph is a series of TensorFlow
operations arranged into a graph of nodes
TensorFlow Tutorial

TensorFlow Building And Running A Graph
Building a computational graph Running a computational graph
import tensorflow as tf
node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0)
print(node1, node2)
Constant nodes
sess = tf.Session()
print(sess.run([node1, node2]))
To actually evaluate the nodes, we must run
the computational graph within a session.
As the session encapsulates the control and
state of the TensorFlow runtime.
TensorFlow Tutorial

Tensorflow Example
a
5.0
Constimport tensorflow as tf
# Build a graph
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
# Launch the graph in a session
sess = tf.Session()
# Evaluate the tensor 'C'
print(sess.run(c))
Computational Graph
TensorFlow Tutorial

Tensorflow Example
a
b
5.0
6.0
Const
# Build a graph
c = a * b
sess = tf.Session()
print(sess.run(c))
Computational Graph
TensorFlow Tutorial

Tensorflow Example
a
b c
5.0
6.0
Const Mul
# Build a graph
c = a * b
sess = tf.Session()
print(sess.run(c))
Computational Graph
TensorFlow Tutorial

Tensorflow Example
a
b c
5.0
6.0
Const Mul
30.0
# Build a graph
c = a * b
sess = tf.Session()
print(sess.run(c))
Running The Computational Graph
TensorFlow Tutorial

Graph Visualization

Graph Visualization
 For visualizing TensorFlow graphs, we use TensorBoard.
 The first argument when creating the FileWriter is an output directory name, which will be created
if it doesn't exist.
File_writer = tf.summary.FileWriter('log_simple_graph', sess.graph)
TensorBoard runs as a local web app, on port 6006. (this
is default port, “6006” is “ ” upside-down.)oo
TensorFlow Tutorial
tensorboard --logdir = “path_to_the_graph”
Execute this command in the cmd

Constants, Placeholders and Variables
Let’s understand what are constants, placeholders and variables

Constant
One type of a node is a constant. It takes no inputs, and it outputs a value
it stores internally.
print(node1, node2)
Constant nodes
Constant
Placeholder
Variable
TensorFlow Tutorial

Constant
One type of a node is a constant. It takes no inputs, and it outputs a value
it stores internally.
print(node1, node2)
Constant nodes
Constant
Placeholder
Variable
What if I want the
graph to accept
external inputs?
TensorFlow Tutorial

Placeholder
Constant
Placeholder
Variable
A graph can be parameterized to accept external inputs, known as placeholders.
A placeholder is a promise to provide a value later.
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Placeholder
Constant
Placeholder
Variable
A graph can be parameterized to accept external inputs, known as placeholders.
A placeholder is a promise to provide a value later.
How to modify the
graph, if I want new
output for the same
input ?
TensorFlow Tutorial

Variable
Constant
Placeholder
Variable
To make the model trainable, we need to be able to modify the graph to get
new outputs with the same input. Variables allow us to add trainable
parameters to a graph
TensorFlow Tutorial

Let Us Now Create A Model

Simple Linear Model
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print(sess.run(linear_model, {x:[1,2,3,4]}))
We've created a model, but we
don't know how good it is yet
TensorFlow Tutorial

How To Increase The Efficiency Of The Model?
Calculate the loss
Model
Update the Variables
Repeat the process until the loss becomes very small
A loss function measures how
far apart the current model is
from the provided data.
TensorFlow Tutorial

Calculating The Loss
In order to understand how good the Model is, we should know the loss/error.
To evaluate the model on training data, we need a y i.e. a
placeholder to provide the desired values, and we need to
write a loss function.
We'll use a standard loss model for linear regression.
(linear_model – y ) creates a vector where each element is
the corresponding example's error delta.
tf.square is used to square that error.
tf.reduce_sum is used to sum all the squared error.
y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
TensorFlow Tutorial

Reducing The Loss
Optimizer modifies each variable according to the magnitude of the derivative of loss with
respect to that variable. Here we will use Gradient Descent Optimizer
How Gradient Descent Actually
Works?
Let’s understand this
with an analogy
TensorFlow Tutorial

Reducing The Loss
• Suppose you are at the top of a mountain, and you have to reach a lake which is at the lowest
point of the mountain (a.k.a valley).
• A twist is that you are blindfolded and you have zero visibility to see where you are headed. So,
what approach will you take to reach the lake?
TensorFlow Tutorial

Reducing The Loss
• The best way is to check the ground near you and observe where the land tends to descend.
• This will give an idea in what direction you should take your first step. If you follow the
descending path, it is very likely you would reach the lake.
Consider the length of the step as learning rate
Consider the position of the hiker as weight
Consider the process of climbing down
the mountain as cost function/loss
function
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Reducing The Loss
Global Cost/Loss
Minimum
Jmin(w)
J(w)
Let us
understand the
math behind
Gradient
Descent
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Batch Gradient Descent
The weights are updated
incrementally after each
epoch. The cost function J(⋅),
the sum of squared errors
(SSE), can be written as:
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The magnitude and direction
of the weight update is
computed by taking a step in
the opposite direction of the
cost gradient
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cost gradient
The weights are then updated
after each epoch via the
following update rule:
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cost gradient
The weights are then updated
after each epoch via the
following update rule:
Here, Δw is a vector that
contains the weight
updates of each weight
coefficient w, which are
computed as follows:
TensorFlow Tutorial

Reducing The Loss
Suppose, we want to find the best parameters (W) for our learning algorithm. We can apply the
same analogy and find the best possible values for that parameter. Consider the example below:
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
sess.run(init)
for i in range(1000):
sess.run(train, {x:[1,2,3,4], y:[0,-1,-2,-3]})
print(sess.run([W, b]))
TensorFlow Tutorial

Implementation Of The Use-Case

Start
Read the
Dataset
Define features
and labels
variable
TensorFlow Tutorial

Start
Read the
Dataset
Define features
and labels
TensorFlow data structure for
holding features, labels etc..
Implement the model
Train the model
Reduce MSE (actual output –
desired output)
End
Repeat the process to
decrease the loss
Make prediction on the test
data
TensorFlow Tutorial
variable

Session In A Minute
Use-Case What Are Tensors? What Is TensorFlow?
TensorFlow Code-Basics TensorFlow Datastructures Implementation Of The Use-Case

TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka

TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | Edureka

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