1. The document describes how to implement regression with neural networks using TensorFlow on Amazon Web Services. It involves launching an EC2 instance, connecting to it, and running a Jupyter notebook to generate sample data and build a neural network model to predict outputs. 2. A neural network with three dense layers is created using Keras API to predict a numeric output value. The model is trained on a training set for 500 epochs and tested on a held-out test set. 3. Regression is performed to predict a value based on a single input feature, with the goal of minimizing mean squared error loss. The model learns from the training data through backpropagation and tweaks the weights to improve predictions.

1. RAMCO INSTITUTE OF TECHNOLOGY
RAJAPALAYAM
13.04.2020
Prepared by Dr.M.Kaliappan, Associate Professor/CSE
--------------------------------------------------------------------------------------------------------------------
Topic: Implementation of Regression with Neural Networks using TensorFlow in Amazon
Deep Learning Server
---------------------------------------------------------------------------------------------------------------------
1. Visit to https://aws.amazon.com/deep-learning/
2. Sign in to console , if not, signup with your data and make payment of Rs.2/-

2. 3. Click on services  then, click on EC2
4. Click on EC2 Dashboard and launch instances

3. 5. Press Ctrl+F (search) : Search a keyword deep learning
6. Select Deep learning AMI(Amazon Linux2)
7. Choose an instance type
8. Click Review and launch
9. Click launch

4. 10. Select create a new keypair
11. Give key file name: key(any name)
12. Download key pair
A key.pem file is downloaded and saved in your computer(Download section)

5. 13. Select your created instances
14. Click connect button

6. Copy the Public DNS: ec2-3-133-153-1.us-east-2.compute.amazonaws.com
15. Open command prompt  go to Downloads
Type the following command
ssh -L localhost:8888:localhost:8888 -i key1.pem ec2-user@ec2-3-133-153-1.us-east-
2.compute.amazonaws.com

7. Now, we got Amazon Linux system

8. 16. Type jupyter notebook

9. The jupyter notebook is running at the following URL.
17. Type this URL in browser.
Click on new button. Select Environment (conda_tensorflow_p36)
Regression with Neural Networks using TensorFlow Keras API
(Blue color represents code, copy and paste in jupyter notebook)
Figure 1: Neural Network

10. In regression, the computer/machine should be able to predict a value – mostly numeric.
Generate Data: Here we are going to generate some data using our own function. This function
is a non-linear function and a usual line fitting may not work for such a function
def myfunc(x):
if x < 30:
mult = 10
elif x < 60:
mult = 20
else:
mult = 50
return x*mult
Let us check what does this function return.
print(myfunc(10))
print(myfunc(30))
print(myfunc(60))
It should print something like:
100
600
3000
Now, let us generate data. Let us import numpy library as np. here x is a numpy array of input
values. Then, we are generating values between 0 and 100 with a gap of 0.01 using arange
function. It generates a numpy array.
import numpy as np
x = np.arange(0, 100, .01)
print(x)
data:
array([0.000e+00, 1.000e-02, 2.000e-02, ..., 9.997e+01, 9.998e+01, 9.999e+01])
To call a function repeatedly on a numpy array we first need to convert the function using
vectorize. Afterwards, we are converting 1-D array to 2-D array having only one value in the
second dimension – you can think of it as a table of data with only one column.
myfuncv = np.vectorize(myfunc)
y = myfuncv(x)

11. X = x.reshape(-1, 1)
Print(X)
[[0.000e+00],
[1.000e-02],
[2.000e-02],
...
[9.997e+01],
[9.998e+01],
[9.999e+01]]
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
y_test). We are going make neural ne
to produce y from X – we are going to test the model on the test set.
import sklearn.model_selection as sk
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You
can try plotting X vs y, as well as, X_test vs y_test.
import matplotlib.pyplot as plt
plt.scatter(X_train,y_train)
plt.scatter(X_test,y_test)
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
y_test). We are going make neural network learn from training data, and once it has learnt
we are going to test the model on the test set.
import sklearn.model_selection as sk
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You
can try plotting X vs y, as well as, X_test vs y_test.
Figure 2: X_train vs y_train
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
twork learn from training data, and once it has learnt – how
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state = 42)
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You

12. Let us import TensorFlow libraries and check the version.
import tensorflow as tf
print(tf.__version__)
Now, let us create a neural network using Keras API of TensorFlow.
# Import the kera modules
from keras.layers import Input, Dense
from keras.models import Model
# This returns a tensor. Since the input only has one column
inputs = Input(shape=(1,))
# a layer instance is callable on a tensor, and returns a tensor
# To the first layer we are feeding inputs
x = Dense(32, activation='relu')(inputs)
# To the next layer we are feeding the result of previous call here it is h
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# Predictions are the result of the neural network. Notice that the predictions are also having one
column.
predictions = Dense(1)(x)
# This creates a model that includes
# the Input layer and three Dense layers
model = Model(inputs=inputs, outputs=predictions)
# Here the loss function is mse -
model.compile(optimizer='rmsprop',
loss='mse',
Figure 2: X_test vs y_test
et us import TensorFlow libraries and check the version.
us create a neural network using Keras API of TensorFlow.
from keras.layers import Input, Dense
from keras.models import Model
# This returns a tensor. Since the input only has one column
# a layer instance is callable on a tensor, and returns a tensor
# To the first layer we are feeding inputs
x = Dense(32, activation='relu')(inputs)
# To the next layer we are feeding the result of previous call here it is h
)(x)
x = Dense(64, activation='relu')(x)
# Predictions are the result of the neural network. Notice that the predictions are also having one
# This creates a model that includes
# the Input layer and three Dense layers
odel = Model(inputs=inputs, outputs=predictions)
Mean Squared Error because it is a regression problem.
model.compile(optimizer='rmsprop',
# Predictions are the result of the neural network. Notice that the predictions are also having one
Mean Squared Error because it is a regression problem.

13. metrics=['mse'])
Let us now train the model. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
X at a time.
model.fit(X_train, y_train, epochs=500, batch_size=100) # starts training
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
y_test = model.predict(X_test)
plt.scatter(X_test, y_test)
References:
1. https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n
eural_networks.htm
2. https://cloudxlab.com/blog/regression
. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
model.fit(X_train, y_train, epochs=500, batch_size=100) # starts training
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n
https://cloudxlab.com/blog/regression-using-tensorflow-keras-api/
. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n