Master of technology in information technology department of information science and


Step 1: In this module, there are three processes involved. Step 2



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Step 1:
In this module, there are three processes involved.
Step 2
: First the image is rotated
Step 3
: Then the image is zoomed
Step 4:
Finally the image is flipped horizontally.


24 
Figure 4.2 Output of Data Augmentation 
4.3 MODEL TRAINING
Input
: Preprocessed input image
Output 

Trained Data for 30 Epochs
Process Description
Step 1:
Initially, the base model MobilenetV2 is loaded with “ImageNet” 
and the last layer of pretrained model is fine tuned.
Step 2
: Then the dimension is flattened and the dense activation value (relu) 
and dropout value are entered.
Step 3
: Then the dense and activation function value (softmax) are entered.
Step 4
: The Model summary is obtained and configuration is saved.
Step 5:
The optimizer loss entropy and accuracy metric are configured and 
the trained model is evaluated and saved.


25 
Figure 4.3 Model Summary 
 
 
 
 
 
 
 


26 
 
Figure 4.4 Model Trained for 30 Epochs 
4.5 TESTING THE MODEL
Input

Trained data
Output 

Predicted data
Process Description 
Step 
1: In this module, the training loss and training accuracy data are plotted.
Step 2:
Next, a prediction on the training set is done.
Step 3
: Finally, the model is evaluated.


27 

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