Keras CNN Pre-trained Deep Learning models for Flower Recognition
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Using Keras CNN pre-trained deep learning model for recognizing 17 different types of flowers. The accuracy is high i.e. 98.53%.
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Ans) istream, ostream, and iostream are derived from ifstream, ofstream, and fstream,
respectively.
11.11) Compare and contrast the mode operators ios::in, ios::app, and ios::out. Provide a C++
code segment that illustrates the use of these mode operators.
Ans)
To open a file we have to use this member function.
As a first argument we have to supply the filename.
As a second argument we can supply the mode of opening the file.this is an optional parameter.
Syntax: open(filename,mode);
ios::in – if we want to read the data from the file we have to use this mode.This is used for input
operations.
Code:
ifstream file;
file.open(\"D:\\\\bank.txt\"); or file.open(\"D:\\\\bank.txt\",ios::in);
here we are trying to read the data from the file named bank.txt which is available in D
drive.Here we didn’t specify the mode here.By default the file will be opened in ios::in mode
ios::out - if we want to write the data from to a file we have to use this mode.This is used for
output operations.
Code:
ofstream myfile;
myfile.open (\"D:\\\\bankbal.txt\"); or myfile.open (\"D:\\\\bankbal.txt\", ios::out);
myfile <<”Hell.
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Class 3 Chapter 17 (Store data in a file)
Due Wednesday by 8pm Points 10 Submitting a file upload Available Feb 8 at 7pm - Feb 15 at 8pm
Program 3.1 Requirements
Write/Read Test Program using RandomAccessFile and Objects
*Note you should be using methods for the code, everything should not just be dumped in the main method.
Create a new binary data file using RandomAccessFile populate it with integers from 0 to 100
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Update the integer binary file so that all of the even numbers are 2
Read the integer binary file and print it to the screen with 10 numbers on each line
Create a new binary data file using RandomAccessFile populate it with doubles from 0 to 100
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Update the double binary file so that all of the odd numbers are 1
Read the double binary file and print it to the screen with 10 numbers on each line
Create a new binary data file using RandomAccessFile populate it with Strings "Hello", "Good Morning", "Good Afternoon", "Good Evening"
Note: make all of the Strings the same size.
Read the String binary file and print each String on it's own line
Update the String binary file so that the last String says "Good Bye"
Read the String binary file and print each String on it's own line
Create a new binary data file using ObjectOutputStream
Use the Employee object from EmployeeManager Interface Part 1 to populate the file with the data below:
1, 100, "John", "Wayne", Employee.EmployeeType.CONTRACTOR)
2, 1000.0, "Selina", "Kyle", Employee.EmployeeType.CONTRACTOR
Create a new binary data file using RandomAccessFile
Use the Employee object from EmployeeManager Interface Part 1 to populate the file with the data below:
1, 100, "John", "Wayne", Employee.EmployeeType.CONTRACTOR)
2, 1000.0, "Selina", "Kyle", Employee.EmployeeType.CONTRACTOR
Example output:
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
2, 1, 2, 3, 2, 5, 2, 7, 2, 9, 2,
11, 2, 13, 2, 15, 2, 17, 2, 19, 2,
21, 2, 23, 2, 25, 2, 27, 2, 29, 2,
31, 2, 33, 2, 35, 2, 37, 2, 39, 2,
41, 2, 43, 2, 45, 2, 47, 2, 49, 2,
51, 2, 53, 2, 55, 2, 57, 2, 59, 2,
61, 2, 63, 2, 65, 2, 67, 2, 69, 2,
71, 2, 73, 2, 75, 2, 77, 2, 79, 2,
81, 2, 83, 2, 85, 2, 87, 2, 89, 2,
91, 2, 93, 2, 95, 2, 97, 2, 99, 2,
0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0,
11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0,
21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0.
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- 2. “ 2
- 3. Steps 3 Step 1 Dataset Acquisition Step 3 Testing Step 2 Training Deep Learning
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- 9. 9
- 10. 2. Data Training
- 11. Configuration File ◦ This is the configuration file or the settings file we will be using to provide inputs to our system. ◦ This is just a .json file which is a key-value pair file format to store data effectively. 11
- 12. 12 1. Model: The model key takes the parameters – ’mobilenet’. 2. Weights: The weights key takes the value ’imagenet’ as we are using weights from imagenet. You can also set this to ’None’. 3. Include-top: This key takes the value false specifying that we are going to take the features from any intermediate layer of the network. 4. Test_size: The test_size key takes the value in the range (0.10 - 0.90). This is to make a split between your overall data into training and testing. 5. Seed: The seed key takes any value to reproduce same results everytime you run the code. 6. Num_classes: The num_classes specifies the number of classes or labels considered for the image classification problem.
- 14. “ 14
- 15. “ 15 Output
- 16. Output Files 16
- 17. 17
- 18. 18
- 19. Output 19
- 25. 25
- 26. 26