https://github.com/hayatiyrtgl/cell_classification_cnn

This code trains a CNN in Keras to classify cell images (infected/uninfected). It sets up data generators, defines model architecture with convolutional layers, applies regularization, configures callbacks, and trains the model for binary classification.
https://github.com/hayatiyrtgl/cell_classification_cnn

cnn cnn-architecture cnn-classification cnn-for-visual-recognition cnn-keras cnn-model convolutional convolutional-layers convolutional-neural-network convolutional-neural-networks python python-classifiers python3

Last synced: 12 days ago
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This code trains a CNN in Keras to classify cell images (infected/uninfected). It sets up data generators, defines model architecture with convolutional layers, applies regularization, configures callbacks, and trains the model for binary classification.

• Host: GitHub
• URL: https://github.com/hayatiyrtgl/cell_classification_cnn
• Owner: HayatiYrtgl
• License: mit
• Created: 2024-04-18T17:21:05.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-18T17:21:49.000Z (9 months ago)
• Last Synced: 2024-11-05T09:15:45.162Z (about 2 months ago)
• Topics: cnn, cnn-architecture, cnn-classification, cnn-for-visual-recognition, cnn-keras, cnn-model, convolutional, convolutional-layers, convolutional-neural-network, convolutional-neural-networks, python, python-classifiers, python3
• Language: Python
• Homepage:
• Size: 38.1 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Explanation of the code line by line:

1. `from keras.layers import *`: Imports all layers from the Keras library. This allows you to use any layer without specifying the module.

2. `from keras.models import Sequential`: Imports the Sequential model from Keras. Sequential is a linear stack of layers.

3. `from keras.utils import to_categorical`: Imports a utility function to convert class vector (integers) to binary class matrix.

4. `import numpy as np`: Imports the numpy library with the alias np.

5. `from keras.regularizers import l1`: Imports L1 regularization.

6. `from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard`: Imports various callbacks used during training.

7. `from keras.preprocessing.image import ImageDataGenerator`: Imports the ImageDataGenerator class from Keras for real-time data augmentation.

8. `train_path = "../DATASET/cell_images/train"`: Specifies the path to the training data directory.

9. `test_path = "../DATASET/cell_images/test"`: Specifies the path to the test data directory.

10. `train_generator = ImageDataGenerator(...)`: Creates an ImageDataGenerator object for training data augmentation.

11. `test_generator = ImageDataGenerator(...)`: Creates an ImageDataGenerator object for test data preprocessing.

12. `train_set = train_generator.flow_from_directory(...)`: Generates batches of augmented data for training from the specified directory.

13. `test_set = test_generator.flow_from_directory(...)`: Generates batches of augmented data for testing from the specified directory.

14. `early_stopping = EarlyStopping(...)`: Defines the early stopping criteria for training.

15. `tensorboard = TensorBoard(...)`: Configures TensorBoard for visualization during training.

16. `model_checkpoint = ModelCheckpoint(...)`: Configures model checkpointing to save the best model during training.

17. `callbacks_list = [early_stopping, tensorboard, model_checkpoint]`: Creates a list of callbacks to be used during training.

18. `model = Sequential()`: Creates a Sequential model.

19. `model.add(Conv2D(...))`: Adds a convolutional layer to the model with specified parameters.

20. `model.add(MaxPooling2D(...))`: Adds a max pooling layer to the model.

21. `model.add(BatchNormalization())`: Adds a batch normalization layer to the model.

22. `model.add(Flatten())`: Flattens the input to a one-dimensional array.

23. `model.add(Dense(...))`: Adds a fully connected dense layer to the model.

24. `model.summary()`: Prints a summary of the model architecture.

25. `model.compile(...)`: Compiles the model with specified loss function, optimizer, and metrics.

26. `model.fit(...)`: Trains the model using the training data generator, with specified parameters such as number of epochs, batch size, validation split, and callbacks.

That's a comprehensive overview of what each line of the code does. Let me know if you need further clarification on any specific part!