Explanation of the code line by line:
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from keras.layers import *: Imports all layers from the Keras library. This allows you to use any layer without specifying the module. -
from keras.models import Sequential: Imports the Sequential model from Keras. Sequential is a linear stack of layers. -
from keras.utils import to_categorical: Imports a utility function to convert class vector (integers) to binary class matrix. -
import numpy as np: Imports the numpy library with the alias np. -
from keras.regularizers import l1: Imports L1 regularization. -
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard: Imports various callbacks used during training. -
from keras.preprocessing.image import ImageDataGenerator: Imports the ImageDataGenerator class from Keras for real-time data augmentation. -
train_path = "../DATASET/cell_images/train": Specifies the path to the training data directory. -
test_path = "../DATASET/cell_images/test": Specifies the path to the test data directory. -
train_generator = ImageDataGenerator(...): Creates an ImageDataGenerator object for training data augmentation. -
test_generator = ImageDataGenerator(...): Creates an ImageDataGenerator object for test data preprocessing. -
train_set = train_generator.flow_from_directory(...): Generates batches of augmented data for training from the specified directory. -
test_set = test_generator.flow_from_directory(...): Generates batches of augmented data for testing from the specified directory. -
early_stopping = EarlyStopping(...): Defines the early stopping criteria for training. -
tensorboard = TensorBoard(...): Configures TensorBoard for visualization during training. -
model_checkpoint = ModelCheckpoint(...): Configures model checkpointing to save the best model during training. -
callbacks_list = [early_stopping, tensorboard, model_checkpoint]: Creates a list of callbacks to be used during training. -
model = Sequential(): Creates a Sequential model. -
model.add(Conv2D(...)): Adds a convolutional layer to the model with specified parameters. -
model.add(MaxPooling2D(...)): Adds a max pooling layer to the model. -
model.add(BatchNormalization()): Adds a batch normalization layer to the model. -
model.add(Flatten()): Flattens the input to a one-dimensional array. -
model.add(Dense(...)): Adds a fully connected dense layer to the model. -
model.summary(): Prints a summary of the model architecture. -
model.compile(...): Compiles the model with specified loss function, optimizer, and metrics. -
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!