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Deep learning models for respiratory disease detection.
Includes CNN and LSTM architectures.
"""
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models, callbacks
from tensorflow.keras.utils import to_categorical
from typing import Tuple, Optional, Dict
import pickle
from pathlib import Path
class CNNModel:
"""Convolutional Neural Network for audio classification."""
def __init__(self, input_shape: Tuple, num_classes: int, model_name: str = "cnn_model"):
"""
Initialize CNN model.
Args:
input_shape: Shape of input (height, width, channels)
num_classes: Number of output classes
model_name: Name of the model
"""
self.input_shape = input_shape
self.num_classes = num_classes
self.model_name = model_name
self.model = None
self.history = None
def build_model(self, dropout_rate: float = 0.3):
"""
Build CNN architecture.
Args:
dropout_rate: Dropout rate for regularization
"""
model = models.Sequential(name=self.model_name)
# First convolutional block
model.add(layers.Conv2D(32, (3, 3), activation='relu',
padding='same', input_shape=self.input_shape))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(dropout_rate))
# Second convolutional block
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(dropout_rate))
# Third convolutional block
model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(dropout_rate))
# Fourth convolutional block
model.add(layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(layers.BatchNormalization())
model.add(layers.GlobalAveragePooling2D())
# Dense layers
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(dropout_rate))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dropout(dropout_rate))
# Output layer
if self.num_classes == 2:
model.add(layers.Dense(1, activation='sigmoid'))
else:
model.add(layers.Dense(self.num_classes, activation='softmax'))
self.model = model
print(f"\n{self.model_name} architecture:")
self.model.summary()
return model
def compile_model(self, learning_rate: float = 0.001):
"""Compile the model."""
if self.model is None:
raise ValueError("Model must be built before compilation")
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
if self.num_classes == 2:
loss = 'binary_crossentropy'
metrics = ['accuracy', keras.metrics.AUC(name='auc')]
else:
loss = 'sparse_categorical_crossentropy'
metrics = ['accuracy']
self.model.compile(
optimizer=optimizer,
loss=loss,
metrics=metrics
)
print(f"Model compiled with optimizer={optimizer.__class__.__name__}, loss={loss}")
def train(self, X_train: np.ndarray, y_train: np.ndarray,
X_val: np.ndarray, y_val: np.ndarray,
epochs: int = 50, batch_size: int = 32,
model_dir: str = 'models'):
"""
Train the CNN model.
Args:
X_train: Training features
y_train: Training labels
X_val: Validation features
y_val: Validation labels
epochs: Number of training epochs
batch_size: Batch size
model_dir: Directory to save model checkpoints
"""
if self.model is None:
raise ValueError("Model must be built and compiled before training")
# Create model directory
model_path = Path(model_dir)
model_path.mkdir(parents=True, exist_ok=True)
# Define callbacks
checkpoint_path = model_path / f"{self.model_name}_best.keras"
callbacks_list = [
callbacks.ModelCheckpoint(
str(checkpoint_path),
monitor='val_loss',
save_best_only=True,
verbose=1
),
callbacks.EarlyStopping(
monitor='val_loss',
patience=10,
restore_best_weights=True,
verbose=1
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
factor=0.5,
patience=5,
min_lr=1e-7,
verbose=1
)
]
print(f"\nTraining {self.model_name}...")
print(f"Training samples: {len(X_train)}, Validation samples: {len(X_val)}")
print(f"Epochs: {epochs}, Batch size: {batch_size}")
# Train model
self.history = self.model.fit(
X_train, y_train,
validation_data=(X_val, y_val),
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks_list,
verbose=1
)
print(f"\nTraining complete. Best model saved to {checkpoint_path}")
return self.history
def evaluate(self, X_test: np.ndarray, y_test: np.ndarray) -> Dict:
"""Evaluate model on test set."""
if self.model is None:
raise ValueError("Model must be trained before evaluation")
print(f"\nEvaluating {self.model_name}...")
results = self.model.evaluate(X_test, y_test, verbose=1)
# Get predictions
y_pred_proba = self.model.predict(X_test)
if self.num_classes == 2:
y_pred = (y_pred_proba > 0.5).astype(int).flatten()
else:
y_pred = np.argmax(y_pred_proba, axis=1)
evaluation_results = {
'loss': results[0],
'accuracy': results[1],
'predictions': y_pred,
'probabilities': y_pred_proba
}
if len(results) > 2:
evaluation_results['auc'] = results[2]
print(f"Test Loss: {results[0]:.4f}")
print(f"Test Accuracy: {results[1]:.4f}")
return evaluation_results
def save(self, filepath: str):
"""Save model to disk."""
self.model.save(filepath)
print(f"Model saved to {filepath}")
@classmethod
def load(cls, filepath: str):
"""Load model from disk."""
model = keras.models.load_model(filepath)
print(f"Model loaded from {filepath}")
return model
class LSTMModel:
"""LSTM model for sequential audio classification."""
def __init__(self, input_shape: Tuple, num_classes: int, model_name: str = "lstm_model"):
"""
Initialize LSTM model.
Args:
input_shape: Shape of input (time_steps, features)
num_classes: Number of output classes
model_name: Name of the model
"""
self.input_shape = input_shape
self.num_classes = num_classes
self.model_name = model_name
self.model = None
self.history = None
def build_model(self, dropout_rate: float = 0.3):
"""
Build LSTM architecture.
Args:
dropout_rate: Dropout rate for regularization
"""
model = models.Sequential(name=self.model_name)
# LSTM layers
model.add(layers.LSTM(128, return_sequences=True, input_shape=self.input_shape))
model.add(layers.Dropout(dropout_rate))
model.add(layers.BatchNormalization())
model.add(layers.LSTM(64, return_sequences=True))
model.add(layers.Dropout(dropout_rate))
model.add(layers.BatchNormalization())
model.add(layers.LSTM(32))
model.add(layers.Dropout(dropout_rate))
# Dense layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dropout(dropout_rate))
# Output layer
if self.num_classes == 2:
model.add(layers.Dense(1, activation='sigmoid'))
else:
model.add(layers.Dense(self.num_classes, activation='softmax'))
self.model = model
print(f"\n{self.model_name} architecture:")
self.model.summary()
return model
def compile_model(self, learning_rate: float = 0.001):
"""Compile the model."""
if self.model is None:
raise ValueError("Model must be built before compilation")
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
if self.num_classes == 2:
loss = 'binary_crossentropy'
metrics = ['accuracy', keras.metrics.AUC(name='auc')]
else:
loss = 'sparse_categorical_crossentropy'
metrics = ['accuracy']
self.model.compile(
optimizer=optimizer,
loss=loss,
metrics=metrics
)
print(f"Model compiled with optimizer={optimizer.__class__.__name__}, loss={loss}")
def train(self, X_train: np.ndarray, y_train: np.ndarray,
X_val: np.ndarray, y_val: np.ndarray,
epochs: int = 50, batch_size: int = 32,
model_dir: str = 'models'):
"""Train the LSTM model."""
if self.model is None:
raise ValueError("Model must be built and compiled before training")
# Create model directory
model_path = Path(model_dir)
model_path.mkdir(parents=True, exist_ok=True)
# Define callbacks
checkpoint_path = model_path / f"{self.model_name}_best.keras"
callbacks_list = [
callbacks.ModelCheckpoint(
str(checkpoint_path),
monitor='val_loss',
save_best_only=True,
verbose=1
),
callbacks.EarlyStopping(
monitor='val_loss',
patience=10,
restore_best_weights=True,
verbose=1
),
callbacks.ReduceLROnPlateau(
monitor='val_loss',
factor=0.5,
patience=5,
min_lr=1e-7,
verbose=1
)
]
print(f"\nTraining {self.model_name}...")
print(f"Training samples: {len(X_train)}, Validation samples: {len(X_val)}")
# Train model
self.history = self.model.fit(
X_train, y_train,
validation_data=(X_val, y_val),
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks_list,
verbose=1
)
print(f"\nTraining complete. Best model saved to {checkpoint_path}")
return self.history
def evaluate(self, X_test: np.ndarray, y_test: np.ndarray) -> Dict:
"""Evaluate model on test set."""
if self.model is None:
raise ValueError("Model must be trained before evaluation")
print(f"\nEvaluating {self.model_name}...")
results = self.model.evaluate(X_test, y_test, verbose=1)
# Get predictions
y_pred_proba = self.model.predict(X_test)
if self.num_classes == 2:
y_pred = (y_pred_proba > 0.5).astype(int).flatten()
else:
y_pred = np.argmax(y_pred_proba, axis=1)
evaluation_results = {
'loss': results[0],
'accuracy': results[1],
'predictions': y_pred,
'probabilities': y_pred_proba
}
if len(results) > 2:
evaluation_results['auc'] = results[2]
print(f"Test Loss: {results[0]:.4f}")
print(f"Test Accuracy: {results[1]:.4f}")
return evaluation_results
def save(self, filepath: str):
"""Save model to disk."""
self.model.save(filepath)
print(f"Model saved to {filepath}")
@classmethod
def load(cls, filepath: str):
"""Load model from disk."""
model = keras.models.load_model(filepath)
print(f"Model loaded from {filepath}")
return model
if __name__ == "__main__":
print("Deep learning models module loaded successfully")
print("Available models: CNNModel, LSTMModel")
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