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# model_evaluation_sequences.py
import tensorflow as tf
import pickle
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.metrics import confusion_matrix, classification_report
def load_model(model_path='best_model_sequences.keras'):
"""
Loads the trained model.
Args:
model_path (str): Path to the saved model.
Returns:
tensorflow.keras.Model: Loaded model.
"""
model = tf.keras.models.load_model(model_path)
return model
def load_dataset_pickle(pickle_path='dataset_sequences.pkl'):
"""
Loads the dataset from a pickle file.
Args:
pickle_path (str): Path to the pickle file.
Returns:
tuple: Split data and label mapping.
"""
with open(pickle_path, 'rb') as f:
data = pickle.load(f)
return data['X_train'], data['X_test'], data['y_train'], data['y_test'], data['label_map']
def plot_history(history):
"""
Plots the training and validation accuracy and loss.
Args:
history (dict): Training history.
"""
acc = history.get('accuracy', history.get('acc'))
val_acc = history.get('val_accuracy', history.get('val_acc'))
loss = history['loss']
val_loss = history['val_loss']
epochs = range(1, len(acc) + 1)
plt.figure(figsize=(14,5))
plt.subplot(1,2,1)
plt.plot(epochs, acc, 'b', label='Training accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.subplot(1,2,2)
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
def evaluate_model(model, X_test, y_test, label_map):
"""
Evaluates the model on the test set.
Args:
model (tensorflow.keras.Model): Trained model.
X_test (numpy.ndarray): Test sequences.
y_test (numpy.ndarray): Test labels.
label_map (dict): Mapping from class names to indices.
"""
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy: {accuracy * 100:.2f}%")
print(f"Test Loss: {loss:.4f}")
# Predictions
y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_true = np.argmax(y_test, axis=1)
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred_classes)
plt.figure(figsize=(10,8))
sns.heatmap(cm, annot=True, fmt='d', xticklabels=label_map.keys(), yticklabels=label_map.keys(), cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()
# Classification Report
print("Classification Report:")
print(classification_report(y_true, y_pred_classes, target_names=label_map.keys()))
def main():
# Load the trained model
model = load_model('best_model_sequences.keras')
# Load the dataset
X_train, X_test, y_train, y_test, label_map = load_dataset_pickle('dataset_sequences.pkl')
# Evaluate the model
evaluate_model(model, X_test, y_test, label_map)
# Load and plot training history
try:
with open('history_sequences.pkl', 'rb') as f:
history = pickle.load(f)
plot_history(history)
except FileNotFoundError:
print("Training history not found. Skipping plotting.")
if __name__ == "__main__":
main()
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