def test():
# TODO : Test Later
print('==> Testing network..')
# Make predictions on full X_test mels
y_predicted = accuracy.predict_class_all(create_segmented_mels(X_test), a_net)
# Print statistics
print(np.sum(accuracy.confusion_matrix(y_predicted, y_test),axis=1))
print(accuracy.confusion_matrix(y_predicted, y_test))
print(accuracy.get_accuracy(y_predicted,y_test))
X_test = pool.map(get_wav, X_test)
# Convert to MFCC
if DEBUG:
print('converting to mfcc')
X_train = pool.map(to_mfcc, X_train)
X_test = pool.map(to_mfcc, X_test)
# Create segments from MFCCs
X_train, y_train = make_segments(X_train, y_train)
X_validation, y_validation = make_segments(X_test, y_test)
# Randomize training segments
X_train, _, y_train, _ = train_test_split(X_train, y_train, test_size=0)
# Train model
model = train_model(np.array(X_train), np.array(y_train), np.array(X_validation),np.array(y_validation))
# Make predictions on full X_test MFCCs
y_predicted = accuracy.predict_class_all(create_segmented_mfccs(X_test), model)
# Print statistics
print train_count
print test_count
print acc_to_beat
print np.sum(accuracy.confusion_matrix(y_predicted, y_test),axis=1)
print accuracy.confusion_matrix(y_predicted, y_test)
print accuracy.get_accuracy(y_predicted,y_test)
# Save model
save_model(model, model_filename)
# # Get statistics
train_count = Counter(y_train)
test_count = Counter(y_test)
# print('==> Creatting segments..')
# # Create segments
# X_train, y_train = make_segments(X_train, y_train)
# # Randomize training segments
# X_train, _, y_train, _ = train_test_split(X_train, y_train, test_size=0)
# print(X_train.shape)
#Training
for epoch in range(start_epoch, start_epoch + args.epochs):
train(epoch, X_train, y_train)
print('==> Testing network..')
# Make predictions on full X_test mels
y_predicted = accuracy.predict_class_all(create_segmented_mels(X_test, args.colsize), net)
# Print statistics
print(train_count)
print(test_count)
print(np.sum(accuracy.confusion_matrix(y_predicted, y_test),axis=1))
print(accuracy.confusion_matrix(y_predicted, y_test))
print(accuracy.get_accuracy(y_predicted,y_test))
# Create segments from MFCCs
X_train, y_train = make_segments(X_train, y_train)
# X_validation, y_validation = make_segments(X_test, y_test)
X_train, X_validation, y_train, y_validation = train_test_split(
X_train, y_train, test_size=0.15)
# print "Validation shape: {}".format(X_validation)
# Randomize training segments
X_train, _, y_train, _ = train_test_split(X_train, y_train, test_size=0)
if network == 'cnn':
# Train model
model = train_model(np.array(X_train), np.array(y_train),
np.array(X_validation), np.array(y_validation),
EPOCHS)
# Make predictions on full X_test MFCCs
y_predicted = accuracy.predict_class_all(
create_segmented_mfccs(X_test), model, 'cnn')
class_sum = np.sum(accuracy.confusion_matrix(y_predicted, y_test),
axis=1)
confusion_matrix = accuracy.confusion_matrix(y_predicted, y_test)
print confusion_matrix
print accuracy.get_accuracy(y_predicted, y_test)
show_confusion_matrix(confusion_matrix, plt,
['mandarin', 'arabic', 'english'], 'cnn')
if network == 'lstm':
# Train Lstm Model
lstm = train_lstm_model(np.array(X_train), np.array(y_train),
np.array(X_validation), np.array(y_validation),
EPOCHS)
y_predicted_lstm = accuracy.predict_class_all(
create_segmented_mfccs(X_test), lstm, 'lstm')