def baseline(dataset):
audio, _, _, labels = load_data(dataset, 'audio', 'frame', verbose=True)
X = flatten_data(audio, image=False)
y = labels[:, 0]
print("--" * 20)
print("processed data shape", X.shape)
print("processed label shape", y.shape)
print("--" * 20)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
assert X_train.shape[1] == X_test.shape[1]
test_AE = Autoencoder(dataset, 'audio', X_train.shape[1])
test_AE.build_model()
test_AE.train_model(X_train)
X_encoded_train = test_AE.transform(X_train)
X_encoded_test = test_AE.transform(X_test)
test_SVM = LinearSVM('%s_baseline_%s' % (dataset, 'audio'))
test_SVM.train(X_encoded_train, y_train)
test_SVM.test(X_encoded_test, y_test)
def bimodal_fusion(dataset):
# concat_data_audio_1, concat_data_audio_2, labels = load_data(dataset, 'audio', 'concat', verbose=True)
concat_data_mfcc_1, concat_data_mfcc_2, labels = load_data(dataset,
'mfcc',
'concat',
verbose=True)
# X = np.vstack((concat_data_audio_1, concat_data_audio_2))
X = np.vstack((concat_data_mfcc_1, concat_data_mfcc_2))
y = np.hstack((labels[:, 0], labels[:, 0]))
print("--" * 20)
print("processed data shape", X.shape)
print("processed label shape", y.shape)
print("--" * 20)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
assert X_train.shape[1] == X_test.shape[1]
test_AE = Autoencoder(dataset, 'concat_audio', X_train.shape[1])
test_AE.build_model()
test_AE.train_model(X_train)
X_encoded_train = test_AE.transform(X_train)
X_encoded_test = test_AE.transform(X_test)
test_SVM = LinearSVM('%s_baseline_%s' % (dataset, 'concat_audio'))
test_SVM.train(X_encoded_train, y_train)
test_SVM.test(X_encoded_test, y_test)
def mnist_test(self):
from keras.datasets import mnist
print("running autoencoders on MNIST data")
(X_train, _), (X_test, _) = mnist.load_data()
assert X_train.shape[1:] == X_test.shape[1:]
X_train = flatten_data(X_train)
X_test = flatten_data(X_test)
assert X_train.shape == X_test.shape
mnist_ae = Autoencoder('12','12', X_train.shape[1])
mnist_ae.build_model()
mnist_ae.train_model(X_train, X_test)
mnist_ae.vis_model(X_test)