def test_pca_mdb(self):
featureset = musicset.MusicSet(dirname='features2')
train = featureset.load_train_data()
new_features = reduce.pca(train.music)
storage = store(featureset.results_dir)
storage['reduced_mdb.dat'] = new_features
self.assertGreater(train.music.shape[1], new_features.shape[1])
def classify(self):
storage = store(os.path.join(settings.BRAIN_DIR, 'nn'))
save_path = storage['save.path']
meta_path = save_path + '.meta'
saver = tf.train.import_meta_graph(meta_path)
data = self.get_features()
n_f = data.shape[0]
data = data.reshape((1, n_f))
prediction = None
with tf.Session() as sess:
saver.restore(sess, save_path)
graph = tf.get_default_graph()
x = graph.get_tensor_by_name('x:0')
y_ = graph.get_tensor_by_name('y_:0')
keep_prob = graph.get_tensor_by_name('keep_prob:0')
result = sess.run(y_, feed_dict={x: data, keep_prob: 1})[0]
idx = np.argmax(result)
dataset = musicset.MusicSet()
dataset.one_hot_encode_genres()
for genre in dataset.genres:
if dataset.encoded_genres[genre][idx] == 1:
prediction = genre
if self.prediction is not None:
self.prediction.destroy()
self.prediction = tk.Label(self.root, text=prediction)
self.prediction.config(font=("Courier", 36))
self.prediction.grid(row=0, column=1)
def test_training(self):
p = os.path.join(settings.BRAIN_DIR, "specfeat")
if not os.path.isdir(p):
os.mkdir(p)
dataset = musicset.MusicSet(dirname='features_s_10', force_load=True)
dataset.load_files()
dataset.one_hot_encode_genres()
cnn_t = sf.CNNFeat(dataset)
ds = cnn_t.train(display_step=100, path=p)
print(ds.music.shape)
print(ds.labels.shape)
def test_training(self):
dataset = musicset.MusicSet(dirname='features_m_10_fl100')
dataset.load_files()
dataset.load_train_data()
dataset.load_test_data()
if settings.PCA:
components = reduce.pca_comp(dataset.train.music)
k = components.shape[1]
dataset.train.music = np.matmul(components.T,
dataset.train.music.T)
dataset.train.music = dataset.train.music.T
dataset.test.music = np.matmul(components.T, dataset.test.music.T)
dataset.test.music = dataset.test.music.T
cnn_t = cnn.CNN(dataset)
cnn_t.train(display_step=100)
def test_nn(self):
p = os.path.join(settings.BRAIN_DIR, "specfeat")
dataset = musicset.MusicSet(dirname='special')
dataset.load_files()
dataset.one_hot_encode_genres()
storage = store(p)
ds = storage['features.dat']
all_data = np.hstack(tuple(np.split(ds.music, 10, axis=0)))
all_labels = np.hstack(tuple(np.split(ds.labels, 10, axis=0)))
dataset.train = musicset.DataSet()
dataset.test = musicset.DataSet()
dataset.train.music = np.vstack(
tuple(np.split(all_data[:70, :], 10, axis=-1)))
dataset.train.labels = np.vstack(
tuple(np.split(all_labels[:70, :], 10, axis=-1)))
dataset.test.music = np.vstack(
tuple(np.split(all_data[70:, :], 10, axis=-1)))
dataset.test.labels = np.vstack(
tuple(np.split(all_labels[70:, :], 10, axis=-1)))
nn_t = nn.NN(dataset, n_input=256)
step = 100
tr, te, trc, tec = nn_t.train(display_step=step,
out=True,
path='special.final')
epochs = np.arange(int(settings.NN['TRAINING_CYCLES'] / step))
f, ax = plt.subplots(2)
ax[0].plot(epochs, tr, color='#FF2D00', label='Training Accuracy')
#ax[0].plot(epochs, vl, color='#1DFF00', label='Validation Accuracy')
ax[0].plot(epochs, te, color='#0014FF', label='Testing Accuracy')
ax[0].legend(loc="lower right")
ax[0].set_xlabel('Epoch')
ax[0].set_ylabel('Accuracy')
ax[0].set_title('Accuracy vs Number of epochs')
ax[1].plot(epochs, trc, color='#FF2D00', label='Training Lost')
ax[1].plot(epochs, tec, color='#1DFF00', label='Test Lost')
ax[1].legend(loc="lower right")
ax[1].set_xlabel('Epoch')
ax[1].set_ylabel('Loss')
ax[1].set_title('Loss vs Number of Epochs')
f.suptitle('Network with %s layers' % settings.NN['NUM_HIDDEN_LAYERS'])
plt.show()
def test_training(self):
dataset = musicset.MusicSet(dirname='features_md_6')
dataset.load_files()
dataset.load_train_data()
dataset.load_test_data()
#print(dataset.test.music.shape)
#return
# dataset.load_validation_data()
# print(dataset.validation.music[:10, :10])
# return
if settings.PCA:
components = reduce.pca_comp(dataset.train.music)
k = components.shape[1]
dataset.train.music = np.matmul(components.T,
dataset.train.music.T)
dataset.train.music = dataset.train.music.T
dataset.test.music = np.matmul(components.T, dataset.test.music.T)
dataset.test.music = dataset.test.music.T
nn_t = nn.NN(dataset, n_input=k)
else:
nn_t = nn.NN(dataset)
step = 100
tr, te, trc, tec = nn_t.train(display_step=step,
out=True,
path='test.final')
epochs = np.arange(int(settings.NN['TRAINING_CYCLES'] / step))
f, ax = plt.subplots(2)
ax[0].plot(epochs, tr, color='#FF2D00', label='Training Accuracy')
#ax[0].plot(epochs, vl, color='#1DFF00', label='Validation Accuracy')
ax[0].plot(epochs, te, color='#0014FF', label='Testing Accuracy')
ax[0].legend(loc="lower right")
ax[0].set_xlabel('Epoch')
ax[0].set_ylabel('Accuracy')
ax[0].set_title('Accuracy vs Number of epochs')
ax[1].plot(epochs, trc, color='#FF2D00', label='Training Lost')
ax[1].plot(epochs, tec, color='#1DFF00', label='Test Lost')
ax[1].legend(loc="lower right")
ax[1].set_xlabel('Epoch')
ax[1].set_ylabel('Loss')
ax[1].set_title('Loss vs Number of Epochs')
f.suptitle('Network with %s layers' % settings.NN['NUM_HIDDEN_LAYERS'])
plt.show()
def setUpClass(cls):
cls.fileset = musicset.MusicSet(dirname='features3')
cls.fileset.load_files()
cls.total_features = 0
for f in settings.FEATURES:
cls.total_features += settings.FEATURES_LENGTH[f]
def setUp(self):
self.dataset = musicset.MusicSet()
self.dataset.one_hot_encode_genres()
def setUpClass(cls):
super().setUpClass()
cls.fileset = musicset.MusicSet()
cls.fileset.load_files()
def setUp(self):
self.fileset = musicset.MusicSet()
self.fileset.load_files()
def test_visual_data(self):
featureset = musicset.MusicSet(dirname='features_md_6')
featureset.visualize()
def test_lda(self):
featureset = musicset.MusicSet(dirname='features2')
train = featureset.load_train_data()
new_features = reduce.lda(train.music, train.labels)
print(new_features.shape)
