def get_combined_data(): # reading train data train = read_training_data() # reading test data test = read_test_data() # import ipdb; ipdb.set_trace() # extracting and then removing the targets from the training data targets = train.Survived train.drop('Survived', 1, inplace=True) # merging train data and test data for future feature engineering # import ipdb; ipdb.set_trace() combined = train.append(test) combined.reset_index(inplace=True) combined.drop('index', inplace=True, axis=1) return combined
def predict(self): X_test = read_data.read_test_data() return self.model.predict(X_test, verbose=1)
sp_train = params.sp_train sp_cont = params.sp_cont ap_train = params.ap_train ap_cont = params.ap_cont spectral_data, aperiodic_data, label_data, cutoff_points = read_training_data( model_name, load=load_data) singing_model = SingingModel(spectral_data, aperiodic_data, label_data, cutoff_points, model_name) if sp_train: singing_model.train_model(SPECTRAL_MODE, sp_cont) if ap_train: singing_model.train_model(APERIODIC_MODE, ap_cont) label_data, frequency = read_test_data(model_name) spectral_output = singing_model.inference(label_data, SPECTRAL_MODE) aperiodic_output = singing_model.inference(label_data, APERIODIC_MODE, spectral_output) spectral_output, aperiodic_output = decode_envelopes( spectral_output, aperiodic_output, params.sample_rate, model_name) construct_audio(spectral_output, aperiodic_output, frequency, output_name)
def test_performance(model): test_x, test_y = read_test_data() test_pred = model.predict(test_x, batch_size=512, verbose=1) evals = get_evals(test_y, test_pred) return evals
import tensorflow as tf from tensorflow._api.v1.keras import layers import read_data x_train, y_train = read_data.read_train_data() x_test, y_test = read_data.read_test_data() num, H, W, _ = x_train.shape model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=(1, 1), padding='valid', data_format='channels_last', activation='relu', use_bias=True, input_shape=(H, W, 1)), tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=(1, 1), padding='same', data_format='channels_last', activation='relu', use_bias=True, input_shape=(H - 2, W - 2, 16)), tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same', data_format='channels_last', activation='relu',
if __name__ == "__main__": TRAIN_DATA, LEMMA2SENSES, LEMMA2INT = read_data.read_train_data( read_data.read_x("ALL.data.xml")[0], read_data.read_y("ALL.gold.key.bnids.txt"), True) MAX_NB_SENSES = max([len(LEMMA2SENSES[k]) for k in LEMMA2SENSES ]) # max number of senses among all target words MAX_NB_TARGETS = len(LEMMA2SENSES) # how many target words # load word embedding initialized by init_emb (run init_emb first if you don't have this file) with open('pretrained_vectors/needed' + '.pkl', 'rb') as f: WORD_VECTORS = pickle.load(f) WORD_VECTORS["_drop_"] = np.random.uniform( -0.1, 0.1, 300) # add drop vector for drop words NB_EPOCHS = 100 # number of epochs to train x_val, y_val, _ = read_data.read_test_data( LEMMA2INT, LEMMA2SENSES, WORD_VECTORS) # read validation data """train models""" tf.reset_default_graph() train_model = models.Model2(MAX_NB_SENSES, 32, MAX_NB_TARGETS) val_model = models.Model2(MAX_NB_SENSES, 32, MAX_NB_TARGETS, is_training=False) print("train models created") """run train models""" init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) VAL_ACC_LIST = [] LOSS_LIST = [] TRAIN_ACC_LIST = []