def train(): x, y = load_data() x = x.transpose([0, 2, 1]) x = np.expand_dims(x, 3) # x = np.tile(x,[1,1,3]) print(x.shape) input_x, out = choose_model() model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)
def train(): x, y = load_data() x = x[:, :, 1] print(x.shape) x = utils.seg_signal(x, conf.seq_len, conf.seg_len) print('x_seg.shape:', x.shape) channel = int(conf.seq_len / conf.seg_len) input_x = Input([conf.seg_len, channel]) out = choose_model(input_x) model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)
def train(): x, y = load_data() input_x, out = choose_model() model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)
def train_lead_as_sample(): x, y, _, _ = lead_to_sample() input_x = Input([conf.seq_len, 1]) out = choose_model(input_x) model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)
def train_lead_as_channel(): x, y = load_data() input_x = Input([conf.seq_len, conf.num_lead]) out = choose_model(input_x) model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)
def train_tmp(): x, y = load_data() input_x = Input([5000, 12]) out = choose_model(input_x) model_compiled = compile_model(input_x, out) train_model(x, y, model_compiled)