def main() -> None: """Executes the program.""" kilometrage = input("Kilometrage: ") try: kilometrage = int(kilometrage) except: print("Cannot cast '{}' to float.".format(kilometrage)) exit(1) theta0, theta1 = get_weights() prediction = predict(theta0, theta1, kilometrage) print("Estimated price for {}kms: {:.4f}$".format(kilometrage, prediction)) if (theta0 == 0 and theta1 == 0): print( "Note: it seems that the model is not trained yet. Run train.py to set weights." )
def main() -> None: """Runs the program, plots raw and normalized dataset. If the weights are not null, we draw them as a red line.""" data = pd.read_csv("./data.csv") _, ax = plt.subplots(1, 2) ax[0].scatter(data=data, x="km", y="price") ax[0].set_title("Raw dataset") theta0, theta1 = get_weights() if (theta0 != 0 and theta1 != 0): x = data["km"] y = theta0 + theta1 * x ax[0].plot(x, y, 'r') prepare_data(data) ax[1].set_title("Normalized dataset") ax[1].scatter(data=data, x="km", y="price") plt.show()
parser.add_argument("--weights", default=None, help="Model checkpoint to get pretrained weights from") args = parser.parse_args() num_steps = int(args.steps) # Directory setup abs_path = os.path.abspath(__file__) # Absolute path of this file directory = os.path.dirname(abs_path) model_dir = directory + "/models/" + args.output_name # Get pretrained weights for feature extractor weights = None if args.weights is not None: weights = os.path.join(os.path.dirname(__file__), args.weights) weights = get_weights(weights) # numpy weights # Define the input function for training def tfrecord_input(): # Keep list of filenames, so you can input directory of tfrecords easily train_filenames = glob.glob("../data_processing/tfrecords/*tfrecords") valid_filenames = glob.glob("../data_processing/tfrecords/val*tfrecords") batch_size = 1024 # Import data dataset = tf.data.TFRecordDataset(train_filenames, num_parallel_reads=6, buffer_size=1000 * 1000 * 128) # 128mb of io cache