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