max-kuk

class GCN(tf.Module):
    def __init__(self, filters, k=7):
        super(GCN, self).__init__()
        self.padding_l1 = tf.keras.layers.ZeroPadding2D(padding=((k - 1) // 2, 0))
        self.conv_l1 = tf.keras.layers.Conv2D(filters, kernel_size=(k, 1))
        self.padding_l2 = tf.keras.layers.ZeroPadding2D(padding=(0, (k - 1) // 2))
        self.conv_l2 = tf.keras.layers.Conv2D(filters, kernel_size=(1, k))

        self.padding_r1 = tf.keras.layers.ZeroPadding2D(padding=((k - 1) // 2, 0))
        self.conv_r1 = tf.keras.layers.Conv2D(filters, kernel_size=(1, k))

max-kuk

# Copyright 2020 by Maksim Kukushkin, The Information Systems Institute, University of Leipzig.
# Weather crawler to collect weather data from Wunderground.com
# This file is part of the tools used
# for master thesis "Flight price prediction and Dynamic Pricing in European low-cost airlines"


import csv
import json
import time
from calendar import monthrange

max-kuk

max-kuk

max-kuk

max-kuk

import torch
from collections import OrderedDict
from typing import List

checkpoints_weights_paths: List[str] = ...  # sorted in descending order by score
model: torch.nn.Module = ...


def average_weights(state_dicts: List[dict]):
    everage_dict = OrderedDict()