| """ | |
| Train a neural network to implement the discrete Fourier transform | |
| """ | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| N = 32 | |
| batch = 10000 |
| """ | |
| Copyright (c) 2017 Arthur Mensch | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: |
| from tensorflow.python.client import device_lib | |
| def get_available_gpus(): | |
| local_device_protos = device_lib.list_local_devices() | |
| return [x.name for x in local_device_protos if x.device_type == 'GPU'] | |
| get_available_gpus() |
| from random import randint | |
| from math import log10, floor | |
| def human_format(num, ends=["", "K", "M", "B", "T"]): | |
| # divides by 3 to separate into thousands (...000) | |
| return ends[int(floor(log10(num))/3)] | |
| if __name__ == '__main__': | |
| for i in range(10): | |
| x = randint(1,10**i) |
| import multiprocessing | |
| import pandas as pd | |
| import numpy as np | |
| def _apply_df(args): | |
| df, func, kwargs = args | |
| return df.apply(func, **kwargs) | |
| def apply_by_multiprocessing(df, func, **kwargs): | |
| workers = kwargs.pop('workers') |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| import numpy as np | |
| def moving_average(data_set, periods=3): | |
| weights = np.ones(periods) / periods | |
| return np.convolve(data_set, weights, mode='valid') | |
| data = [1, 2, 3, 6, 9, 12, 20, 28, 30, 25, 22, 20, 15, 12, 10] | |
| ma = moving_average(np.asarray(data), 3) | |
| assert (np.around(ma, decimals=2)==np.array([2.0, 3.67, 6.0, 9.0, 13.67, 20.0, 26.0, 27.67, 25.67, 22.33, 19.0, 15.67, 12.33])).all() == True |