endolith

"""
Train a neural network to learn an FIR filter.

Created on Fri Aug  3 15:00:40 2018
"""
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.callbacks import Callback
import numpy as np
from scipy import signal

bitonic

// Copyright (c) 2021 Francesco Mazzoli <f@mazzo.li>
//
// Permission to use, copy, modify, and distribute this software for any
// purpose with or without fee is hereby granted, provided that the above
// copyright notice and this permission notice appear in all copies.
//
// THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
// WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
// MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
// ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

fengye

import audio_io
import numpy

audio1, _ = audio_io.ReadWavFile('voice_nodelay.wav')
audio1 = audio1.ravel()

audio2, _ = audio_io.ReadWavFile('voice_mic_blend3.wav')
audio2 = audio2.ravel()

# Truncate all signals same length, then pad to avoid boundary effects.

rtqichen

## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

import torch
import torch.nn as nn
from torch.nn import Parameter
from functools import wraps

class WeightNorm(nn.Module):
    append_g = '_g'
    append_v = '_v'

luzfcb

#!/bin/sh

# recursively removes all .pyc , .pyo files and __pycache__ directories in the current
# directory

find . | \
  grep -E "(__pycache__|\.pyc$|\.pyo$)" | \
  xargs rm -rf

# or, for copy-pasting:

anantzoid

Conditional Image Generation with PixelCNN Decoder

Implemenetation:

What

• New image density model based on PixelCNN
• Can generate variety of images from text embeddings or CNN layer weights
• Serves as decoder in image autoencoder
• Gated PixelCNN: Matches PixelRNN accuracy

guozhou

#include <iostream>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>

// GTS450 sm_21
#define NUM_SM 4 // no. of streaming multiprocessors
#define NUM_WARP_PER_SM 48 // maximum no. of resident warps per SM
#define NUM_BLOCK_PER_SM 8 // maximum no. of resident blocks per SM
#define NUM_BLOCK NUM_SM * NUM_BLOCK_PER_SM
#define NUM_WARP_PER_BLOCK NUM_WARP_PER_SM / NUM_BLOCK_PER_SM

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

hbsdev

#!/bin/sh

# recursively removes all .pyc files and __pycache__ directories in the current
# directory

find . | grep -E "(__pycache__|\.pyc$)" | xargs rm -rf