RuizSerra

"""
Based on @4rtemi5 's TF implementation, ported to PyTorch
https://www.rpisoni.dev/posts/cossim-convolution/
"""

import torch
from torch import nn
import torch.nn.functional as F

class CosSimConv2D(nn.Module):

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):
    '''

felixgwu

import torch
from torch import nn

__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop',
           'fcdensenet56', 'fcdensenet67', 'fcdensenet103',
           'fcdensenet103_nodrop']


class DenseBlock(nn.Module):

enakai00

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

eerwitt

# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

tencia

from PIL import Image
import sys
import os
import math
import numpy as np

###########################################################################################
# script to generate moving mnist video dataset (frame by frame) as described in
# [1] arXiv:1502.04681 - Unsupervised Learning of Video Representations Using LSTMs
#     Srivastava et al

yanhaijing

(function ($) {
	var
		followers = ["zedshaw"];
		followeds = [];
	function follow(id, callback) {
		$.post('https://github.com/users/follow', {target: id}, function(data, textStatus, xhr) {
			/*optional stuff to do after success */

			callback(id, data);
			//console.log("成功follow ", id, "此人共有", data.count, "位粉丝");

econchick

class Graph:
  def __init__(self):
    self.nodes = set()
    self.edges = defaultdict(list)
    self.distances = {}

  def add_node(self, value):
    self.nodes.add(value)

  def add_edge(self, from_node, to_node, distance):

JeffPaine

import json
import requests

# Authentication for user filing issue (must have read/write access to
# repository to add issue to)
USERNAME = 'CHANGEME'
PASSWORD = 'CHANGEME'

# The repository to add this issue to
REPO_OWNER = 'CHANGEME'