kenorb

import hashlib, struct, codecs
 
ver = 2
prev_block = "000000000000000117c80378b8da0e33559b5997f2ad55e2f7d18ec1975b9717"
mrkl_root = "871714dcbae6c8193a2bb9b2a69fe1c0440399f38d94b3a0f1b447275a29978a"
time_ = 0x53058b35 # 2014-02-20 04:57:25
bits = 0x19015f53
 
# https://en.bitcoin.it/wiki/Difficulty
exp = bits >> 24

kenorb

## Unity ##

*.cs diff=csharp text
*.cginc text
*.shader text

*.mat merge=unityyamlmerge eol=lf
*.anim merge=unityyamlmerge eol=lf
*.unity merge=unityyamlmerge eol=lf
*.prefab merge=unityyamlmerge eol=lf

kenorb

function trigger(el, eventType) {
  if (typeof eventType === 'string' && typeof el[eventType] === 'function') {
    el[eventType]();
  } else {
    const event =
      eventType === 'string'
        ? new Event(eventType, {bubbles: true})
        : eventType;
    el.dispatchEvent(event);
  }

kenorb

alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure

kenorb

alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure

kenorb

# Show Tensor Images utility function

from torchvision.utils import make_grid
import matplotlib.pyplot as plt

def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
    '''
    Function for visualizing images: Given a tensor of images, number of images, and
    size per image, plots and prints the images in a uniform grid.
    '''

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def conv_backward(dH, cache):
    '''
    The backward computation for a convolution function
    
    Arguments:
    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
    cache -- cache of values needed for the conv_backward(), output of conv_forward()
    
    Returns:
    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1

kenorb

def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

kenorb

def conv_forward(X, W):
    '''
    The forward computation for a convolution function
    
    Arguments:
    X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
    W -- Weights, numpy array of size (f, f) assuming number of filters = 1
    
    Returns:
    H -- conv output, numpy array of size (n_H, n_W)

kenorb