kirk86

Option Explicit

Sub AddElements()
Dim shp As Shape

Dim i As Integer, n As Integer
n = ActivePresentation.Slides.Count
For i = 1 To n
    Dim s As Slide
    Set s = ActivePresentation.Slides(i)

kirk86

#!/bin/bash

# remove any pre-existing virtual camera devices
virtualcamera remove-devices

# add virtual device with name FakeCam0 showing in zoom as Virtual Camera
virtualcamera add-device -i FakeCam0 "Virtual Camera"

# setup the format, dimensions and frames per second of virtual device
virtualcamera add-format FakeCam0 RGB24 1024 720 30

kirk86

#!/usr/bin/env python3

import sys
import os
import yaml
import subprocess

import tensorflow as tf
import tfjs_graph_converter.api as tfjs_api
import tfjs_graph_converter.util as tfjs_util

kirk86

tensorflow
pillow
pyyaml
opencv-python
tfjs-graph-converter==1.4.2
mediapipe==0.8.7.1

kirk86

#!/usr/bin/python3
# -*- coding: UTF-8 -*-
#

import os
import subprocess
import time
import socket
import sys
import json

kirk86

"""akmtdfgen:  A Keras multithreaded dataframe generator.

Works with Python 2.7 and Keras 2.x.

For Python 3.x, need to fiddle with the threadsafe generator code.


Test the generator_from_df() functions by running this file:

    python akmtdfgen.py

kirk86

from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D
from keras.layers import UpSampling2D, Reshape, Activation, Dropout
from keras.layers import Deconvolution2D, Dense, Flatten, Input
from keras.layers import Permute
from keras.optimizers import Adam, SGD
from keras import backend as K


def dice_coef(y_true, y_pred):

kirk86

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

kirk86

"""
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)

kirk86

def adam(loss, all_params, learning_rate=0.0002, beta1=0.1, beta2=0.001,
         epsilon=1e-8, gamma=1-1e-7):
    """
    ADAM update rules
    Default values are taken from [Kingma2014]

    References:
    [Kingma2014] Kingma, Diederik, and Jimmy Ba.
    "Adam: A Method for Stochastic Optimization."
    arXiv preprint arXiv:1412.6980 (2014).