mvoelk

import keras.backend as K
from keras.optimizers import Optimizer
from keras.legacy import interfaces

class WNAdam(Optimizer):
    """WNAdam optimizer.
    Default parameters follow those provided in the original paper.
    # Arguments
        lr: float >= 0. Learning rate.
        beta_1: float, 0 < beta < 1. Generally close to 1.

mvoelk

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003].
    
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and

mvoelk

mvoelk

mvoelk

// ==UserScript==
// @name           Google Redirect Remover
// @namespace      Google Redirect Remover
// @description    Stop google links from using their tracking redirect
// @version        1.0.0

// @include        https://*.google.com/*
// @include        https://*.google.de/*
// ==/UserScript==

mvoelk

# -*- coding: utf-8 -*-

import cv2
import numpy as np

from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, AvgPool2D, Activation
from tensorflow.keras.layers import Layer, BatchNormalization, ZeroPadding2D, Flatten, add
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.models import Model
from tensorflow.keras import initializers