foolbox | Python toolbox to create adversarial examples | Machine Learning library

set_invariant <- function(fn, var, predicate) {
    
    var <- rlang::enexpr(var)
    stopifnot(rlang::is_symbol(var))
    
    predicate <- rlang::enexpr(predicate)
    
    set_invariant_callback <- function(expr, ...) {
        if (ex  

f <- function(x) {
  y <- 2 * x
  x + y
}

expr <- body(f)
expr[[1]]
#> `{`
expr[[2]]
#> y <- 2 * x
expr[[3]]
#> x + y

f <- function(x) 2 * x
g <- function(y) f(y)

callbacks <- rewrite_callbacks() %>% 
    add_call_  

python train_new.py model/config/fc1-mnist-100-drop-50.yaml opt/config/sgd-mnist.yaml mnist-50000 --cuda --mc_dropout_passes 200

python train_new.py model/config/fc1-mnist-100.yaml opt/config/sgld-mnist-1.yaml mnist-50000 --cuda

python gan_pytorch.  

import tensorflow as tf
import numpy as np

from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.models import Model
from tensorflow

import tensorflow as tf
import json
# download mnist data and split into train and test sets
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
# reshape data to fit model
X_train = X_train.reshape(X_train.shape[0],