new_contents: # Copyright 2018 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """A basic MNIST example using JAX together with the mini-libraries stax, for
  neural network building, and optimizers, for first-order stochastic optimization.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import time
  import itertools
  
  import numpy.random as npr
  
  import jax.numpy as np
  from jax.config import config
  from jax import jit, grad, random
  from jax.experimental import optimizers
  from jax.experimental import stax
  from jax.experimental.stax import Dense, Relu, LogSoftmax
  from examples import datasets
  
  
  def loss(params, batch):
    inputs, targets = batch
    preds = predict(params, inputs)
    return -np.mean(preds * targets)
  
  def accuracy(params, batch):
    inputs, targets = batch
    target_class = np.argmax(targets, axis=1)
    predicted_class = np.argmax(predict(params, inputs), axis=1)
    return np.mean(predicted_class == target_class)
  
  init_random_params, predict = stax.serial(
      Dense(1024), Relu,
      Dense(1024), Relu,
      Dense(10), LogSoftmax)
  
  if __name__ == "__main__":
    rng = random.PRNGKey(0)
  
    step_size = 0.001
    num_epochs = 10
    batch_size = 128
    momentum_mass = 0.9
  
    train_images, train_labels, test_images, test_labels = datasets.mnist()
    num_train = train_images.shape[0]
    num_complete_batches, leftover = divmod(num_train, batch_size)
    num_batches = num_complete_batches + bool(leftover)
  
    def data_stream():
      rng = npr.RandomState(0)
      while True:
        perm = rng.permutation(num_train)
        for i in range(num_batches):
          batch_idx = perm[i * batch_size:(i + 1) * batch_size]
          yield train_images[batch_idx], train_labels[batch_idx]
    batches = data_stream()
  
    opt_init, opt_update, get_params = optimizers.momentum(step_size, mass=momentum_mass)
  
    @jit
    def update(i, opt_state, batch):
      params = get_params(opt_state)
      return opt_update(i, grad(loss)(params, batch), opt_state)
  
    _, init_params = init_random_params(rng, (-1, 28 * 28))
    opt_state = opt_init(init_params)
    itercount = itertools.count()
  
  
    ###################################################################
    # BEGIN CLEVERHANS TEST
    ###################################################################
  
    from jax import vmap
    @jit
    def generate(images, labels, params, eps=0.05):
      def loss_adv(image, label):
        pred = predict(params, image)
        return - np.sum(label * pred)
      grads_fn = vmap(grad(loss_adv), in_axes=(0, 0), out_axes=0)
      grads = grads_fn(images, labels)
      perturbations = eps * np.sign(grads)
      return images + perturbations
  
    ###################################################################
    # END CLEVERHANS TEST
    ###################################################################
  
    print("\nStarting training...")
    for epoch in range(num_epochs):
      start_time = time.time()
      for _ in range(num_batches):
        opt_state = update(next(itercount), opt_state, next(batches))
      epoch_time = time.time() - start_time
  
      params = get_params(opt_state)
      train_acc = accuracy(params, (train_images, train_labels))
      test_acc = accuracy(params, (test_images, test_labels))
      test_images_adv = generate(test_images, test_labels, params, eps=0.25)
      test_acc_adv = accuracy(params, (test_images_adv, test_labels))
      print("Epoch {} in {:0.2f} sec".format(epoch, epoch_time))
      print("Training set accuracy {}".format(train_acc))
      print("Test set accuracy {}".format(test_acc))
      print("Test set accuracy adv {}".format(test_acc_adv))
  
subject: add wip jax tutorial