remove accidentally-checked-in file

examples/mnist_bench.py

@@ -1,98 +0,0 @@
-# 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 = 3
-  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 = optimizers.momentum(step_size, mass=momentum_mass)
-
-  @jit
-  def update(i, opt_state, batch):
-    params = optimizers.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()
-
-  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 = optimizers.get_params(opt_state)
-    train_acc = accuracy(params, (train_images, train_labels))
-    test_acc = accuracy(params, (test_images, 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))