<commit_before>"""
This script evaluates trained models that have been saved to the filesystem.
See mnist_tutorial_picklable.py for instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np
import tensorflow as tf
from tensorflow.python.platform import flags
import logging

from cleverhans.loss import CrossEntropy
from cleverhans.utils_mnist import data_mnist
from cleverhans.utils_tf import train, model_eval
from cleverhans.attacks import FastGradientMethod
from cleverhans.utils import AccuracyReport, set_log_level
from cleverhans.serial import load

FLAGS = flags.FLAGS


def evaluate_model(filepath,
                   train_start=0, train_end=60000, test_start=0,
                   test_end=10000, batch_size=128,
                   testing=False, num_threads=None):
  """
  Run evaluation on a saved model
  :param filepath: path to model to evaluate
  :param train_start: index of first training set example
  :param train_end: index of last training set example
  :param test_start: index of first test set example
  :param test_end: index of last test set example
  :param batch_size: size of evaluation batches
  """

  # Set TF random seed to improve reproducibility
  tf.set_random_seed(1234)

  # Set logging level to see debug information
  set_log_level(logging.INFO)

  # Create TF session
  if num_threads:
    config_args = dict(intra_op_parallelism_threads=1)
  else:
    config_args = {}
  sess = tf.Session(config=tf.ConfigProto(**config_args))

  # Get MNIST test data
  x_train, y_train, x_test, y_test = data_mnist(train_start=train_start,
                                                train_end=train_end,
                                                test_start=test_start,
                                                test_end=test_end)<commit_msg>fix pickled model evaluation MNIST import<commit_after>"""
This script evaluates trained models that have been saved to the filesystem.
See mnist_tutorial_picklable.py for instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np
import tensorflow as tf
from tensorflow.python.platform import flags
import logging

from cleverhans.dataset import MNIST
from cleverhans.loss import CrossEntropy
from cleverhans.utils_tf import train, model_eval
from cleverhans.attacks import FastGradientMethod
from cleverhans.utils import AccuracyReport, set_log_level
from cleverhans.serial import load

FLAGS = flags.FLAGS


def evaluate_model(filepath,
                   train_start=0, train_end=60000, test_start=0,
                   test_end=10000, batch_size=128,
                   testing=False, num_threads=None):
  """
  Run evaluation on a saved model
  :param filepath: path to model to evaluate
  :param train_start: index of first training set example
  :param train_end: index of last training set example
  :param test_start: index of first test set example
  :param test_end: index of last test set example
  :param batch_size: size of evaluation batches
  """

  # Set TF random seed to improve reproducibility
  tf.set_random_seed(1234)

  # Set logging level to see debug information
  set_log_level(logging.INFO)

  # Create TF session
  if num_threads:
    config_args = dict(intra_op_parallelism_threads=1)
  else:
    config_args = {}
  sess = tf.Session(config=tf.ConfigProto(**config_args))

  # Get MNIST test data
  mnist = MNIST(train_start=train_start, train_end=train_end,
                test_start=test_start, test_end=test_end)
  x_train, y_train = mnist.get_set('train')
  x_test, y_test = mnist.get_set('test')

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{'dir': 'git-commits-cleaned', 'id': '5891848', 'n_tokens_mistral': 1125, 'n_tokens_neox': 1054, 'n_words': 355}