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author | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
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committer | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
commit | f41959ccb2d9d4c722fe8fc3351401d53bcf4900 (patch) | |
tree | ef0ca22cb2a5ac4bdec9d080d8e0788a53ed496d /tensorflow/models/image/cifar10/cifar10_eval.py |
TensorFlow: Initial commit of TensorFlow library.
TensorFlow is an open source software library for numerical computation
using data flow graphs.
Base CL: 107276108
Diffstat (limited to 'tensorflow/models/image/cifar10/cifar10_eval.py')
-rw-r--r-- | tensorflow/models/image/cifar10/cifar10_eval.py | 148 |
1 files changed, 148 insertions, 0 deletions
diff --git a/tensorflow/models/image/cifar10/cifar10_eval.py b/tensorflow/models/image/cifar10/cifar10_eval.py new file mode 100644 index 0000000000..73c224191d --- /dev/null +++ b/tensorflow/models/image/cifar10/cifar10_eval.py @@ -0,0 +1,148 @@ +"""Evaluation for CIFAR-10. + +Accuracy: +cifar10_train.py achieves 83.0% accuracy after 100K steps (256 epochs +of data) as judged by cifar10_eval.py. + +Speed: +On a single Tesla K40, cifar10_train.py processes a single batch of 128 images +in 0.25-0.35 sec (i.e. 350 - 600 images /sec). The model reaches ~86% +accuracy after 100K steps in 8 hours of training time. + +Usage: +Please see the tutorial and website for how to download the CIFAR-10 +data set, compile the program and train the model. + +http://tensorflow.org/tutorials/deep_cnn/ +""" +from datetime import datetime +import math +import time + +import tensorflow.python.platform +from tensorflow.python.platform import gfile +import numpy as np +import tensorflow as tf + +from tensorflow.models.image.cifar10 import cifar10 + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval', + """Directory where to write event logs.""") +tf.app.flags.DEFINE_string('eval_data', 'test', + """Either 'test' or 'train_eval'.""") +tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train', + """Directory where to read model checkpoints.""") +tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5, + """How often to run the eval.""") +tf.app.flags.DEFINE_integer('num_examples', 10000, + """Number of examples to run.""") +tf.app.flags.DEFINE_boolean('run_once', False, + """Whether to run eval only once.""") + + +def eval_once(saver, summary_writer, top_k_op, summary_op): + """Run Eval once. + + Args: + saver: Saver. + summary_writer: Summary writer. + top_k_op: Top K op. + summary_op: Summary op. + """ + with tf.Session() as sess: + ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir) + if ckpt and ckpt.model_checkpoint_path: + # Restores from checkpoint + saver.restore(sess, ckpt.model_checkpoint_path) + # Assuming model_checkpoint_path looks something like: + # /my-favorite-path/cifar10_train/model.ckpt-0, + # extract global_step from it. + global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] + else: + print 'No checkpoint file found' + return + + # Start the queue runners. + coord = tf.train.Coordinator() + try: + threads = [] + for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS): + threads.extend(qr.create_threads(sess, coord=coord, daemon=True, + start=True)) + + num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size)) + true_count = 0 # Counts the number of correct predictions. + total_sample_count = num_iter * FLAGS.batch_size + step = 0 + while step < num_iter and not coord.should_stop(): + predictions = sess.run([top_k_op]) + true_count += np.sum(predictions) + step += 1 + + # Compute precision @ 1. + precision = float(true_count) / float(total_sample_count) + print '%s: precision @ 1 = %.3f' % (datetime.now(), precision) + + summary = tf.Summary() + summary.ParseFromString(sess.run(summary_op)) + summary.value.add(tag='Precision @ 1', simple_value=precision) + summary_writer.add_summary(summary, global_step) + except Exception, e: # pylint: disable=broad-except + coord.request_stop(e) + + coord.request_stop() + coord.join(threads, stop_grace_period_secs=10) + + +def evaluate(): + """Eval CIFAR-10 for a number of steps.""" + with tf.Graph().as_default(): + # Get images and labels for CIFAR-10. + eval_data = FLAGS.eval_data == 'test' + images, labels = cifar10.inputs(eval_data=eval_data) + + # Build a Graph that computes the logits predictions from the + # inference model. + logits = cifar10.inference(images) + + # Calculate predictions. + top_k_op = tf.nn.in_top_k(logits, labels, 1) + + # Restore the moving average version of the learned variables for eval. + variable_averages = tf.train.ExponentialMovingAverage( + cifar10.MOVING_AVERAGE_DECAY) + variables_to_restore = {} + for v in tf.all_variables(): + if v in tf.trainable_variables(): + restore_name = variable_averages.average_name(v) + else: + restore_name = v.op.name + variables_to_restore[restore_name] = v + saver = tf.train.Saver(variables_to_restore) + + # Build the summary operation based on the TF collection of Summaries. + summary_op = tf.merge_all_summaries() + + graph_def = tf.get_default_graph().as_graph_def() + summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir, + graph_def=graph_def) + + while True: + eval_once(saver, summary_writer, top_k_op, summary_op) + if FLAGS.run_once: + break + time.sleep(FLAGS.eval_interval_secs) + + +def main(argv=None): # pylint: disable=unused-argument + cifar10.maybe_download_and_extract() + if gfile.Exists(FLAGS.eval_dir): + gfile.DeleteRecursively(FLAGS.eval_dir) + gfile.MakeDirs(FLAGS.eval_dir) + evaluate() + + +if __name__ == '__main__': + tf.app.run() |