def do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_set):
"""Runs one evaluation against the full epoch of data.
Args:
sess: The session in which the model has been trained.
eval_correct: The Tensor that returns the number of correct predictions.
images_placeholder: The images placeholder.
labels_placeholder: The labels placeholder.
data_set: The set of images and labels to evaluate, from
input_data.read_data_sets().
"""
# And run one epoch of eval.
true_count = 0 # Counts the number of correct predictions.
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set,
images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
precision = true_count / num_examples
print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' %
(num_examples, true_count, precision))
def do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_set):
"""Runs one evaluation against the full epoch of data.
Args:
sess: The session in which the model has been trained.
eval_correct: The Tensor that returns the number of correct predictions.
images_placeholder: The images placeholder.
labels_placeholder: The labels placeholder.
data_set: The set of images and labels to evaluate, from
input_data.read_data_sets().
"""
# And run one epoch of eval.
true_count = 0 # Counts the number of correct predictions.
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set,
images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
precision = true_count / num_examples
print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' %
(num_examples, true_count, precision))
def do_eval_summary(tag, sess, eval_correct, images_placeholder,
labels_placeholder, data_set):
true_count = 0
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set, images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
error = 1 - true_count / num_examples
return sess.run(tf.scalar_summary(tag, tf.identity(error)))
def do_eval_summary(tag,
sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_set):
true_count = 0
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set,
images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
error = 1 - true_count / num_examples
return sess.run(tf.scalar_summary(tag, tf.identity(error)))
from data import read_data_sets
from data import placeholder_inputs, fill_feed_dict
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
tf.app.flags.DEFINE_string('hello', 'Hello World!', 'Example argument')
flags.DEFINE_integer('K', 5000, 'Number of top-K words')
flags.DEFINE_integer('batch_size', 10, 'Size of batch')
flags.DEFINE_integer('max_steps', 1000, 'Maximum number of steps')
train, validation, test = read_data_sets(train_ratio=.8,
validation_ratio=.1,
interval=(5, 15))
input_placeholder, label_placeholder = placeholder_inputs(FLAGS.batch_size)
sess = tf.Session()
for step in xrange(FLAGS.max_steps):
feed_dict = fill_feed_dict(train, input_placeholder, label_placeholder)
sess.run([], feed_dict=feed_dict)
# input sequence length -> dimension = input_length * 5000
input_length = FLAGS.input_length
