def test_accuracy():
x1 = tf.constant(np.array([[1.0],
[2],
[3]]))
x2 = tf.constant(np.array([1,
0,
3]))
acc_t = modeling.accuracy(x1, x2)
init()
acc = sess.run(acc_t)
ep = 0.001
expect = 0.666
assert expect - ep < acc < expect + ep
from tfutils import predict
from tfutils import modeling
import data
FLAGS = tf.app.flags.FLAGS
flags = tf.app.flags
flags.DEFINE_string('checkpoint_dir', 'logs/checkpoints/',
"""directory containing model.pbtxt, saver.pbtxt, parameter
checkpoints""")
flags.DEFINE_boolean('use_validation_data', True,
"""whether to use validation data or training data""")
model = predict.load(FLAGS.checkpoint_dir)
accuracy = modeling.accuracy(model.label_node, model.out_node)
num_examples = data.NUM_TEST if FLAGS.use_validation_data else data.NUM_TRAIN
steps = num_examples / data.BATCH_SIZE + 1
data_str = "test" if FLAGS.use_validation_data else "training"
print "Running " + data_str + " data"
with data.batcher() as batcher:
with tf.Session() as sess:
model.restore(sess)
if FLAGS.use_validation_data:
next_data = batcher.next_validation_batch
else:
next_data = batcher.next_training_batch
