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