test_data = util.batch_data(pickle['test'], time_batch_len = 1,
max_time_batches = -1, softmax = True)
else:
raise Exception("Other datasets not yet implemented")
print(config)
with tf.Graph().as_default(), tf.Session() as session:
with tf.variable_scope("model", reuse=None):
test_model = model_class(config, training=False)
saver = tf.train.Saver(tf.global_variables())
model_path = os.path.join(os.path.dirname(args.config_file),
config.model_name)
saver.restore(session, model_path)
test_loss, test_probs = util.run_epoch(session, test_model, test_data,
training=False, testing=True)
print('Testing Loss: {}'.format(test_loss))
if config.dataset == 'softmax':
if args.seperate:
nottingham_util.seperate_accuracy(test_probs, test_data, num_samples=args.num_samples)
else:
nottingham_util.accuracy(test_probs, test_data, num_samples=args.num_samples)
else:
util.accuracy(test_probs, test_data, num_samples=50)
sys.exit(1)
test_data = util.batch_data(pickle['test'], time_batch_len = 1,
max_time_batches = -1, softmax = True)
else:
raise Exception("Other datasets not yet implemented")
print config
with tf.Graph().as_default(), tf.Session() as session:
with tf.variable_scope("model", reuse=None):
test_model = model_class(config, training=False)
saver = tf.train.Saver(tf.all_variables())
model_path = os.path.join(os.path.dirname(args.config_file),
config.model_name)
saver.restore(session, model_path)
test_loss, test_probs = util.run_epoch(session, test_model, test_data,
training=False, testing=True)
print 'Testing Loss: {}'.format(test_loss)
if config.dataset == 'softmax':
if args.seperate:
nottingham_util.seperate_accuracy(test_probs, test_data, num_samples=args.num_samples)
else:
nottingham_util.accuracy(test_probs, test_data, num_samples=args.num_samples)
else:
util.accuracy(test_probs, test_data, num_samples=50)
sys.exit(1)