def restore_model_from_ckpt(ckpt_dir, ckpt_file):
"""restore model from ckpt file."""
# load params
params_json_file = os.path.join(ckpt_dir, 'params.json')
params = utils.generate_hparams(params_json_file)
params.in_val_data_dir = FLAGS.in_test_data_dir
params.ood_val_data_dir = FLAGS.ood_test_data_dir
# create model
tf.reset_default_graph()
model = generative.SeqModel(params)
model.reset()
model.restore_from_ckpt(ckpt_file)
return params, model
# flags.DEFINE_float('b', .008)
if __name__ == "__main__":
ckpt_dir = '../outputs/generative_l250_bs100_lr0.0005_hr30_nrFalse_regl2_regw0.000000_fi-1_mt0.00/model/'
params_json_file = os.path.join(ckpt_dir, 'params.json')
params = utils.generate_hparams(params_json_file)
params.in_val_data_dir = '../data/before_2011_in_tr/'
params.ood_val_data_dir = '../data/between_2011-2016_ood_val/'
# specify test datasets for eval
params.in_val_file_pattern = 'in_tr'
params.ood_val_file_pattern = 'ood_val'
(_, in_dataset, _) = generative.load_datasets(
params, mode_eval=True)
model = generative.SeqModel(params)
model.reset()
test_dataset = in_dataset.batch(model._params.batch_size)
test_iterator = test_dataset.make_one_shot_iterator()
model.test_handle = model.sess.run(test_iterator.string_handle())
x_test = []
num_samples = 100000
for _ in range(num_samples // model._params.batch_size):
out = model.sess.run(
[model.x],
feed_dict={
model.handle: model.test_handle,
model.dropout_rate: 0
})
x_test.append(out[0])
x = np.array(x_test)