# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest
from test_eager_deletion_dynamic_rnn_base import TestBase
import paddle.fluid as fluid


def gru_net(data,
            label,
            dict_dim,
            emb_dim=128,
            hid_dim=128,
            hid_dim2=96,
            class_dim=2,
            emb_lr=400.0):
    emb = fluid.layers.embedding(
        input=data,
        size=[dict_dim, emb_dim],
        param_attr=fluid.ParamAttr(learning_rate=emb_lr))
    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3)
    gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
    gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
    gru_max_tanh = fluid.layers.tanh(gru_max)
    fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_cost = fluid.layers.mean(x=cost)
    return avg_cost


class GRUTest(TestBase):
    def setUp(self):
        self.net = gru_net


if __name__ == "__main__":
    unittest.main()
