async_executor.py

# 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 tarfile
import paddle.fluid as fluid
import paddle
from paddle.fluid import core

URL = 'http://paddle-unittest-data.gz.bcebos.com/python_paddle_fluid_tests_demo_async-executor/train_data.tar.gz'
MD5 = '2a405a31508969b3ab823f42c0f522ca'


def bow_net(data,
            label,
            dict_dim=89528,
            emb_dim=128,
            hid_dim=128,
            hid_dim2=96,
            class_dim=2):
    """
    BOW net
    This model is from https://github.com/PaddlePaddle/models:
    models/fluid/PaddleNLP/text_classification/nets.py
    """
    # embedding
    emb = fluid.layers.embedding(
        input=data, size=[dict_dim, emb_dim], is_sparse=True)
    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
    bowh = fluid.layers.tanh(bow)
    # fc layer after conv
    fc_1 = fluid.layers.fc(input=bowh, size=hid_dim, act="tanh")
    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
    # probability of each class
    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
    # cross entropy loss
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    # mean loss
    avg_cost = fluid.layers.mean(x=cost)
    acc = fluid.layers.accuracy(input=prediction, label=label)
    return avg_cost, acc, prediction


def train():
    # Download data
    with tarfile.open(paddle.dataset.common.download(URL, "imdb", MD5)) as tarf:
        tarf.extractall(path='./')
        tarf.close()

    # Initialize dataset description
    dataset = fluid.DataFeedDesc('train_data/data.prototxt')
    dataset.set_batch_size(128)  # See API doc for how to change other fields
    print dataset.desc()  # Debug purpose: see what we get

    # define network
    # input text data
    data = fluid.layers.data(
        name="words", shape=[1], dtype="int64", lod_level=1)
    # label data
    label = fluid.layers.data(name="label", shape=[1], dtype="int64")

    avg_cost, acc, prediction = bow_net(data, label)
    sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
    opt_ops, weight_and_grad = sgd_optimizer.minimize(avg_cost)

    # Run startup program
    startup_program = fluid.default_startup_program()
    place = fluid.CPUPlace()
    executor = fluid.Executor(place)
    executor.run(startup_program)

    async_executor = fluid.AsyncExecutor(place)
    main_program = fluid.default_main_program()
    epochs = 10
    filelist = ["train_data/part-%d" % i for i in range(12)]
    for i in range(epochs):
        thread_num = 4
        async_executor.run(
            main_program,  # This can be changed during iteration
            dataset,  # This can be changed during iteration
            filelist,  # This can be changed during iteration
            thread_num,  # This can be changed during iteration
            [data, acc],  # Multiple fetch targets can be specified
            debug=False)
        fluid.io.save_inference_model('imdb/epoch%d.model' % i,
                                      [data.name, label.name], [acc], executor)


if __name__ == "__main__":
    train()