train.py

import os
import six
import sys
import time
import unittest
import contextlib

import paddle
import paddle.fluid as fluid

import utils
from nets import bow_net
from nets import cnn_net
from nets import lstm_net
from nets import gru_net


def train(train_reader,
          word_dict,
          network,
          use_cuda,
          parallel,
          save_dirname,
          lr=0.2,
          pass_num=30):
    """
    train network
    """
    data = fluid.layers.data(
        name="words", shape=[1], dtype="int64", lod_level=1)

    label = fluid.layers.data(name="label", shape=[1], dtype="int64")

    if not parallel:
        cost, acc, prediction = network(data, label, len(word_dict))
    else:
        places = fluid.layers.device.get_places(device_count=2)
        pd = fluid.layers.ParallelDo(places)
        with pd.do():
            cost, acc, prediction = network(
                pd.read_input(data), pd.read_input(label), len(word_dict))

            pd.write_output(cost)
            pd.write_output(acc)

        cost, acc = pd()
        cost = fluid.layers.mean(cost)
        acc = fluid.layers.mean(acc)

    sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=lr)
    sgd_optimizer.minimize(cost)

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)
    feeder = fluid.DataFeeder(feed_list=[data, label], place=place)

    # For internal continuous evaluation
    if "CE_MODE_X" in os.environ:
        fluid.default_startup_program().random_seed = 110
    exe.run(fluid.default_startup_program())
    for pass_id in six.moves.xrange(pass_num):
        pass_start = time.time()
        data_size, data_count, total_acc, total_cost = 0, 0, 0.0, 0.0
        for data in train_reader():
            avg_cost_np, avg_acc_np = exe.run(fluid.default_main_program(),
                                              feed=feeder.feed(data),
                                              fetch_list=[cost, acc])
            data_size = len(data)
            total_acc += data_size * avg_acc_np
            total_cost += data_size * avg_cost_np
            data_count += data_size
        avg_cost = total_cost / data_count

        avg_acc = total_acc / data_count
        print("pass_id: %d, avg_acc: %f, avg_cost: %f, pass_time_cost: %f" %
              (pass_id, avg_acc, avg_cost, time.time() - pass_start))

        epoch_model = save_dirname + "/" + "epoch" + str(pass_id)
        fluid.io.save_inference_model(epoch_model, ["words", "label"], acc, exe)

        pass_end = time.time()
        # For internal continuous evaluation
        if "CE_MODE_X" in os.environ:
            print("kpis	train_acc	%f" % avg_acc)
            print("kpis	train_cost	%f" % avg_cost)
            print("kpis	train_duration	%f" % (pass_end - pass_start))


def train_net():
    word_dict, train_reader, test_reader = utils.prepare_data(
        "imdb", self_dict=False, batch_size=128, buf_size=50000)

    if sys.argv[1] == "bow":
        train(
            train_reader,
            word_dict,
            bow_net,
            use_cuda=False,
            parallel=False,
            save_dirname="bow_model",
            lr=0.002,
            pass_num=30)
    elif sys.argv[1] == "cnn":
        train(
            train_reader,
            word_dict,
            cnn_net,
            use_cuda=True,
            parallel=False,
            save_dirname="cnn_model",
            lr=0.01,
            pass_num=30)
    elif sys.argv[1] == "lstm":
        train(
            train_reader,
            word_dict,
            lstm_net,
            use_cuda=True,
            parallel=False,
            save_dirname="lstm_model",
            lr=0.05,
            pass_num=30)
    elif sys.argv[1] == "gru":
        train(
            train_reader,
            word_dict,
            gru_net,
            use_cuda=True,
            parallel=False,
            save_dirname="gru_model",
            lr=0.05,
            pass_num=30)
    else:
        print("network name cannot be found!")
        sys.exit(1)


if __name__ == "__main__":
    train_net()