# 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. from __future__ import print_function import os import paddle import paddle.fluid as fluid import numpy as np import sys import math import argparse CLASS_DIM = 2 EMB_DIM = 128 HID_DIM = 512 STACKED_NUM = 3 BATCH_SIZE = 128 def parse_args(): parser = argparse.ArgumentParser("stacked_lstm") parser.add_argument( '--enable_ce', action='store_true', help="If set, run the task with continuous evaluation logs.") parser.add_argument( '--use_gpu', type=int, default=0, help="Whether to use GPU or not.") parser.add_argument( '--num_epochs', type=int, default=1, help="number of epochs.") args = parser.parse_args() return args def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num): assert stacked_num % 2 == 1 emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True) fc1 = fluid.layers.fc(input=emb, size=hid_dim) lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim) inputs = [fc1, lstm1] for i in range(2, stacked_num + 1): fc = fluid.layers.fc(input=inputs, size=hid_dim) lstm, cell = fluid.layers.dynamic_lstm( input=fc, size=hid_dim, is_reverse=(i % 2) == 0) inputs = [fc, lstm] fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max') lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max') prediction = fluid.layers.fc( input=[fc_last, lstm_last], size=class_dim, act='softmax') return prediction def inference_program(word_dict): data = fluid.data(name="words", shape=[None], dtype="int64", lod_level=1) dict_dim = len(word_dict) net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM) return net def train_program(prediction): # prediction = inference_program(word_dict) label = fluid.data(name="label", shape=[None, 1], dtype="int64") cost = fluid.layers.cross_entropy(input=prediction, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=prediction, label=label) return [avg_cost, accuracy] def optimizer_func(): return fluid.optimizer.Adagrad(learning_rate=0.002) def train(use_cuda, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() print("Loading IMDB word dict....") word_dict = paddle.dataset.imdb.word_dict() print("Reading training data....") if args.enable_ce: train_reader = paddle.batch( paddle.dataset.imdb.train(word_dict), batch_size=BATCH_SIZE) else: train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.imdb.train(word_dict), buf_size=25000), batch_size=BATCH_SIZE) print("Reading testing data....") test_reader = paddle.batch( paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE) feed_order = ['words', 'label'] pass_num = args.num_epochs main_program = fluid.default_main_program() star_program = fluid.default_startup_program() if args.enable_ce: main_program.random_seed = 90 star_program.random_seed = 90 prediction = inference_program(word_dict) train_func_outputs = train_program(prediction) avg_cost = train_func_outputs[0] test_program = main_program.clone(for_test=True) # [avg_cost, accuracy] = train_program(prediction) sgd_optimizer = optimizer_func() sgd_optimizer.minimize(avg_cost) exe = fluid.Executor(place) def train_test(program, reader): count = 0 feed_var_list = [ program.global_block().var(var_name) for var_name in feed_order ] feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) test_exe = fluid.Executor(place) accumulated = len(train_func_outputs) * [0] for test_data in reader(): avg_cost_np = test_exe.run( program=program, feed=feeder_test.feed(test_data), fetch_list=train_func_outputs) accumulated = [ x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) ] count += 1 return [x / count for x in accumulated] def train_loop(): feed_var_list_loop = [ main_program.global_block().var(var_name) for var_name in feed_order ] feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) exe.run(star_program) for epoch_id in range(pass_num): for step_id, data in enumerate(train_reader()): metrics = exe.run( main_program, feed=feeder.feed(data), fetch_list=[var.name for var in train_func_outputs]) print("step: {0}, Metrics {1}".format( step_id, list(map(np.array, metrics)))) if (step_id + 1) % 10 == 0: avg_cost_test, acc_test = train_test(test_program, test_reader) print('Step {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( step_id, avg_cost_test, acc_test)) print("Step {0}, Epoch {1} Metrics {2}".format( step_id, epoch_id, list(map(np.array, metrics)))) if math.isnan(float(metrics[0])): sys.exit("got NaN loss, training failed.") if params_dirname is not None: fluid.io.save_inference_model(params_dirname, ["words"], prediction, exe) if args.enable_ce and epoch_id == pass_num - 1: print("kpis\tlstm_train_cost\t%f" % metrics[0]) print("kpis\tlstm_train_acc\t%f" % metrics[1]) print("kpis\tlstm_test_cost\t%f" % avg_cost_test) print("kpis\tlstm_test_acc\t%f" % acc_test) train_loop() def infer(use_cuda, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() word_dict = paddle.dataset.imdb.word_dict() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # Use fluid.io.load_inference_model to obtain the inference program desc, # the feed_target_names (the names of variables that will be feeded # data using feed operators), and the fetch_targets (variables that # we want to obtain data from using fetch operators). [inferencer, feed_target_names, fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) # Setup input by creating LoDTensor to represent sequence of words. # Here each word is the basic element of the LoDTensor and the shape of # each word (base_shape) should be [1] since it is simply an index to # look up for the corresponding word vector. # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]], # which has only one lod level. Then the created LoDTensor will have only # one higher level structure (sequence of words, or sentence) than the basic # element (word). Hence the LoDTensor will hold data for three sentences of # length 3, 4 and 2, respectively. # Note that lod info should be a list of lists. reviews_str = [ 'read the book forget the movie', 'this is a great movie', 'this is very bad' ] reviews = [c.split() for c in reviews_str] UNK = word_dict[''] lod = [] for c in reviews: lod.append([np.int64(word_dict.get(words, UNK)) for words in c]) base_shape = [[len(c) for c in lod]] tensor_words = fluid.create_lod_tensor(lod, base_shape, place) assert feed_target_names[0] == "words" results = exe.run( inferencer, feed={feed_target_names[0]: tensor_words}, fetch_list=fetch_targets, return_numpy=False) np_data = np.array(results[0]) for i, r in enumerate(np_data): print("Predict probability of ", r[0], " to be positive and ", r[1], " to be negative for review \'", reviews_str[i], "\'") def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return params_dirname = "understand_sentiment_stacked_lstm.inference.model" train(use_cuda, params_dirname) infer(use_cuda, params_dirname) if __name__ == '__main__': args = parse_args() use_cuda = args.use_gpu # set to True if training with GPU main(use_cuda)