# 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 paddle import paddle.fluid as fluid import numpy as np import sys import math CLASS_DIM = 2 EMB_DIM = 128 BATCH_SIZE = 128 LSTM_SIZE = 128 def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size): emb = fluid.layers.embedding( input=data, size=[input_dim, emb_dim], is_sparse=True) sentence = fluid.layers.fc(input=emb, size=lstm_size, act='tanh') rnn = fluid.layers.DynamicRNN() with rnn.block(): word = rnn.step_input(sentence) prev_hidden = rnn.memory(value=0.0, shape=[lstm_size]) prev_cell = rnn.memory(value=0.0, shape=[lstm_size]) def gate_common(ipt, hidden, size): gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True) gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False) return gate0 + gate1 forget_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, lstm_size)) input_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, lstm_size)) output_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, lstm_size)) cell_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, lstm_size)) cell = forget_gate * prev_cell + input_gate * cell_gate hidden = output_gate * fluid.layers.tanh(x=cell) rnn.update_memory(prev_cell, cell) rnn.update_memory(prev_hidden, hidden) rnn.output(hidden) last = fluid.layers.sequence_last_step(rnn()) prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax") return prediction def inference_program(word_dict): data = fluid.layers.data( name="words", shape=[1], dtype="int64", lod_level=1) dict_dim = len(word_dict) pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE) return pred def train_program(prediction): label = fluid.layers.data(name="label", shape=[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....") 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 = 1 main_program = fluid.default_main_program() star_program = fluid.default_startup_program() 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) 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(fluid.default_startup_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]) 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) 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_conv.inference.model" train(use_cuda, params_dirname) infer(use_cuda, params_dirname) if __name__ == '__main__': use_cuda = False # set to True if training with GPU main(use_cuda)