情感分析¶

#文本卷积神经网络
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
    emb = fluid.layers.embedding(
        input=data, size=[input_dim, emb_dim], is_sparse=True)
    conv_3 = fluid.nets.sequence_conv_pool(
        input=emb,
        num_filters=hid_dim,
        filter_size=3,
        act="tanh",
        pool_type="sqrt")
    conv_4 = fluid.nets.sequence_conv_pool(
        input=emb,
        num_filters=hid_dim,
        filter_size=4,
        act="tanh",
        pool_type="sqrt")
    prediction = fluid.layers.fc(
        input=[conv_3, conv_4], size=class_dim, act="softmax")
    return prediction

#栈式双向LSTM
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):

    #计算词向量
    emb = fluid.layers.embedding(
        input=data, size=[input_dim, emb_dim], is_sparse=True)

    #第一层栈
    #全连接层
    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
    #lstm层
    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)

    inputs = [fc1, lstm1]

    #其余的所有栈结构
    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]

    #池化层
    fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
    lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')

    #全连接层，softmax预测
    prediction = fluid.layers.fc(
        input=[fc_last, lstm_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)
    net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
    # net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM, STACKED_NUM)
    return net

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]   #返回平均cost和准确率acc

#优化函数
def optimizer_func():
    return fluid.optimizer.Adagrad(learning_rate=0.002)

use_cuda = False  #在cpu上进行训练
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)

word_dict

exe = fluid.Executor(place)
prediction = inference_program(word_dict)
[avg_cost, accuracy] = train_program(prediction)#训练程序
sgd_optimizer = optimizer_func()#训练优化函数
sgd_optimizer.minimize(avg_cost)

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([avg_cost, accuracy]) * [0]
    for test_data in reader():
        avg_cost_np = test_exe.run(
            program=program,
            feed=feeder_test.feed(test_data),
            fetch_list=[avg_cost, accuracy])
        accumulated = [
            x[0] + x[1][0] for x in zip(accumulated, avg_cost_np)
        ]
        count += 1
    return [x / count for x in accumulated]

# Specify the directory path to save the parameters
params_dirname = "understand_sentiment_conv.inference.model"

feed_order = ['words', 'label']
pass_num = 1  #训练循环的轮数

#程序主循环部分
def train_loop(main_program):
    #启动上文构建的训练器
    exe.run(fluid.default_startup_program())

    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)

    test_program = fluid.default_main_program().clone(for_test=True)

    #训练循环
    for epoch_id in range(pass_num):
        for step_id, data in enumerate(train_reader()):
            #运行训练器  
            metrics = exe.run(main_program,
                              feed=feeder.feed(data),
                              fetch_list=[avg_cost, accuracy])

            #测试结果
            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 step_id == 30:
                if params_dirname is not None:
                    fluid.io.save_inference_model(params_dirname, ["words"],
                                                  prediction, exe)#保存模型
                return

train_loop(fluid.default_main_program())

place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()

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['<unk>']
lod = []
for c in reviews:
    lod.append([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)