【NLP保姆级教程】手把手带你fastText文本分类(附代码)

class fastTextModel(BaseModel):
    """
    A simple implementation of fasttext for text classification
    """
    def __init__(self, sequence_length, num_classes, vocab_size,
                 embedding_size, learning_rate, decay_steps, decay_rate,
                 l2_reg_lambda, is_training=True,
                 initializer=tf.random_normal_initializer(stddev=0.1)):
        self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.num_classes = num_classes
        self.sequence_length = sequence_length
        self.learning_rate = learning_rate
        self.decay_steps = decay_steps
        self.decay_rate = decay_rate
        self.is_training = is_training
        self.l2_reg_lambda = l2_reg_lambda
        self.initializer = initializer

        self.input_x = tf.placeholder(tf.int32, [None, self.sequence_length], name='input_x')
        self.input_y = tf.placeholder(tf.int32, [None, self.num_classes], name='input_y')

        self.global_step = tf.Variable(0, trainable=False, name='global_step')
        self.instantiate_weight()
        self.logits = self.inference()
        self.loss_val = self.loss()
        self.train_op = self.train()

        self.predictions = tf.argmax(self.logits, axis=1, name='predictions')
        correct_prediction = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'), name='accuracy')

    def instantiate_weight(self):
        with tf.name_scope('weights'):
            self.Embedding = tf.get_variable('Embedding', shape=[self.vocab_size, self.embedding_size],
                                             initializer=self.initializer)
            self.W_projection = tf.get_variable('W_projection', shape=[self.embedding_size, self.num_classes],
                                                initializer=self.initializer)
            self.b_projection = tf.get_variable('b_projection', shape=[self.num_classes])


    def inference(self):
        """
        1. word embedding
        2. average embedding
        3. linear classifier
        :return:
        """
        # embedding layer
        with tf.name_scope('embedding'):
            words_embedding = tf.nn.embedding_lookup(self.Embedding, self.input_x)
            self.average_embedding = tf.reduce_mean(words_embedding, axis=1)

        logits = tf.matmul(self.average_embedding, self.W_projection) +self.b_projection

        return logits


    def loss(self):
        # loss
        with tf.name_scope('loss'):
            losses = tf.nn.softmax_cross_entropy_with_logits(labels=self.input_y, logits=self.logits)
            data_loss = tf.reduce_mean(losses)
            l2_loss = tf.add_n([tf.nn.l2_loss(cand_var) for cand_var in tf.trainable_variables()
                                if 'bias' not in cand_var.name]) * self.l2_reg_lambda
            data_loss += l2_loss * self.l2_reg_lambda
            return data_loss

    def train(self):
        with tf.name_scope('train'):
            learning_rate = tf.train.exponential_decay(self.learning_rate, self.global_step,
                                                       self.decay_steps, self.decay_rate,
                                                       staircase=True)

            train_op = tf.contrib.layers.optimize_loss(self.loss_val, global_step=self.global_step,
                                                      learning_rate=learning_rate, optimizer='Adam')

        return train_op

def prepocess():
    """
    For load and process data
    :return:
    """
    print("Loading data...")
    x_text, y = data_process.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
    # bulid vocabulary
    max_document_length = max(len(x.split(' ')) for x in x_text)
    vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
    x = np.array(list(vocab_processor.fit_transform(x_text)))

    # shuffle
    np.random.seed(10)
    shuffle_indices = np.random.permutation(np.arange(len(y)))
    x_shuffled = x[shuffle_indices]
    y_shuffled = y[shuffle_indices]

    # split train/test dataset
    dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
    x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
    y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
    del x, y, x_shuffled, y_shuffled

    print('Vocabulary Size: {:d}'.format(len(vocab_processor.vocabulary_)))
    print('Train/Dev split: {:d}/{:d}'.format(len(y_train), len(y_dev)))
    return x_train, y_train, vocab_processor, x_dev, y_dev


def train(x_train, y_train, vocab_processor, x_dev, y_dev):
    with tf.Graph().as_default():
        session_conf = tf.ConfigProto(
            # allows TensorFlow to fall back on a device with a certain operation implemented
            allow_soft_placement= FLAGS.allow_soft_placement,
            # allows TensorFlow log on which devices (CPU or GPU) it places operations
            log_device_placement=FLAGS.log_device_placement
        )
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            # initialize cnn
            fasttext = fastTextModel(sequence_length=x_train.shape[1],
                      num_classes=y_train.shape[1],
                      vocab_size=len(vocab_processor.vocabulary_),
                      embedding_size=FLAGS.embedding_size,
                      l2_reg_lambda=FLAGS.l2_reg_lambda,
                      is_training=True,
                      learning_rate=FLAGS.learning_rate,
                      decay_steps=FLAGS.decay_steps,
                      decay_rate=FLAGS.decay_rate
                    )

            # output dir for models and summaries
            timestamp = str(time.time())
            out_dir = os.path.abspath(os.path.join(os.path.curdir, 'run', timestamp))
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)
            print('Writing to {} n'.format(out_dir))

            # checkpoint dir. checkpointing – saving the parameters of your model to restore them later on.
            checkpoint_dir = os.path.abspath(os.path.join(out_dir, FLAGS.ckpt_dir))
            checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
            if not os.path.exists(checkpoint_dir):
                os.makedirs(checkpoint_dir)
            saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)

            # Write vocabulary
            vocab_processor.save(os.path.join(out_dir, 'vocab'))

            # Initialize all
            sess.run(tf.global_variables_initializer())


            def train_step(x_batch, y_batch):
                """
                A single training step
                :param x_batch:
                :param y_batch:
                :return:
                """
                feed_dict = {
                    fasttext.input_x: x_batch,
                    fasttext.input_y: y_batch,
                }
                _, step, loss, accuracy = sess.run(
                    [fasttext.train_op, fasttext.global_step, fasttext.loss_val, fasttext.accuracy],
                    feed_dict=feed_dict
                )
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))

            def dev_step(x_batch, y_batch):
                """
                Evaluate model on a dev set
                Disable dropout
                :param x_batch:
                :param y_batch:
                :param writer:
                :return:
                """
                feed_dict = {
                    fasttext.input_x: x_batch,
                    fasttext.input_y: y_batch,
                }
                step, loss, accuracy = sess.run(
                    [fasttext.global_step, fasttext.loss_val, fasttext.accuracy],
                    feed_dict=feed_dict
                )
                time_str = datetime.datetime.now().isoformat()
                print("dev results:{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))

            # generate batches
            batches = data_process.batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
            # training loop
            for batch in batches:
                x_batch, y_batch = zip(*batch)
                train_step(x_batch, y_batch)
                current_step = tf.train.global_step(sess, fasttext.global_step)
                if current_step % FLAGS.validate_every == 0:
                    print('n Evaluation:')
                    dev_step(x_dev, y_dev)
                    print('')

            path = saver.save(sess, checkpoint_prefix, global_step=current_step)
            print('Save model checkpoint to {} n'.format(path))

def main(argv=None):
    x_train, y_train, vocab_processor, x_dev, y_dev = prepocess()
    train(x_train, y_train, vocab_processor, x_dev, y_dev)

if __name__ == '__main__':
    tf.app.run()