t2

@@ -84,10 +84,10 @@ if __name__ == '__main__':
 
 
     # User options
     # User options
     args.add_argument('--output', type=int, default=1)
     args.add_argument('--output', type=int, default=1)
-    args.add_argument('--epochs', type=int, default=100)
+    args.add_argument('--epochs', type=int, default=200)
     args.add_argument('--batch', type=int, default=3000)
     args.add_argument('--batch', type=int, default=3000)
     args.add_argument('--strmaxlen', type=int, default=400)
     args.add_argument('--strmaxlen', type=int, default=400)
-    args.add_argument('--embedding', type=int, default=30)
+    args.add_argument('--embedding', type=int, default=50)
     args.add_argument('--threshold', type=float, default=0.5)
     args.add_argument('--threshold', type=float, default=0.5)
     config = args.parse_args()
     config = args.parse_args()
 
 
@@ -97,18 +97,17 @@ if __name__ == '__main__':
     # 모델의 specification
     # 모델의 specification
     input_size = config.embedding*config.strmaxlen
     input_size = config.embedding*config.strmaxlen
     output_size = 1
     output_size = 1
-    learning_rate = 0.001
+    learning_rate = 0.0003
     character_size = 251
     character_size = 251
 
 
     x = tf.placeholder(tf.int32, [None, config.strmaxlen])
     x = tf.placeholder(tf.int32, [None, config.strmaxlen])
     y_ = tf.placeholder(tf.float32, [None, output_size])
     y_ = tf.placeholder(tf.float32, [None, output_size])
     keep_probs = tf.placeholder(tf.float32)
     keep_probs = tf.placeholder(tf.float32)
     # 임베딩
     # 임베딩
-    with tf.name_scope('embedding'):
-        char_embedding = tf.get_variable('char_embedding', [character_size, config.embedding])
-        embedded_chars_base = tf.nn.embedding_lookup(char_embedding, x)
-        embedded = tf.expand_dims(embedded_chars_base, -1)
-        print("emb", embedded.shape)
+    char_embedding = tf.get_variable('char_embedding', [character_size, config.embedding])
+    embedded_chars_base = tf.nn.embedding_lookup(char_embedding, x)
+    embedded = tf.expand_dims(embedded_chars_base, -1)
+    print("emb", embedded.shape)
     
     
     # MODEL
     # MODEL
     l2_conv = tf.layers.conv2d(embedded, 256, [2, config.embedding], activation=tf.nn.relu)
     l2_conv = tf.layers.conv2d(embedded, 256, [2, config.embedding], activation=tf.nn.relu)

+# -*- coding: utf-8 -*-
+
+"""
+Copyright 2018 NAVER Corp.
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+associated documentation files (the "Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+The above copyright notice and this permission notice shall be included in all copies or substantial
+portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+"""
+
+import os
+
+import numpy as np
+
+from kor_char_parser import decompose_str_as_one_hot
+
+
+class MovieReviewDataset():
+    """
+    영화리뷰 데이터를 읽어서, tuple (데이터, 레이블)의 형태로 리턴하는 파이썬 오브젝트 입니다.
+    """
+    def __init__(self, dataset_path: str, max_length: int):
+        """
+        initializer
+        :param dataset_path: 데이터셋 root path
+        :param max_length: 문자열의 최대 길이
+        """
+        # 데이터, 레이블 각각의 경로
+        data_review = os.path.join(dataset_path, 'train', 'train_data')
+        data_label = os.path.join(dataset_path, 'train', 'train_label')
+
+        # 영화리뷰 데이터를 읽고 preprocess까지 진행합니다
+        with open(data_review, 'rt', encoding='utf-8') as f:
+            self.reviews = preprocess(f.readlines(), max_length)
+        # 영화리뷰 레이블을 읽고 preprocess까지 진행합니다.
+        with open(data_label) as f:
+            self.labels = [[np.float32(x)] for x in f.readlines()]
+
+    def __len__(self):
+        """
+        :return: 전체 데이터의 수를 리턴합니다
+        """
+        return len(self.reviews)
+
+    def __getitem__(self, idx):
+        """
+        :param idx: 필요한 데이터의 인덱스
+        :return: 인덱스에 맞는 데이터, 레이블 pair를 리턴합니다
+        """
+        return self.reviews[idx], self.labels[idx]
+
+
+def preprocess(data: list, max_length: int):
+    """
+     입력을 받아서 딥러닝 모델이 학습 가능한 포맷으로 변경하는 함수입니다.
+     기본 제공 알고리즘은 char2vec이며, 기본 모델이 MLP이기 때문에, 입력 값의 크기를 모두 고정한 벡터를 리턴합니다.
+     문자열의 길이가 고정값보다 길면 긴 부분을 제거하고, 짧으면 0으로 채웁니다.
+    :param data: 문자열 리스트 ([문자열1, 문자열2, ...])
+    :param max_length: 문자열의 최대 길이
+    :return: 벡터 리스트 ([[0, 1, 5, 6], [5, 4, 10, 200], ...]) max_length가 4일 때
+    """
+    vectorized_data = [decompose_str_as_one_hot(datum, warning=False) for datum in data]
+    zero_padding = np.zeros((len(data), max_length), dtype=np.int32)
+    for idx, seq in enumerate(vectorized_data):
+        length = len(seq)
+        if length >= max_length:
+            length = max_length
+            zero_padding[idx, :length] = np.array(seq)[:length]
+        else:
+            zero_padding[idx, :length] = np.array(seq)
+    return zero_padding

+# -*- coding: utf-8 -*-
+
+import argparse
+import os
+
+import numpy as np
+import tensorflow as tf
+
+import nsml
+from nsml import DATASET_PATH, HAS_DATASET, IS_ON_NSML
+from dataset import MovieReviewDataset, preprocess
+
+
+# DONOTCHANGE: They are reserved for nsml
+# This is for nsml leaderboard
+def bind_model(sess, config):
+    # 학습한 모델을 저장하는 함수입니다.
+    def save(dir_name, *args):
+        # directory
+        os.makedirs(dir_name, exist_ok=True)
+        saver = tf.train.Saver()
+        saver.save(sess, os.path.join(dir_name, 'model'))
+
+    # 저장한 모델을 불러올 수 있는 함수입니다.
+    def load(dir_name, *args):
+        saver = tf.train.Saver()
+        # find checkpoint
+        ckpt = tf.train.get_checkpoint_state(dir_name)
+        if ckpt and ckpt.model_checkpoint_path:
+            checkpoint = os.path.basename(ckpt.model_checkpoint_path)
+            saver.restore(sess, os.path.join(dir_name, checkpoint))
+        else:
+            raise NotImplemented('No checkpoint!')
+        print('Model loaded')
+
+    def infer(raw_data, **kwargs):
+        """
+        :param raw_data: raw input (여기서는 문자열)을 입력받습니다
+        :param kwargs:
+        :return:
+        """
+        # dataset.py에서 작성한 preprocess 함수를 호출하여, 문자열을 벡터로 변환합니다
+        preprocessed_data = preprocess(raw_data, config.strmaxlen)
+        # 저장한 모델에 입력값을 넣고 prediction 결과를 리턴받습니다
+        pred = sess.run(output_sigmoid, feed_dict={x: preprocessed_data})
+        clipped = np.array(pred > config.threshold, dtype=np.int)
+        # DONOTCHANGE: They are reserved for nsml
+        # 리턴 결과는 [(확률, 0 or 1)] 의 형태로 보내야만 리더보드에 올릴 수 있습니다. 리더보드 결과에 확률의 값은 영향을 미치지 않습니다
+        return list(zip(pred.flatten(), clipped.flatten()))
+
+    # DONOTCHANGE: They are reserved for nsml
+    # nsml에서 지정한 함수에 접근할 수 있도록 하는 함수입니다.
+    nsml.bind(save=save, load=load, infer=infer)
+
+
+def _batch_loader(iterable, n=1):
+    """
+    데이터를 배치 사이즈만큼 잘라서 보내주는 함수입니다. PyTorch의 DataLoader와 같은 역할을 합니다
+    :param iterable: 데이터 list, 혹은 다른 포맷
+    :param n: 배치 사이즈
+    :return:
+    """
+    length = len(iterable)
+    for n_idx in range(0, length, n):
+        yield iterable[n_idx:min(n_idx + n, length)]
+
+
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+
+if __name__ == '__main__':
+    args = argparse.ArgumentParser()
+    # DONOTCHANGE: They are reserved for nsml
+    args.add_argument('--mode', type=str, default='train')
+    args.add_argument('--pause', type=int, default=0)
+    args.add_argument('--iteration', type=str, default='0')
+
+    # User options
+    args.add_argument('--output', type=int, default=1)
+    args.add_argument('--epochs', type=int, default=10)
+    args.add_argument('--batch', type=int, default=3000)
+    args.add_argument('--strmaxlen', type=int, default=300)
+    args.add_argument('--embedding', type=int, default=50)
+    args.add_argument('--threshold', type=float, default=0.5)
+    config = args.parse_args()
+
+    if not HAS_DATASET and not IS_ON_NSML:  # It is not running on nsml
+        DATASET_PATH = '../sample_data/movie/'
+
+    # 모델의 specification
+    input_size = config.embedding*config.strmaxlen
+    output_size = 1
+    learning_rate = 0.001
+    character_size = 251
+
+    x = tf.placeholder(tf.int32, [None, config.strmaxlen])
+    y_ = tf.placeholder(tf.float32, [None, output_size])
+    keep_probs = tf.placeholder(tf.float32)
+    # 임베딩
+    char_embedding = tf.get_variable('char_embedding', [character_size, config.embedding])
+    embedded_chars_base = tf.nn.embedding_lookup(char_embedding, x)
+    embedded = tf.expand_dims(embedded_chars_base, -1)
+    print("emb", embedded.shape)
+    
+    # MODEL
+    l2_conv = tf.layers.conv2d(embedded, 256, [2, config.embedding], activation=tf.nn.relu)
+    print("l2", l2_conv.shape)
+    l2_pool = tf.layers.max_pooling2d(l2_conv, [character_size-2+1, 1], strides=(1,1))
+    print("l2 pool", l2_pool.shape)
+
+    l3_conv = tf.layers.conv2d(embedded, 256, [3, config.embedding], activation=tf.nn.relu)
+    print("l3", l3_conv.shape)
+    l3_pool = tf.layers.max_pooling2d(l3_conv, [character_size-3+1, 1], strides=(1,1))
+    print("l3 pool", l3_pool.shape)
+
+    concat = tf.concat([l2_pool, l3_pool], 3)
+    print('concat', concat.shape)
+    flatten = tf.contrib.layers.flatten(concat)
+    print('flattne', flatten.shape)
+
+    dense = tf.layers.dense(flatten, 256, activation=tf.nn.relu)
+
+    drop = tf.layers.dropout(dense, keep_probs)
+    output_sigmoid = 10*tf.layers.dense(drop, output_size, activation=tf.nn.sigmoid)
+
+
+    # loss와 optimizer
+    binary_cross_entropy = -tf.reduce_sum(y_*tf.log(tf.clip_by_value(tf.nn.log_softmax(output_sigmoid),1e-10,1.0)))
+    train_step = tf.train.AdamOptimizer(learning_rate).minimize(binary_cross_entropy)
+
+    sess = tf.InteractiveSession()
+    tf.global_variables_initializer().run()
+
+    # DONOTCHANGE: Reserved for nsml
+    bind_model(sess=sess, config=config)
+
+    # DONOTCHANGE: Reserved for nsml
+    if config.pause:
+        nsml.paused(scope=locals())
+
+    if config.mode == 'train':
+        # 데이터를 로드합니다.
+        dataset = MovieReviewDataset(DATASET_PATH, config.strmaxlen)
+        dataset_len = len(dataset)
+        one_batch_size = dataset_len//config.batch
+        if dataset_len % config.batch != 0:
+            one_batch_size += 1
+        # epoch마다 학습을 수행합니다.
+        for epoch in range(config.epochs):
+            avg_loss = 0.0
+            for i, (data, labels) in enumerate(_batch_loader(dataset, config.batch)):
+                _, loss = sess.run([train_step, binary_cross_entropy],
+                                   feed_dict={x: data, y_: labels, keep_probs: 1.})
+                print('Batch : ', i + 1, '/', one_batch_size,
+                      ', BCE in this minibatch: ', float(loss))
+                avg_loss += float(loss)
+            print('epoch:', epoch, ' train_loss:', float(avg_loss/one_batch_size))
+            nsml.report(summary=True, scope=locals(), epoch=epoch, epoch_total=config.epochs,
+                        train__loss=float(avg_loss/one_batch_size), step=epoch)
+            # DONOTCHANGE (You can decide how often you want to save the model)
+            nsml.save(epoch)
+
+    # 로컬 테스트 모드일때 사용합니다
+    # 결과가 아래와 같이 나온다면, nsml submit을 통해서 제출할 수 있습니다.
+    # [(0.3, 0), (0.7, 1), ... ]
+    elif config.mode == 'test_local':
+        with open(os.path.join(DATASET_PATH, 'train/train_data'), 'rt', encoding='utf-8') as f:
+            queries = f.readlines()
+        res = []
+        for batch in _batch_loader(queries, config.batch):
+            temp_res = nsml.infer(batch)
+            res += temp_res
+        print(res)