update LSTM

LSTM_age_multi_input.py

@@ -18,6 +18,7 @@
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
 # %%
+# 统计creative_id序列的长度，只需要统计一次
 # f = open('word2vec/userid_creative_ids.txt')
 # LEN_creative_id = -1
 # for line in f:
@@ -27,10 +28,10 @@
 # %%
 parser = argparse.ArgumentParser()
 parser.add_argument('--load_from_npy', action='store_true',
-                    help='从npy文件加载数据',
+                    help='从npy文件加载训练数据，不用每次训练都重新生成array文件',
                     default=False)
 parser.add_argument('--not_train_embedding', action='store_false',
-                    help='从npy文件加载数据',
+                    help='不训练embedding文件，一般来说加上这个参数效果不太好',
                     default=True)
 
 parser.add_argument('--epoch', type=int,
@@ -40,12 +41,12 @@
                     help='batch size大小',
                     default=256)
 parser.add_argument('--examples', type=int,
-                    help='训练数据，默认为训练集，不包含验证集',
+                    help='训练数据，默认为训练集，不包含验证集，调试时候可以设置1000',
                     default=810000)
 
 
 parser.add_argument('--num_lstm', type=int,
-                    help='LSTM head个数',
+                    help='LSTM层数个数，目前结果3层比5层好用，1层还在做实验中...',
                     default=1)
 
 args = parser.parse_args()
@@ -57,6 +58,22 @@
 
 def get_train_val():
 
+    # 提取词向量文件
+    def get_embedding(feature_name):
+        path = f"word2vec/wordvectors_{feature_name}.kv"
+        wv = KeyedVectors.load(path, mmap='r')
+        feature_tokens = list(wv.vocab.keys())
+        embedding_dim = 128
+        embedding_matrix = np.random.randn(
+            len(feature_tokens)+1, embedding_dim)
+        for feature in feature_tokens:
+            embedding_vector = wv[feature]
+            if embedding_vector is not None:
+                index = tokenizer.texts_to_sequences([feature])[0][0]
+                embedding_matrix[index] = embedding_vector
+        return embedding_matrix
+
+    # 第一个输入
     # 获取 creative_id 特征
     # f = open('tmp/userid_creative_ids.txt')
     f = open('word2vec/userid_creative_ids.txt')
@@ -72,23 +89,9 @@ def get_train_val():
     X1_train = pad_sequences(
         sequences, maxlen=LEN_creative_id, padding='post')
 
-    # 获取creative_id embedding
-    def get_creative_id_emb():
-        path = "word2vec/wordvectors_creative_id.kv"
-        wv = KeyedVectors.load(path, mmap='r')
-        creative_id_tokens = list(wv.vocab.keys())
-        embedding_dim = 128
-        embedding_matrix = np.random.randn(
-            len(creative_id_tokens)+1, embedding_dim)
-        for creative_id in creative_id_tokens:
-            embedding_vector = wv[creative_id]
-            if embedding_vector is not None:
-                index = tokenizer.texts_to_sequences([creative_id])[0][0]
-                embedding_matrix[index] = embedding_vector
-        return embedding_matrix
-
-    creative_id_emb = get_creative_id_emb()
+    creative_id_emb = get_embedding(feature_name='creative_id')
 
+    # 第二个输入
     # 获取 ad_id 特征
     f = open('word2vec/userid_ad_ids.txt')
     tokenizer = Tokenizer(num_words=NUM_ad_id)
@@ -103,22 +106,9 @@ def get_creative_id_emb():
     X2_train = pad_sequences(
         sequences, maxlen=LEN_ad_id, padding='post')
 
-    def get_ad_id_emb():
-        path = "word2vec/wordvectors_ad_id.kv"
-        wv = KeyedVectors.load(path, mmap='r')
-        ad_id_tokens = list(wv.vocab.keys())
-        embedding_dim = 128
-        embedding_matrix = np.random.randn(
-            len(ad_id_tokens)+1, embedding_dim)
-        for ad_id in ad_id_tokens:
-            embedding_vector = wv[ad_id]
-            if embedding_vector is not None:
-                index = tokenizer.texts_to_sequences([ad_id])[0][0]
-                embedding_matrix[index] = embedding_vector
-        return embedding_matrix
-
-    ad_id_emb = get_ad_id_emb()
+    ad_id_emb = get_embedding(feature_name='ad_id')
 
+    # 第三个输入
     # 获取 product_id 特征
     # f = open('tmp/userid_product_ids.txt')
     f = open('word2vec/userid_product_ids.txt')
@@ -134,24 +124,10 @@ def get_ad_id_emb():
     X3_train = pad_sequences(
         sequences, maxlen=LEN_product_id, padding='post')
 
-    # 获取product_id embedding
-    def get_product_id_emb():
-        path = "word2vec/wordvectors_product_id.kv"
-        wv = KeyedVectors.load(path, mmap='r')
-        product_id_tokens = list(wv.vocab.keys())
-        embedding_dim = 128
-        embedding_matrix = np.random.randn(
-            len(product_id_tokens)+1, embedding_dim)
-        for product_id in product_id_tokens:
-            embedding_vector = wv[product_id]
-            if embedding_vector is not None:
-                index = tokenizer.texts_to_sequences([product_id])[0][0]
-                embedding_matrix[index] = embedding_vector
-        return embedding_matrix
-
-    product_id_emb = get_product_id_emb()
+    product_id_emb = get_embedding(feature_name='product_id')
 
-    # 获得age标签
+    # 构造输出的训练标签
+    # 获得age、gender标签
     user_train = pd.read_csv(
         'data/train_preliminary/user.csv').sort_values(['user_id'], ascending=(True,))
     Y_gender = user_train['gender'].values
@@ -268,15 +244,6 @@ def save_data(datas):
 # %%
 checkpoint = ModelCheckpoint("tmp/age_epoch_{epoch:02d}.hdf5", monitor='val_loss', verbose=1,
                              save_best_only=False, mode='auto', period=1)
-# %%
-# model.fit(
-#     {'creative_id': x1_train, 'ad_id': x2_train},
-#     y_train,
-#     validation_data=([x1_val, x2_val], y_val),
-#     epochs=5,
-#     batch_size=256,
-#     callbacks=[checkpoint],
-# )
 
 # %%
 try:
@@ -298,6 +265,7 @@ def save_data(datas):
 
 
 # %%
+# 后续为预测过程，暂时注释掉不使用但是不要删除
 # model.load_weights('tmp\gender_epoch_01.hdf5')