update lstm

LSTM_age_gender.py

@@ -83,13 +83,17 @@ def get_embedding(feature_name, tokenizer):
         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)
+        # embedding_matrix = np.random.randn(
+        # len(feature_tokens)+1, embedding_dim, dtype=float)
+        embedding_matrix = np.zeros(
+            (len(feature_tokens)+1, embedding_dim), dtype=float)
         # 得到索引
         for word, i in tokenizer.word_index.items():
             embedding_vector = wv[word]
             if embedding_vector is not None:
                 embedding_matrix[i] = embedding_vector
+            else:
+                print(str(word)+' 没有找到')
         return embedding_matrix
 
     # 从序列文件提取array格式数据
@@ -303,7 +307,8 @@ def get_tail_concat_model(DATA, predict_age=True, predict_gender=False):
         output_y)
 
     model.compile(loss='categorical_crossentropy',
-                  optimizer='adam', metrics=['accuracy'])
+                  optimizer=optimizers.Adam(1e-3),
+                  metrics=['accuracy'])
 
     model.summary()
     return model

word2vec_creative_id.py

@@ -11,31 +11,30 @@
 import pickle
 from mymail import mail
 # %%
-df_train = pd.read_csv(
+df = pd.read_csv(
     'data/click_log_ad.csv')
 # df_test = pd.read_csv('data/test/clicklog_ad_user_test.csv')
-columns = ['user_id', 'creative_id', 'time']
-frame = [df_train[columns], df_test[columns]]
-df_train_test = pd.concat(frame, ignore_index=True)
-df_train_test_sorted = df_train_test.sort_values(
-    ["user_id", "time"], ascending=(True, True))
+columns = ['user_id', 'creative_id']
+# frame = [df_train[columns], df_test[columns]]
+# df_train_test = pd.concat(frame, ignore_index=True)
+# df_train_test_sorted = df_train_test.sort_values(
+#     ["user_id", "time"], ascending=(True, True))
 # %%
 with open('word2vec/df_train_test_sorted.pkl', 'wb') as f:
     pickle.dump(df_train_test_sorted, f)
-# %%
 with open('word2vec/df_train_test_sorted.pkl', 'rb') as f:
     df_train_test_sorted = pickle.load(f)
 # %%
-userid_creative_ids = df_train_test_sorted.groupby(
-    'user_id')['creative_id'].apply(list).reset_index(name='creative_ids')
+userid_creative_id = df.groupby(
+    'user_id')['creative_id'].apply(list).reset_index(name='creative_id')
 # %%
-with open('word2vec/userid_creative_ids.txt', 'w')as f:
-    for ids in userid_creative_ids.creative_ids:
+with open('word2vec_new/creative_id.txt', 'w')as f:
+    for ids in userid_creative_id.creative_ids:
         ids = [str(e) for e in ids]
         line = ' '.join(ids)
         f.write(line+'\n')
 # %%
-sentences = LineSentence('word2vec/userid_creative_ids.txt')
+sentences = LineSentence('word2vec_new/creative_id.txt')
 dimension_embedding = 128
 model = Word2Vec(sentences, size=dimension_embedding,
                  window=10, min_count=1, workers=-1, iter=10, sg=1)