weekly report

@@ -4,7 +4,7 @@ import pandas as pd
 
 
 # Load files.
 # Load files.
 nltk.download('stopwords')
 nltk.download('stopwords')
-vocab = pd.read_csv('../vocabulary.csv')
+vocab = pd.read_csv('E:/khuhub/2015104192/web/backend/yt8m/esot3ria/vocabulary.csv')
 
 
 # Lower corpus and Remove () from name.
 # Lower corpus and Remove () from name.
 vocab['WikiDescription'] = vocab['WikiDescription'].str.lower().str.replace('[^a-zA-Z0-9]', ' ')
 vocab['WikiDescription'] = vocab['WikiDescription'].str.lower().str.replace('[^a-zA-Z0-9]', ' ')
@@ -36,7 +36,7 @@ vocab_phrased = phraser[tokenlist]
 
 
 # Vectorize tags.
 # Vectorize tags.
 w2v = gensim.models.word2vec.Word2Vec(sentences=tokenlist, min_count=1)
 w2v = gensim.models.word2vec.Word2Vec(sentences=tokenlist, min_count=1)
-w2v.save('tag_vectors.model')
+w2v.save('E:/khuhub/2015104192/web/backend/yt8m/esot3ria/tag_vectors2.model')
 
 
 # word_vectors = w2v.wv
 # word_vectors = w2v.wv
 # vocabs = word_vectors.vocab.keys()
 # vocabs = word_vectors.vocab.keys()

@@ -176,7 +176,7 @@ class DbofModel(models.BaseModel):
     return aggregated_model().create_model(model_input=activation,
     return aggregated_model().create_model(model_input=activation,
                                            vocab_size=vocab_size,
                                            vocab_size=vocab_size,
                                            **unused_params)
                                            **unused_params)
-
+    
 
 
 class LstmModel(models.BaseModel):
 class LstmModel(models.BaseModel):
   """Creates a model which uses a stack of LSTMs to represent the video."""
   """Creates a model which uses a stack of LSTMs to represent the video."""
@@ -262,7 +262,9 @@ class GruModel(models.BaseModel):
         vocab_size=vocab_size,
         vocab_size=vocab_size,
         is_training=is_training,
         is_training=is_training,
         **unused_params)
         **unused_params)
-    
+
+
+
 class FrameLevelLogisticModel(models.BaseModel):
 class FrameLevelLogisticModel(models.BaseModel):
   """Creates a logistic classifier over the aggregated frame-level features."""
   """Creates a logistic classifier over the aggregated frame-level features."""
   def create_model(self, model_input, vocab_size, num_frames, **unused_params):
   def create_model(self, model_input, vocab_size, num_frames, **unused_params):

@@ -15,7 +15,7 @@ module.exports = {
   rules: {
   rules: {
     'no-console': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
     'no-console': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
     'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
     'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
-    'linebreak-style': ['error', 'unix'],
+    'linebreak-style': 0,
     'max-len': ['error', { code: 200 }],
     'max-len': ['error', { code: 200 }],
   },
   },
 };
 };

@@ -11752,9 +11752,9 @@
       "dev": true
       "dev": true
     },
     },
     "vue": {
     "vue": {
-      "version": "2.6.11",
-      "resolved": "https://registry.npmjs.org/vue/-/vue-2.6.11.tgz",
-      "integrity": "sha512-VfPwgcGABbGAue9+sfrD4PuwFar7gPb1yl1UK1MwXoQPAw0BKSqWfoYCT/ThFrdEVWoI51dBuyCoiNU9bZDZxQ=="
+      "version": "2.6.12",
+      "resolved": "https://registry.npmjs.org/vue/-/vue-2.6.12.tgz",
+      "integrity": "sha512-uhmLFETqPPNyuLLbsKz6ioJ4q7AZHzD8ZVFNATNyICSZouqP2Sz0rotWQC8UNBF6VGSCs5abnKJoStA6JbCbfg=="
     },
     },
     "vue-cli-plugin-vuetify": {
     "vue-cli-plugin-vuetify": {
       "version": "2.0.5",
       "version": "2.0.5",

@@ -13,7 +13,7 @@
     "core-js": "^3.6.5",
     "core-js": "^3.6.5",
     "moment": "^2.24.0",
     "moment": "^2.24.0",
     "roboto-fontface": "*",
     "roboto-fontface": "*",
-    "vue": "^2.6.11",
+    "vue": "^2.6.12",
     "vue-router": "^3.1.6",
     "vue-router": "^3.1.6",
     "vuetify": "^2.2.11",
     "vuetify": "^2.2.11",
     "vuex": "^3.1.3"
     "vuex": "^3.1.3"