config

Author: dalinvip

Config/config.cfg

@@ -1,7 +1,7 @@
 [Data]
 word_Embedding = False
 freq_1_unk = False
-word_Embedding_Path = ./word2vec/converted_word_Twitter.txt
+word_Embedding_Path = 
 datafile_path = 
 name_trainfile = ./Data/raw.clean.train
 name_devfile = ./Data/raw.clean.dev
@@ -24,12 +24,12 @@ rm_model = True
 [Model]
 static = False
 wide_conv = True
-CNN = False
+CNN = True
 HighWay_CNN = False
 CNN_MUI = False
 DEEP_CNN = False
 DEEP_CNN_MUI = False
-LSTM = True
+LSTM = False
 GRU = False
 BiLSTM = False
 BiLSTM_1 = False
@@ -50,10 +50,10 @@ batch_norm_affine = False
 dropout = 0.4
 dropout_embed = 0.5
 max_norm = None
-clip_max_norm = 3
+clip_max_norm = 10
 kernel_num = 200
-kernel_sizes = 1,2
-init_weight = False
+kernel_sizes = 3,4,5
+init_weight = True
 init_weight_value = 6.0
 
 [Optimizer]
@@ -67,10 +67,10 @@ weight_decay = 1e-8
 [Train]
 num_threads = 1
 device = -1
-no_cuda = False
+cuda = False
 epochs = 500
 batch_size = 16
-log_interval = 1
+log_interval = 10
 test_interval = 100
 save_interval = 100
 

Config/config.py

@@ -294,8 +294,8 @@ def device(self):
         return self._config.getint("Train", "device")
 
     @property
-    def no_cuda(self):
-        return self._config.getboolean("Train", "no_cuda")
+    def cuda(self):
+        return self._config.getboolean("Train", "cuda")
 
     @property
     def epochs(self):

main.py

@@ -243,7 +243,6 @@ def update_arguments():
     if config.CNN_MUI is True or config.DEEP_CNN_MUI is True:
         config.embed_num_mui = len(config.static_text_field.vocab)
     # config.kernel_sizes = [int(k) for k in config.kernel_sizes.split(',')]
-    config.cuda = config.no_cuda
     print(config.kernel_sizes)
     mulu = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
     config.mulu = mulu
@@ -452,7 +451,7 @@ def main():
     config = parser.parse_args()
 
     config = configurable.Configurable(config_file=config.config_file)
-    if config.no_cuda is True:
+    if config.cuda is True:
         print("Using GPU To Train......")
         # torch.backends.cudnn.enabled = True
         # torch.backends.cudnn.deterministic = True