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train.log
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train.log
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--------------------------------------
some config:
data_dir = ./data
output_dir = ./output
embedding_path = ./embedding/hlbl-embeddings-scaled.EMBEDDING_SIZE=50.txt
word_dim = 50
model_name = CNN
mode = 1
seed = 5782
cuda = 0
epoch = 20
dropout = 0.5
batch_size = 128
lr = 0.001
max_len = 100
pos_dis = 50
pos_dim = 5
hidden_size = 100
filter_num = 200
window = 3
L2_decay = 1e-05
device = cuda:0
model_dir = ./output/CNN
--------------------------------------
start to load data ...
finish!
--------------------------------------
CNN(
(word_embedding): Embedding(246123, 50)
(pos1_embedding): Embedding(103, 5)
(pos2_embedding): Embedding(103, 5)
(conv): Conv2d(1, 200, kernel_size=(3, 60), stride=(1, 1), padding=(1, 0))
(maxpool): MaxPool2d(kernel_size=(100, 1), stride=(100, 1), padding=0, dilation=1, ceil_mode=False)
(tanh): Tanh()
(dropout): Dropout(p=0.5, inplace=False)
(linear): Linear(in_features=200, out_features=100, bias=True)
(dense): Linear(in_features=100, out_features=19, bias=True)
)
traning model parameters:
word_embedding.weight : torch.Size([246123, 50])
pos1_embedding.weight : torch.Size([103, 5])
pos2_embedding.weight : torch.Size([103, 5])
conv.weight : torch.Size([200, 1, 3, 60])
conv.bias : torch.Size([200])
linear.weight : torch.Size([100, 200])
linear.bias : torch.Size([100])
dense.weight : torch.Size([19, 100])
dense.bias : torch.Size([19])
--------------------------------------
start to train the model ...
[001] train_loss: 2.446 | dev_loss: 2.449 | micro f1 on dev: 0.5220 >>> save models!
[002] train_loss: 1.856 | dev_loss: 1.850 | micro f1 on dev: 0.4652
[003] train_loss: 1.403 | dev_loss: 1.426 | micro f1 on dev: 0.5612 >>> save models!
[004] train_loss: 1.186 | dev_loss: 1.265 | micro f1 on dev: 0.6535 >>> save models!
[005] train_loss: 0.976 | dev_loss: 1.131 | micro f1 on dev: 0.6994 >>> save models!
[006] train_loss: 0.806 | dev_loss: 1.039 | micro f1 on dev: 0.7259 >>> save models!
[007] train_loss: 0.658 | dev_loss: 0.976 | micro f1 on dev: 0.7508 >>> save models!
[008] train_loss: 0.571 | dev_loss: 0.972 | micro f1 on dev: 0.7557 >>> save models!
[009] train_loss: 0.457 | dev_loss: 0.927 | micro f1 on dev: 0.7690 >>> save models!
[010] train_loss: 0.364 | dev_loss: 0.905 | micro f1 on dev: 0.7666
[011] train_loss: 0.289 | dev_loss: 0.896 | micro f1 on dev: 0.7796 >>> save models!
[012] train_loss: 0.234 | dev_loss: 0.912 | micro f1 on dev: 0.7846 >>> save models!
[013] train_loss: 0.189 | dev_loss: 0.916 | micro f1 on dev: 0.7926 >>> save models!
[014] train_loss: 0.151 | dev_loss: 0.924 | micro f1 on dev: 0.7857
[015] train_loss: 0.123 | dev_loss: 0.959 | micro f1 on dev: 0.7862
[016] train_loss: 0.097 | dev_loss: 0.980 | micro f1 on dev: 0.7779
[017] train_loss: 0.078 | dev_loss: 0.985 | micro f1 on dev: 0.7906
[018] train_loss: 0.064 | dev_loss: 1.025 | micro f1 on dev: 0.7840
[019] train_loss: 0.051 | dev_loss: 1.025 | micro f1 on dev: 0.7831
[020] train_loss: 0.042 | dev_loss: 1.066 | micro f1 on dev: 0.7876
--------------------------------------
start test ...
test_loss: 0.916 | micro f1 on test: 0.7926