This example demonstrates the use of Conv1D for CNN text classification. Original paper could be found at: https://arxiv.org/abs/1408.5882
This is the baseline model: CNN-rand, on MR dataset.
The model is implemented in two frameworks:
- cnn_mxnet.py: MXNET/Gluon API
- cnn_pytorch: PyTorch
We didn't implement cross validation, but simply run python mr_cnn.py for multiple times, the average accuracy is close to 76%.
Loading data...
Training: 9595, Testing: 1067, Vocabulary: 8000
Configuring CNN model...
Initializing weights on gpu(0)
TextCNN(
(embedding): Embedding(8000 -> 128, float32)
(conv1): Conv_Max_Pooling(
(conv): Conv1D(None -> 100, kernel_size=(3,), stride=(1,))
(pooling): GlobalMaxPool1D(size=(1,), stride=(1,), padding=(0,), ceil_mode=True)
)
(conv2): Conv_Max_Pooling(
(conv): Conv1D(None -> 100, kernel_size=(4,), stride=(1,))
(pooling): GlobalMaxPool1D(size=(1,), stride=(1,), padding=(0,), ceil_mode=True)
)
(conv3): Conv_Max_Pooling(
(conv): Conv1D(None -> 100, kernel_size=(5,), stride=(1,))
(pooling): GlobalMaxPool1D(size=(1,), stride=(1,), padding=(0,), ceil_mode=True)
)
(dropout): Dropout(p = 0.5)
(fc1): Dense(None -> 2, linear)
)
Training and evaluating...
Epoch 1, Train_loss: 0.42, Train_acc 83.12%, Test_loss: 0.53, Test_acc 72.73%, Time: 0:00:29 *
Epoch 2, Train_loss: 0.22, Train_acc 93.63%, Test_loss: 0.48, Test_acc 77.23%, Time: 0:00:56 *
Epoch 3, Train_loss: 0.11, Train_acc 96.73%, Test_loss: 0.59, Test_acc 77.13%, Time: 0:01:23
Epoch 4, Train_loss: 0.048, Train_acc 98.90%, Test_loss: 0.75, Test_acc 76.10%, Time: 0:01:51
Epoch 5, Train_loss: 0.021, Train_acc 99.66%, Test_loss: 0.91, Test_acc 75.45%, Time: 0:02:18
Epoch 6, Train_loss: 0.011, Train_acc 99.87%, Test_loss: 1.1, Test_acc 76.01%, Time: 0:02:46
Epoch 7, Train_loss: 0.006, Train_acc 99.95%, Test_loss: 1.2, Test_acc 76.10%, Time: 0:03:13
Epoch 8, Train_loss: 0.0036, Train_acc 99.97%, Test_loss: 1.3, Test_acc 76.29%, Time: 0:03:41
Epoch 9, Train_loss: 0.0024, Train_acc 99.98%, Test_loss: 1.4, Test_acc 76.38%, Time: 0:04:08
Epoch 10, Train_loss: 0.0019, Train_acc 99.98%, Test_loss: 1.5, Test_acc 76.38%, Time: 0:04:36
Testing...
Test accuracy: 77.23%, F1-Score: 77.21%
Precision, Recall and F1-Score...
precision recall f1-score support
POS 0.78 0.76 0.77 525
NEG 0.77 0.79 0.78 542
avg / total 0.77 0.77 0.77 1067
Confusion Matrix...
[[397 128]
[115 427]]
Time usage: 0:00:01
POS
NEGLoading data...
Training: 9595, Testing: 1067, Vocabulary: 8000
Configuring CNN model...
TextCNN(
(embedding): Embedding(8000, 128)
(convs): ModuleList(
(0): Conv1d (128, 100, kernel_size=(3,), stride=(1,))
(1): Conv1d (128, 100, kernel_size=(4,), stride=(1,))
(2): Conv1d (128, 100, kernel_size=(5,), stride=(1,))
)
(dropout): Dropout(p=0.5)
(fc1): Linear(in_features=300, out_features=2)
)
Training and evaluating...
Epoch 1, Train_loss: 0.57, Train_acc 76.20%, Test_loss: 0.63, Test_acc 65.42%, Time: 0:00:09 *
Epoch 2, Train_loss: 0.46, Train_acc 83.75%, Test_loss: 0.6, Test_acc 67.01%, Time: 0:00:15 *
Epoch 3, Train_loss: 0.35, Train_acc 87.68%, Test_loss: 0.58, Test_acc 69.35%, Time: 0:00:21 *
Epoch 4, Train_loss: 0.28, Train_acc 89.46%, Test_loss: 0.61, Test_acc 69.54%, Time: 0:00:27 *
Epoch 5, Train_loss: 0.23, Train_acc 90.62%, Test_loss: 0.66, Test_acc 70.29%, Time: 0:00:33 *
Epoch 6, Train_loss: 0.13, Train_acc 96.47%, Test_loss: 0.63, Test_acc 73.01%, Time: 0:00:39 *
Epoch 7, Train_loss: 0.069, Train_acc 98.83%, Test_loss: 0.63, Test_acc 73.48%, Time: 0:00:45 *
Epoch 8, Train_loss: 0.05, Train_acc 99.33%, Test_loss: 0.69, Test_acc 73.66%, Time: 0:00:51 *
Epoch 9, Train_loss: 0.031, Train_acc 99.74%, Test_loss: 0.71, Test_acc 74.51%, Time: 0:00:56 *
Epoch 10, Train_loss: 0.027, Train_acc 99.66%, Test_loss: 0.79, Test_acc 74.13%, Time: 0:01:02
Epoch 11, Train_loss: 0.019, Train_acc 99.78%, Test_loss: 0.82, Test_acc 74.60%, Time: 0:01:08 *
Epoch 12, Train_loss: 0.014, Train_acc 99.91%, Test_loss: 0.85, Test_acc 73.66%, Time: 0:01:14
Epoch 13, Train_loss: 0.013, Train_acc 99.90%, Test_loss: 0.94, Test_acc 74.70%, Time: 0:01:20 *
Epoch 14, Train_loss: 0.013, Train_acc 99.86%, Test_loss: 1.0, Test_acc 74.04%, Time: 0:01:26
Epoch 15, Train_loss: 0.0089, Train_acc 99.94%, Test_loss: 1.0, Test_acc 74.98%, Time: 0:01:32 *
Epoch 16, Train_loss: 0.0066, Train_acc 99.98%, Test_loss: 1.0, Test_acc 73.95%, Time: 0:01:38
Epoch 17, Train_loss: 0.0057, Train_acc 99.99%, Test_loss: 1.1, Test_acc 75.45%, Time: 0:01:44 *
Epoch 18, Train_loss: 0.0051, Train_acc 100.00%, Test_loss: 1.1, Test_acc 75.16%, Time: 0:01:50
Epoch 19, Train_loss: 0.0053, Train_acc 99.97%, Test_loss: 1.2, Test_acc 75.45%, Time: 0:01:56
Epoch 20, Train_loss: 0.005, Train_acc 99.95%, Test_loss: 1.3, Test_acc 75.45%, Time: 0:02:02
Testing...
Test accuracy: 75.45%, F1-Score: 75.37%
Precision, Recall and F1-Score...
precision recall f1-score support
POS 0.77 0.76 0.77 568
NEG 0.73 0.75 0.74 499
avg / total 0.75 0.75 0.75 1067
Confusion Matrix...
[[432 136]
[126 373]]
Time usage: 0:00:00
POS
NEGThe result of MXNET is better than the PyTorch version, and it converges faster.
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