added 18.dilated-cnn-seq2seq

README.md

@@ -49,6 +49,7 @@
  15. GRU Seq2seq VAE
  16. Attention-is-all-you-Need
  17. CNN-Seq2seq
+ 18. Dilated-CNN-Seq2seq
 
 **Bonus**
 
@@ -217,74 +218,78 @@ I will cut the dataset to train and test datasets,
 
 So we will let the model do forecasting based on last 30 days, and we will going to repeat the experiment for 10 times. You can increase it locally if you want, and tuning parameters will help you by a lot.
 
-1. LSTM, 95.693%
+1. LSTM, accuracy 95.693%, time taken for 1 epoch 01:09
 
 <img src="output/lstm.png" width="70%" align="">
 
-2. LSTM Bidirectional, 93.8%
+2. LSTM Bidirectional, accuracy 93.8%, time taken for 1 epoch 01:40
 
 <img src="output/bidirectional-lstm.png" width="70%" align="">
 
-3. LSTM 2-Path, 94.63%
+3. LSTM 2-Path, accuracy 94.63%, time taken for 1 epoch 01:39
 
 <img src="output/lstm-2path.png" width="70%" align="">
 
-4. GRU, 94.63%
+4. GRU, accuracy 94.63%, time taken for 1 epoch 02:10
 
 <img src="output/gru.png" width="70%" align="">
 
-5. GRU Bidirectional, 92.5673%
+5. GRU Bidirectional, accuracy 92.5673%, time taken for 1 epoch 01:40
 
 <img src="output/bidirectional-gru.png" width="70%" align="">
 
-6. GRU 2-Path, 93.2117%
+6. GRU 2-Path, accuracy 93.2117%, time taken for 1 epoch 01:39
 
 <img src="output/gru-2path.png" width="70%" align="">
 
-7. Vanilla, 91.4686%
+7. Vanilla, accuracy 91.4686%, time taken for 1 epoch 00:52
 
 <img src="output/vanilla.png" width="70%" align="">
 
-8. Vanilla Bidirectional, 88.9927%
+8. Vanilla Bidirectional, accuracy 88.9927%, time taken for 1 epoch 01:06
 
 <img src="output/bidirectional-vanilla.png" width="70%" align="">
 
-9. Vanilla 2-Path, 91.5406%
+9. Vanilla 2-Path, accuracy 91.5406%, time taken for 1 epoch 01:08
 
 <img src="output/vanilla-2path.png" width="70%" align="">
 
-10. LSTM Seq2seq, 94.9817%
+10. LSTM Seq2seq, accuracy 94.9817%, time taken for 1 epoch 01:36
 
 <img src="output/lstm-seq2seq.png" width="70%" align="">
 
-11. LSTM Bidirectional Seq2seq, 94.517%
+11. LSTM Bidirectional Seq2seq, accuracy 94.517%, time taken for 1 epoch 02:30
 
 <img src="output/bidirectional-lstm-seq2seq.png" width="70%" align="">
 
-12. LSTM Seq2seq VAE, 95.4190%
+12. LSTM Seq2seq VAE, accuracy 95.4190%, time taken for 1 epoch 01:48
 
 <img src="output/lstm-seq2seq-vae.png" width="70%" align="">
 
-13. GRU Seq2seq, 90.8854%
+13. GRU Seq2seq, accuracy 90.8854%, time taken for 1 epoch 01:34
 
 <img src="output/gru-seq2seq.png" width="70%" align="">
 
-14. GRU Bidirectional Seq2seq, 67.9915%
+14. GRU Bidirectional Seq2seq, accuracy 67.9915%, time taken for 1 epoch 02:30
 
 <img src="output/bidirectional-gru-seq2seq.png" width="70%" align="">
 
-15. GRU Seq2seq VAE, 89.1321%
+15. GRU Seq2seq VAE, accuracy 89.1321%, time taken for 1 epoch 01:48
 
 <img src="output/gru-seq2seq-vae.png" width="70%" align="">
 
-16. Attention-is-all-you-Need, 94.2482%
+16. Attention-is-all-you-Need, accuracy 94.2482%, time taken for 1 epoch 01:41
 
 <img src="output/attention-is-all-you-need.png" width="70%" align="">
 
-17. CNN-Seq2seq, 90.74%
+17. CNN-Seq2seq, accuracy 90.74%, time taken for 1 epoch 00:43
 
 <img src="output/cnn-seq2seq.png" width="70%" align="">
 
+18. Dilated-CNN-Seq2seq, accuracy 95.86%, time taken for 1 epoch 00:14
+
+<img src="output/dilated-cnn-seq2seq.png" width="70%" align="">
+
 **Bonus**
 
 1. How to forecast,