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Guanheng Zhang
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update docs
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‎beginner_source/text_sentiment_ngrams_tutorial.py

Lines changed: 51 additions & 54 deletions
Original file line numberDiff line numberDiff line change
@@ -203,11 +203,8 @@ def train(model, dataloader):
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total_count += label.size(0)
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if idx % log_interval == 0 and idx > 0:
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elapsed = time.time() - start_time
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print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:05.5f} | '
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'ms/batch {:5.2f} '
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print('| epoch {:3d} | {:5d}/{:5d} batches '
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'| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
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scheduler.get_last_lr()[0],
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elapsed * 1000 / log_interval,
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total_acc/total_count))
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total_acc, total_count = 0, 0
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start_time = time.time()
@@ -292,66 +289,66 @@ def evaluate(model, dataloader):
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#
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# ::
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#
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# | epoch 1 | 500/ 1782 batches | lr 5.00000 | ms/batch 4.75 | accuracy 0.684
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# | epoch 1 | 1000/ 1782 batches | lr 5.00000 | ms/batch 4.42 | accuracy 0.852
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# | epoch 1 | 1500/ 1782 batches | lr 5.00000 | ms/batch 4.43 | accuracy 0.877
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# -----------------------------------------------------------------------------------------
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# | epoch 1 | 500/ 1782 batches | accuracy 0.684
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# | epoch 1 | 1000/ 1782 batches | accuracy 0.852
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# | epoch 1 | 1500/ 1782 batches | accuracy 0.877
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# -----------------------------------------------------------
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# | end of epoch 1 | time: 8.33s | valid accuracy 0.867
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# -----------------------------------------------------------------------------------------
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# | epoch 2 | 500/ 1782 batches | lr 5.00000 | ms/batch 4.45 | accuracy 0.895
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# | epoch 2 | 1000/ 1782 batches | lr 5.00000 | ms/batch 4.43 | accuracy 0.900
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# | epoch 2 | 1500/ 1782 batches | lr 5.00000 | ms/batch 4.43 | accuracy 0.903
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 2 | 500/ 1782 batches | accuracy 0.895
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# | epoch 2 | 1000/ 1782 batches | accuracy 0.900
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# | epoch 2 | 1500/ 1782 batches | accuracy 0.903
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# -----------------------------------------------------------
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# | end of epoch 2 | time: 8.18s | valid accuracy 0.890
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# -----------------------------------------------------------------------------------------
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# | epoch 3 | 500/ 1782 batches | lr 5.00000 | ms/batch 4.46 | accuracy 0.914
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# | epoch 3 | 1000/ 1782 batches | lr 5.00000 | ms/batch 4.44 | accuracy 0.914
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# | epoch 3 | 1500/ 1782 batches | lr 5.00000 | ms/batch 4.43 | accuracy 0.916
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 3 | 500/ 1782 batches | accuracy 0.914
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# | epoch 3 | 1000/ 1782 batches | accuracy 0.914
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# | epoch 3 | 1500/ 1782 batches | accuracy 0.916
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# -----------------------------------------------------------
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# | end of epoch 3 | time: 8.20s | valid accuracy 0.897
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# -----------------------------------------------------------------------------------------
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# | epoch 4 | 500/ 1782 batches | lr 5.00000 | ms/batch 4.44 | accuracy 0.926
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# | epoch 4 | 1000/ 1782 batches | lr 5.00000 | ms/batch 4.44 | accuracy 0.924
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# | epoch 4 | 1500/ 1782 batches | lr 5.00000 | ms/batch 4.43 | accuracy 0.921
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 4 | 500/ 1782 batches | accuracy 0.926
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# | epoch 4 | 1000/ 1782 batches | accuracy 0.924
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# | epoch 4 | 1500/ 1782 batches | accuracy 0.921
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# -----------------------------------------------------------
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# | end of epoch 4 | time: 8.18s | valid accuracy 0.895
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# -----------------------------------------------------------------------------------------
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# | epoch 5 | 500/ 1782 batches | lr 0.50000 | ms/batch 4.44 | accuracy 0.938
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# | epoch 5 | 1000/ 1782 batches | lr 0.50000 | ms/batch 4.42 | accuracy 0.935
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# | epoch 5 | 1500/ 1782 batches | lr 0.50000 | ms/batch 4.41 | accuracy 0.937
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 5 | 500/ 1782 batches | accuracy 0.938
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# | epoch 5 | 1000/ 1782 batches | accuracy 0.935
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# | epoch 5 | 1500/ 1782 batches | accuracy 0.937
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# -----------------------------------------------------------
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# | end of epoch 5 | time: 8.16s | valid accuracy 0.902
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# -----------------------------------------------------------------------------------------
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# | epoch 6 | 500/ 1782 batches | lr 0.50000 | ms/batch 4.43 | accuracy 0.939
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# | epoch 6 | 1000/ 1782 batches | lr 0.50000 | ms/batch 4.41 | accuracy 0.939
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# | epoch 6 | 1500/ 1782 batches | lr 0.50000 | ms/batch 4.41 | accuracy 0.938
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 6 | 500/ 1782 batches | accuracy 0.939
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# | epoch 6 | 1000/ 1782 batches | accuracy 0.939
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# | epoch 6 | 1500/ 1782 batches | accuracy 0.938
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# -----------------------------------------------------------
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# | end of epoch 6 | time: 8.16s | valid accuracy 0.906
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# -----------------------------------------------------------------------------------------
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# | epoch 7 | 500/ 1782 batches | lr 0.50000 | ms/batch 4.44 | accuracy 0.941
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# | epoch 7 | 1000/ 1782 batches | lr 0.50000 | ms/batch 4.42 | accuracy 0.939
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# | epoch 7 | 1500/ 1782 batches | lr 0.50000 | ms/batch 4.44 | accuracy 0.939
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 7 | 500/ 1782 batches | accuracy 0.941
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# | epoch 7 | 1000/ 1782 batches | accuracy 0.939
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# | epoch 7 | 1500/ 1782 batches | accuracy 0.939
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# -----------------------------------------------------------
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# | end of epoch 7 | time: 8.19s | valid accuracy 0.903
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# -----------------------------------------------------------------------------------------
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# | epoch 8 | 500/ 1782 batches | lr 0.05000 | ms/batch 4.44 | accuracy 0.942
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# | epoch 8 | 1000/ 1782 batches | lr 0.05000 | ms/batch 4.41 | accuracy 0.941
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# | epoch 8 | 1500/ 1782 batches | lr 0.05000 | ms/batch 4.42 | accuracy 0.942
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 8 | 500/ 1782 batches | accuracy 0.942
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# | epoch 8 | 1000/ 1782 batches | accuracy 0.941
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# | epoch 8 | 1500/ 1782 batches | accuracy 0.942
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# -----------------------------------------------------------
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# | end of epoch 8 | time: 8.16s | valid accuracy 0.904
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# -----------------------------------------------------------------------------------------
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# | epoch 9 | 500/ 1782 batches | lr 0.00500 | ms/batch 4.44 | accuracy 0.942
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# | epoch 9 | 1000/ 1782 batches | lr 0.00500 | ms/batch 4.42 | accuracy 0.941
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# | epoch 9 | 1500/ 1782 batches | lr 0.00500 | ms/batch 4.42 | accuracy 0.942
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 9 | 500/ 1782 batches | accuracy 0.942
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# | epoch 9 | 1000/ 1782 batches | accuracy 0.941
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# | epoch 9 | 1500/ 1782 batches | accuracy 0.942
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# -----------------------------------------------------------
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#| end of epoch 9 | time: 8.16s | valid accuracy 0.904
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# -----------------------------------------------------------------------------------------
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# | epoch 10 | 500/ 1782 batches | lr 0.00050 | ms/batch 4.43 | accuracy 0.940
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# | epoch 10 | 1000/ 1782 batches | lr 0.00050 | ms/batch 4.41 | accuracy 0.942
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# | epoch 10 | 1500/ 1782 batches | lr 0.00050 | ms/batch 4.41 | accuracy 0.942
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#i -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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# | epoch 10 | 500/ 1782 batches | accuracy 0.940
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# | epoch 10 | 1000/ 1782 batches | accuracy 0.942
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# | epoch 10 | 1500/ 1782 batches | accuracy 0.942
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#i -----------------------------------------------------------
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# | end of epoch 10 | time: 8.15s | valid accuracy 0.904
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# -----------------------------------------------------------------------------------------
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# -----------------------------------------------------------
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