tflearn Training Step每次 We will run it for 10 epochs (the network will see all data 10 times) with a batch size of 16. n_epoch=10, batch_size=16
2017-12-27 10:46
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Training
TFLearn provides a model wrapper 'DNN' that can automatically performs a neural network classifier tasks, such as training, prediction, save/restore, etc... We will run it for 10 epochs (the network will see all data 10 times) with a batch size of 16.# Define model model = tflearn.DNN(net) # Start training (apply gradient descent algorithm) model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
Output:
--------------------------------- Run id: MG9PV8 Log directory: /tmp/tflearn_logs/ --------------------------------- Training samples: 1309 Validation samples: 0 -- Training Step: 82 | total loss: 0.64003 | Adam | epoch: 001 | loss: 0.64003 - acc: 0.6620 -- iter: 1309/1309 -- Training Step: 164 | total loss: 0.61915 | Adam | epoch: 002 | loss: 0.61915 - acc: 0.6614 -- iter: 1309/1309 -- Training Step: 246 | total loss: 0.56067 | Adam | epoch: 003 | loss: 0.56067 - acc: 0.7171 -- iter: 1309/1309 -- Training Step: 328 | total loss: 0.51807 | Adam | epoch: 004 | loss: 0.51807 - acc: 0.7799 -- iter: 1309/1309 -- Training Step: 410 | total loss: 0.47475 | Adam | epoch: 005 | loss: 0.47475 - acc: 0.7962 -- iter: 1309/1309 -- Training Step: 492 | total loss: 0.51677 | Adam | epoch: 006 | loss: 0.51677 - acc: 0.7701 -- iter: 1309/1309 -- Training Step: 574 | total loss: 0.48988 | Adam | epoch: 007 | loss: 0.48988 - acc: 0.7891 -- iter: 1309/1309 -- Training Step: 656 | total loss: 0.55073 | Adam | epoch: 008 | loss: 0.55073 - acc: 0.7427 -- iter: 1309/1309 -- Training Step: 738 | total loss: 0.50242 | Adam | epoch: 009 | loss: 0.50242 - acc: 0.7854 -- iter: 1309/1309 -- Training Step: 820 | total loss: 0.41557 | Adam | epoch: 010 | loss: 0.41557 - acc: 0.8110 -- iter: 1309/1309 --
Our model finish to train with an overall accuracy around 81%, which means that it can predict the correct outcome (survived or not) for 81% of the total passengers.
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