During training, `trainer.train` invokes `event_handler` for certain events. This gives us a chance to print the training progress.
...
...
@@ -305,8 +314,30 @@ print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
Usually, with MNIST data, the softmax regression model can get accuracy around 92.34%, MLP can get about 97.66%, and convolution network can get up to around 99.20%. Convolution layers have been widely considered a great invention for image processsing.
## Application
After training is done, user can use the trained model to classify images. The following code shows how to inference MNIST images through `paddle.infer` interface.
lab=np.argsort(-probs)# probs and lab are the results of one batch data
print"Label of image/infer_3.png is: %d"%lab[0][0]
```
## Conclusion
This tutorial describes a few basic Deep Learning models viz. Softmax regression, Multilayer Perceptron Network and Convolutional Neural Network. The subsequent tutorials will derive more sophisticated models from these. So it is crucial to understand these models for future learning. When our model evolved from a simple softmax regression to slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST data set achieved large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve results of an old one. Moreover, this tutorial introduced the basic flow of PaddlePaddle model design, starting with a dataprovider, model layer construction, to final training and prediction. Readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(
paddle.dataset.mnist.test(), batch_size=128))
print "Test with Pass %d, Cost %f, %s\n" % (
...
...
@@ -322,7 +331,7 @@ trainer.train(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=128),
event_handler=event_handler_plot,
num_passes=100)
num_passes=5)
```
During training, `trainer.train` invokes `event_handler` for certain events. This gives us a chance to print the training progress.
...
...
@@ -347,8 +356,30 @@ print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
Usually, with MNIST data, the softmax regression model can get accuracy around 92.34%, MLP can get about 97.66%, and convolution network can get up to around 99.20%. Convolution layers have been widely considered a great invention for image processsing.
## Application
After training is done, user can use the trained model to classify images. The following code shows how to inference MNIST images through `paddle.infer` interface.
lab = np.argsort(-probs) # probs and lab are the results of one batch data
print "Label of image/infer_3.png is: %d" % lab[0][0]
```
## Conclusion
This tutorial describes a few basic Deep Learning models viz. Softmax regression, Multilayer Perceptron Network and Convolutional Neural Network. The subsequent tutorials will derive more sophisticated models from these. So it is crucial to understand these models for future learning. When our model evolved from a simple softmax regression to slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST data set achieved large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve results of an old one. Moreover, this tutorial introduced the basic flow of PaddlePaddle model design, starting with a dataprovider, model layer construction, to final training and prediction. Readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
