Re-write Chapter 1 in Book to use new Fluid API #524
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| ### Save Topology | ||
| ### Train Program Configuration | ||
| The train_program must return the avg_loss as its first returned parameter and then use the inference_program to setup the train_program |
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You might need to explain a bit more on the Train program topic.
| step = 0 | ||
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| def event_handler_plot(event): |
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Since you are plotting the graph, why not keep using the event_handler_plot?
| print('loss is less than 10.0, stop') | ||
| trainer.stop() | ||
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| if step >= 2000: |
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not sure if we need to terminate early. Fit a line is pretty small.
| train_title = "Train cost" | ||
| test_title = "Test cost" | ||
| plot_cost = Ploter(train_title, test_title) | ||
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| step = 0 |
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EndStepEvent provide step already. There is no need to create another variable to keep track of it.
| ``` | ||
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| ### Start Training | ||
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| ```python | ||
| %matplotlib inline |
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This will show the image inline in Jupyter notebook, otherwise it shows something like Figure(640x480)
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It helps with plotting the graphics inline.
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and it needs to be inside of a python block?
| ```python | ||
| paddle.init(use_gpu=False, trainer_count=1) | ||
| ``` | ||
| We first define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. Users can shuffle a batch out of a `buf_size` in order to make the data random. |
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Users can shuffle a batch out of a buf_size is a bit confusing...
| ``` | ||
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| ### Create Trainer | ||
| The trainer will take the train_program. |
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... train_program as input.
| ### Apply model | ||
| ### Inference | ||
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| Initialize the Inferencer with the inference_program and the params_folder, which is where we saved our params |
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maybe it should be params_dirname instead of params_folder?
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| #### 1. generate testing data | ||
| #### Setup the Inference Program. | ||
| Similar to the trainer.train, the Inferencer needs to take an inference_program to do inferring. |
| ``` | ||
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| ### Save Topology | ||
| ### Train Program Configuration | ||
| The train_program must return the avg_loss as its first returned parameter and then use the inference_program to setup the train_program |
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I feel "use the inference_program to setup the train_program" little bit confusing because we did not show inference_program until later
| @@ -116,49 +117,58 @@ When training complex models, we usually have one more split: the validation set | |||
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| `fit_a_line/trainer.py` demonstrates the training using [PaddlePaddle](http://paddlepaddle.org). | |||
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| ### Initialize PaddlePaddle | |||
| ### Datafeeder Configuration | |||
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All these sections including training and inference are under ##Dataset section, should we bring these 1 level up?
Both Jupyter and train.py are working. See the attached screenshots 😀