Neuraxle refactor #32
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Update code since last changes.
…MRNNTensorflow fit, and transform methods
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@alexbrillant Thank you for the contribution! Let's clean this up together soon before merging. |
…PI call notebook.
…tivity-Recognition into neuraxle-refactor
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This looks cool.
Note that I haven't yet reviewed the DeepLearningPipeline yet so the present PR may wait. Let's finish the seq2seq's refactor first.
| from neuraxle.steps.output_handlers import InputAndOutputTransformerMixin | ||
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| class FormatData(NonFittableMixin, InputAndOutputTransformerMixin, BaseStep): |
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Might be replaced by this?
Pipeline([
ToNumpy(),
OutputTransformerWrapper(ToNumpy())
])
| expected_outputs = np.array(expected_outputs) | ||
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| if expected_outputs.shape != (len(data_inputs), self.n_classes): | ||
| expected_outputs = np.reshape(expected_outputs, (len(data_inputs), self.n_classes)) |
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This if should not be needed. Use a OutputTransformerWrapper(OneHotEncoder()) instead.
If you also apply the previous comment, you should end up deleting this FormatData class as things are already done in other existing classes. We should not need any reshape here whatsoever if data is fed correctly, or if the OneHotEncoder works properly.
train_and_save.py
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| ).set_hyperparams( | ||
| HyperparameterSamples({ | ||
| 'n_steps': self.N_STEPS, # 128 timesteps per series | ||
| 'n_inputs': self.N_INPUTS, # 9 input parameters per timestep | ||
| 'n_hidden': self.N_HIDDEN, # Hidden layer num of features | ||
| 'n_classes': self.N_CLASSES, # Total classes (should go up, or should go down) | ||
| 'learning_rate': self.LEARNING_RATE, | ||
| 'lambda_loss_amount': self.LAMBDA_LOSS_AMOUNT, | ||
| 'batch_size': self.BATCH_SIZE | ||
| }) |
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Here, let's only consider n_hidden, learning_rate, and lambda_loss_amount as hyperparameters per se. The others aren't planned to be changed during meta-optimization for instance).
We could let them there for now, however I would have seen them as something else. Looks like this issue perhaps: Neuraxio/Neuraxle#91
We could as well add a n_stacked hyperparam to control how many LSTMs we stack on top of each other (optional feature, not really needed for now).
| def main(): | ||
| pipeline = DeepLearningPipeline( | ||
| HumanActivityRecognitionPipeline(), | ||
| validation_size=0.15, |
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Was the original project using validation data, or only train/test? I'm tempted to remove validation data here to leave the original example untouched. The simplicity was part of its success.
Note : this code uses neruaxle package from the latest commit in this pull requests : Neuraxio/Neuraxle#182
TODO :
Notebook for demonstration.
Validation Split Wrapper.