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remove complie_model method
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@MRossol
MRossol committed Sep 21, 2020
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24 changes: 23 additions & 1 deletion README.rst
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phygnn
######

.. image:: https://github.com/NREL/phygnn/workflows/Documentation/badge.svg
:target: https://nrel.github.io/phygnn/

.. image:: https://github.com/NREL/phygnn/workflows/Pytests/badge.svg
:target: https://github.com/NREL/phygnn/actions?query=workflow%3A%22Pytests%22

.. image:: https://github.com/NREL/phygnn/workflows/Lint%20Code%20Base/badge.svg
:target: https://github.com/NREL/phygnn/actions?query=workflow%3A%22Lint+Code+Base%22

.. image:: https://img.shields.io/pypi/pyversions/nrel-phygnn.svg
:target: https://pypi.org/project/nrel-phygnn/

.. image:: https://badge.fury.io/py/nrel-phygnn.svg
:target: https://badge.fury.io/py/nrel-phygnn

.. image:: https://anaconda.org/nrel/nrel-phygnn/badges/version.svg
:target: https://anaconda.org/nrel/nrel-phygnn

.. image:: https://anaconda.org/nrel/nrel-phygnn/badges/license.svg
:target: https://anaconda.org/nrel/nrel-phygnn

phygnn stands for **physics-guided neural networks**.

This implementation of physics-guided neural networks augments a traditional
Expand All @@ -14,7 +35,8 @@ when applying purely data-driven machine learning models to scientific
applications, such as when machine learning models produce physically
inconsistent results or have trouble generalizing to out-of-sample scenarios.

For details on the phygnn class framework see `the phygnn module documentation here. <https://nrel.github.io/phygnn/phygnn/phygnn.phygnn.html>`_
For details on the phygnn class framework see `the phygnn module documentation
here. <https://nrel.github.io/phygnn/phygnn/phygnn.phygnn.html>`_

At the National Renewable Energy Lab (NREL), we are using the phygnn framework
to supplement traditional satellite-based cloud property prediction models. We
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136 changes: 16 additions & 120 deletions phygnn/model_interfaces/phygnn_model.py
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Expand Up @@ -107,110 +107,6 @@ def history(self):
"""
return self.model.history

@staticmethod
def compile_model(p_fun, loss_weights=(0.5, 0.5),
n_features=1, n_labels=1, hidden_layers=None,
metric='mae', initializer=None, optimizer=None,
learning_rate=0.01, history=None,
kernel_reg_rate=0.0, kernel_reg_power=1,
bias_reg_rate=0.0, bias_reg_power=1,
feature_names=None, output_names=None):
"""
Parameters
----------
p_fun : function
Physics function to guide the neural network loss function.
This function must take (y_predicted, y_true, p, **p_kwargs)
as arguments with datatypes (tf.Tensor, np.ndarray, np.ndarray).
The function must return a tf.Tensor object with a single numeric
loss value (output.ndim == 0).
loss_weights : tuple, optional
Loss weights for the neural network y_predicted vs. y_true
and for the p_fun loss, respectively. For example,
loss_weights=(0.0, 1.0) would simplify the phygnn loss function
to just the p_fun output.
n_features : int, optional
Number of input features.
n_labels : int, optional
Number of output labels.
hidden_layers : list, optional
List of dictionaries of key word arguments for each hidden
layer in the NN. Dense linear layers can be input with their
activations or separately for more explicit control over the layer
ordering. For example, this is a valid input for hidden_layers that
will yield 7 hidden layers (9 layers total):
[{'units': 64, 'activation': 'relu', 'dropout': 0.01},
{'units': 64},
{'batch_normalization': {'axis': -1}},
{'activation': 'relu'},
{'dropout': 0.01}]
metric : str, optional
Loss metric option for the NN loss function (not the physical
loss function). Must be a valid key in phygnn.loss_metrics.METRICS
initializer : tensorflow.keras.initializers, optional
Instantiated initializer object. None defaults to GlorotUniform
optimizer : tensorflow.keras.optimizers, optional
Instantiated neural network optimization object.
None defaults to Adam.
learning_rate : float, optional
Optimizer learning rate.
history : None | pd.DataFrame, optional
Learning history if continuing a training session.
kernel_reg_rate : float, optional
Kernel regularization rate. Increasing this value above zero will
add a structural loss term to the loss function that
disincentivizes large hidden layer weights and should reduce
model complexity. Setting this to 0.0 will disable kernel
regularization.
kernel_reg_power : int, optional
Kernel regularization power. kernel_reg_power=1 is L1
regularization (lasso regression), and kernel_reg_power=2 is L2
regularization (ridge regression).
bias_reg_rate : float, optional
Bias regularization rate. Increasing this value above zero will
add a structural loss term to the loss function that
disincentivizes large hidden layer biases and should reduce
model complexity. Setting this to 0.0 will disable bias
regularization.
bias_reg_power : int, optional
Bias regularization power. bias_reg_power=1 is L1
regularization (lasso regression), and bias_reg_power=2 is L2
regularization (ridge regression).
feature_names : list | tuple | None, optional
Training feature names (strings). Mostly a convenience so that a
loaded-from-disk model will have declared feature names, making it
easier to feed in features for prediction. This will also get set
if phygnn is trained on a DataFrame.
output_names : list | tuple | None, optional
Prediction output names (strings). Mostly a convenience so that a
loaded-from-disk model will have declared output names, making it
easier to understand prediction output. This will also get set
if phygnn is trained on a DataFrame.
Returns
-------
model : PhysicsGuidedNeuralNetwork
Instantiated phygnn model
"""
model = PhysicsGuidedNeuralNetwork(p_fun,
loss_weights=loss_weights,
n_features=n_features,
n_labels=n_labels,
hidden_layers=hidden_layers,
metric=metric,
initializer=initializer,
optimizer=optimizer,
learning_rate=learning_rate,
history=history,
kernel_reg_rate=kernel_reg_rate,
kernel_reg_power=kernel_reg_power,
bias_reg_rate=bias_reg_rate,
bias_reg_power=bias_reg_power,
feature_names=feature_names,
output_names=output_names)

return model

def train_model(self, features, labels, p, n_batch=16, n_epoch=10,
shuffle=True, validation_split=0.2, run_preflight=True,
return_diagnostics=False, p_kwargs=None,
Expand Down Expand Up @@ -399,22 +295,22 @@ def build(cls, p_fun, feature_names, label_names,
if isinstance(label_names, str):
label_names = [label_names]

model = cls.compile_model(p_fun,
loss_weights=loss_weights,
n_features=len(feature_names),
n_labels=len(label_names),
hidden_layers=hidden_layers,
metric=metric,
initializer=initializer,
optimizer=optimizer,
learning_rate=learning_rate,
history=history,
kernel_reg_rate=kernel_reg_rate,
kernel_reg_power=kernel_reg_power,
bias_reg_rate=bias_reg_rate,
bias_reg_power=bias_reg_power,
feature_names=feature_names,
output_names=label_names)
model = PhysicsGuidedNeuralNetwork(p_fun,
loss_weights=loss_weights,
n_features=len(feature_names),
n_labels=len(label_names),
hidden_layers=hidden_layers,
metric=metric,
initializer=initializer,
optimizer=optimizer,
learning_rate=learning_rate,
history=history,
kernel_reg_rate=kernel_reg_rate,
kernel_reg_power=kernel_reg_power,
bias_reg_rate=bias_reg_rate,
bias_reg_power=bias_reg_power,
feature_names=feature_names,
output_names=label_names)

model = cls(model, feature_names=feature_names,
label_names=label_names, normalize=normalize)
Expand Down

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