mirror of https://github.com/VLSIDA/OpenRAM.git
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# See LICENSE for licensing information.
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#
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# Copyright (c) 2016-2019 Regents of the University of California and The Board
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# of Regents for the Oklahoma Agricultural and Mechanical College
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# (acting for and on behalf of Oklahoma State University)
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# All rights reserved.
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#
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from .regression_model import regression_model
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from globals import OPTS
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import debug
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from sklearn.neural_network import MLPRegressor
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class neural_network(regression_model):
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def __init__(self, sram, spfile, corner):
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super().__init__(sram, spfile, corner)
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def get_model(self):
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return MLPRegressor(solver='lbfgs', alpha=1e-5,
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hidden_layer_sizes=(40, 40, 40, 40), random_state=1)
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def generate_model(self, features, labels):
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"""
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Training multilayer model
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"""
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flat_labels = np.ravel(labels)
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model = self.get_model()
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model.fit(features, flat_labels)
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return model
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def model_prediction(self, model, features):
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"""
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Have the model perform a prediction and unscale the prediction
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as the model is trained with scaled values.
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"""
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pred = model.predict(features)
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reshape_pred = np.reshape(pred, (len(pred),1))
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return reshape_pred
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