mirror of https://github.com/VLSIDA/OpenRAM.git
48 lines
1.4 KiB
Python
48 lines
1.4 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 tensorflow import keras
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from tensorflow.keras import layers
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import tensorflow as tf
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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 generate_model(self, features, labels):
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"""
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Supervised training of model.
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"""
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model = keras.Sequential([
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layers.Dense(32, activation=tf.nn.relu, input_shape=[features.shape[1]]),
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layers.Dense(32, activation=tf.nn.relu),
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layers.Dense(32, activation=tf.nn.relu),
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layers.Dense(1)
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])
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optimizer = keras.optimizers.RMSprop(0.0099)
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model.compile(loss='mean_squared_error', optimizer=optimizer)
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model.fit(features, labels, epochs=100, verbose=0)
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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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return pred
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