For questions and comments contact the developer directly at: seilis@unbc.ca.
GrowPy is available through PyPi, and can be installed via pip using
pip install growpy
or
pip3 install growpy
from growpy import models
import tensorflow as tf
import matplotlib.pyplot as plt
# Constuct/Import data
x = tf.abs(tf.random.uniform((100000,1), 0, 10))
y = 500 / (1 + (500-50)/50 * tf.exp(-0.5 * x))
# Construct model
model = models.GeneralizedLogistic()
optimizer = tf.keras.optimizers.Nadam()
loss = tf.keras.losses.MeanSquaredError()
model.compile(optimizer=optimizer, loss=loss)
# Train model
history = model.fit(x, y, epochs=100, batch_size=1000)
# Inspect results
print(model.weights)
fig, axes = plt.subplots(2, 1)
axes[0].scatter(x,y, alpha=0.5, s=1)
axes[0].scatter(x, model(x), alpha=0.5, s=1)
axes[0].set_ylabel('y')
axes[0].set_xlabel('x')
axes[1].plot(history.history['loss'])
axes[1].set_ylabel('MSE')
axes[1].set_xlabel('Epoch')
plt.show()BSD 3-Clause License
Copyright (c) 2021, Galen Seilis All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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