mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-19 20:44:39 +09:00
105 lines
3.6 KiB
Python
Executable File
105 lines
3.6 KiB
Python
Executable File
"""
|
|
example.py
|
|
|
|
Demonstrates usage of PSOkeras module by training dense Keras model for classifying Iris data set. Also compares
|
|
results with a number of independent runs of standard Backpropagation algorithm (Adam) equal to the particle count.
|
|
|
|
@author Mike Holcomb (mjh170630@utdallas.edu)
|
|
"""
|
|
|
|
import tensorflow as tf
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.model_selection import train_test_split
|
|
from tensorflow import keras
|
|
from tensorflow.keras.layers import Dense
|
|
from tensorflow.keras.models import Sequential
|
|
|
|
from psokeras import Optimizer
|
|
|
|
N = 50 # number of particles
|
|
STEPS = 500 # number of steps
|
|
LOSS = "mse" # Loss function
|
|
BATCH_SIZE = 32 # Size of batches to train on
|
|
|
|
|
|
def build_model(loss):
|
|
"""
|
|
Builds test Keras model for predicting Iris classifications
|
|
|
|
:param loss (str): Type of loss - must be one of Keras accepted keras losses
|
|
:return: Keras dense model of predefined structure
|
|
"""
|
|
model = Sequential()
|
|
model.add(Dense(4, activation="sigmoid", input_dim=4, use_bias=True))
|
|
model.add(Dense(4, activation="sigmoid", use_bias=True))
|
|
model.add(Dense(3, activation="softmax", use_bias=True))
|
|
|
|
model.compile(loss=loss, optimizer="adam")
|
|
|
|
return model
|
|
|
|
|
|
def vanilla_backpropagation(x_train, y_train):
|
|
"""
|
|
Runs N number of backpropagation model training simulations
|
|
:param x_train: x values to train on
|
|
:param y_train: target labels to train with
|
|
:return: best model run as measured by LOSS
|
|
"""
|
|
best_model = None
|
|
best_score = 100.0
|
|
|
|
for i in range(N):
|
|
model_s = build_model(LOSS)
|
|
model_s.fit(x_train, y_train, epochs=STEPS, batch_size=BATCH_SIZE, verbose=0)
|
|
train_score = model_s.evaluate(
|
|
x_train, y_train, batch_size=BATCH_SIZE, verbose=0
|
|
)
|
|
if train_score < best_score:
|
|
best_model = model_s
|
|
best_score = train_score
|
|
return best_model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Section I: Build the data set
|
|
iris = load_iris()
|
|
x_train, x_test, y_train, y_test = train_test_split(
|
|
iris.data,
|
|
keras.utils.to_categorical(iris.target, num_classes=None),
|
|
test_size=0.5,
|
|
random_state=0,
|
|
stratify=iris.target,
|
|
)
|
|
|
|
# Section II: First run the backpropagation simulation
|
|
model_s = vanilla_backpropagation(x_train=x_train, y_train=y_train)
|
|
|
|
b_train_score = model_s.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0)
|
|
b_test_score = model_s.evaluate(x_test, y_test, batch_size=BATCH_SIZE, verbose=0)
|
|
print("Backprop -- train: {:.4f} test: {:.4f}".format(b_train_score, b_test_score))
|
|
|
|
# Section III: Then run the particle swarm optimization
|
|
# First build model to train on (primarily used for structure, also included in swarm)
|
|
model_p = build_model(LOSS)
|
|
|
|
# Instantiate optimizer with model, loss function, and hyperparameters
|
|
pso = Optimizer(
|
|
model=model_p,
|
|
loss=LOSS,
|
|
n=N, # Number of particles
|
|
acceleration=1.0, # Contribution of recursive particle velocity (acceleration)
|
|
local_rate=0.6, # Contribution of locally best weights to new velocity
|
|
global_rate=0.4, # Contribution of globally best weights to new velocity
|
|
)
|
|
|
|
# Train model on provided data
|
|
pso.fit(x_train, y_train, steps=STEPS, batch_size=BATCH_SIZE)
|
|
|
|
# Get a copy of the model with the globally best weights
|
|
model_p = pso.get_best_model()
|
|
|
|
p_train_score = model_p.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0)
|
|
p_test_score = model_p.evaluate(x_test, y_test, batch_size=BATCH_SIZE, verbose=0)
|
|
print("PSO -- train: {:.4f} test: {:.4f}".format(p_train_score, p_test_score))
|