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Improvement on memory usage #282

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Mar 27, 2024
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Improvement on memory usage #282

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33 changes: 22 additions & 11 deletions examples/KerasGA/image_classification_CNN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@
import pygad.kerasga
import numpy
import pygad
import gc


def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
Expand All @@ -11,27 +13,33 @@ def fitness_func(ga_instanse, solution, sol_idx):
data=data_inputs)

cce = tensorflow.keras.losses.CategoricalCrossentropy()
solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001)
solution_fitness = 1.0 / \
(cce(data_outputs, predictions).numpy() + 0.00000001)

return solution_fitness


def on_generation(ga_instance):
print(f"Generation = {ga_instance.generations_completed}")
print(f"Fitness = {ga_instance.best_solution()[1]}")
gc.collect() # can useful for not exploding the memory usage on notebooks (ipynb) freeing memory


# Build the keras model using the functional API.
input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3))
conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5,
kernel_size=7,
activation="relu")(input_layer)
max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5),
max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5, 5),
strides=5)(conv_layer1)
conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3,
kernel_size=3,
activation="relu")(max_pool1)
flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2)
dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer)
output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer)
flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2)
dense_layer = tensorflow.keras.layers.Dense(
15, activation="relu")(flatten_layer)
output_layer = tensorflow.keras.layers.Dense(
4, activation="softmax")(dense_layer)

model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)

Expand All @@ -47,13 +55,15 @@ def on_generation(ga_instance):
data_outputs = tensorflow.keras.utils.to_categorical(data_outputs)

# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
num_generations = 200 # Number of generations.
num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
initial_population = keras_ga.population_weights # Initial population of network weights.
num_generations = 200 # Number of generations.
# Number of solutions to be selected as parents in the mating pool.
num_parents_mating = 5
# Initial population of network weights.
initial_population = keras_ga.population_weights

# Create an instance of the pygad.GA class
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
initial_population=initial_population,
fitness_func=fitness_func,
on_generation=on_generation)
Expand All @@ -62,7 +72,8 @@ def on_generation(ga_instance):
ga_instance.run()

# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4)
ga_instance.plot_fitness(
title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4)

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
Expand Down