我成功运行代码(查找代码的原始链接)，使用PyGAD的遗传算法训练Keras模型:
import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,
weights_vector=solution)
model.set_weights(weights=model_weights_matrix)
predictions = model.predict(data_inputs)
mae = tensorflow.keras.losses.MeanAbsoluteError()
abs_error = mae(data_outputs, predictions).numpy() + 0.00000001
solution_fitness = 1.0 / abs_error
return solution_fitness
def callback_generation(ga_instance):
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
input_layer = tensorflow.keras.layers.Input(3)
dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer)
output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1)
model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)
weights_vector = pygad.kerasga.model_weights_as_vector(model=model)
keras_ga = pygad.kerasga.KerasGA(model=model,
num_solutions=10)
# Data inputs
data_inputs = numpy.array([[0.02, 0.1, 0.15],
[0.7, 0.6, 0.8],
[1.5, 1.2, 1.7],
[3.2, 2.9, 3.1]])
# Data outputs
data_outputs = numpy.array([[0.1],
[0.6],
[1.3],
[2.5]])
num_generations = 10
num_parents_mating = 5
initial_population = keras_ga.population_weights
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
initial_population=initial_population,
fitness_func=fitness_func,
on_generation=callback_generation)
ga_instance.run()
# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
ga_instance.plot_result(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4)
# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
# Fetch the parameters of the best solution.
best_solution_weights = pygad.kerasga.model_weights_as_matrix(model=model,
weights_vector=solution)
model.set_weights(best_solution_weights)
predictions = model.predict(data_inputs)
print("Predictions : n", predictions)
mae = tensorflow.keras.losses.MeanAbsoluteError()
abs_error = mae(data_outputs, predictions).numpy()
print("Absolute Error : ", abs_error)
:
Fitness value of the best solution = 5.007608966738384
Index of the best solution : 0
1/1 [==============================] - 0s 18ms/step
Predictions :
[[0.4351511 ]
[0.78366435]
[1.3436508 ]
[2.736318 ]]
Absolute Error : 0.1996961
据我所知，上面的代码应该训练一个模型来帮助我预测一个新的三维输入，如[0.9, 0.7, 0.85]。
我想知道我如何修改代码以适应如下输入和输出数据，或者调用模型然后对新的data_inputs = numpy.array([[0.9, 0.7, 0.85]])进行预测:
# Data inputs
data_inputs = numpy.array([[0.02, 0.1, 0.15],
[0.7, 0.6, 0.8],
[1.5, 1.2, 1.7],
[3.2, 2.9, 3.1],
[0.9, 0.7, 0.85] # new entry which need forecast
])
# Data outputs
data_outputs = numpy.array([[0.1],
[0.6],
[1.3],
[2.5]]) # Output data for training
非常感谢您事先的帮助。
我的试用代码:
from tensorflow import keras
# Load model and weights
with open("./ga_model.json", "r") as json_file:
model_json = json_file.read()
model = keras.models.model_from_json(model_json)
model.load_weights("./ga_model.h5")
# Data inputs
new_data_inputs = numpy.array([
[0.9, 0.7, 0.85] # new entry which need forecast
])
predictions = model.predict(new_data_inputs)
print("Predictions : n", predictions)
:
Predictions :
[[0.8672837]]