使用PSO优化卷积神经网络的学习率和损失函数系数的例子,PSO使用python自带库pyswarm中的模型
时间: 2024-05-15 16:15:50 浏览: 14
首先,我们需要定义一个带有学习率和损失函数系数的卷积神经网络模型。这里我们以MNIST数据集为例,定义一个简单的卷积神经网络模型:
``` python
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
def create_model(learning_rate, loss_factor):
model = tf.keras.Sequential([
Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
MaxPooling2D((2,2)),
Flatten(),
Dense(10, activation='softmax')
])
optimizer = tf.keras.optimizers.Adam(lr=learning_rate)
loss_function = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss_function, metrics=['accuracy'])
return model
```
接下来,我们使用pyswarm库来进行PSO优化。首先,我们需要定义一个适应度函数,用于计算每个粒子的适应度,即模型在给定超参数下的准确率。
``` python
import numpy as np
from tensorflow.keras.datasets import mnist
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
x_train = x_train.reshape(60000, 28, 28, 1) / 255.0
x_test = x_test.reshape(10000, 28, 28, 1) / 255.0
def fitness_function(params):
# 解压超参数
learning_rate, loss_factor = params
# 创建模型
model = create_model(learning_rate, loss_factor)
# 训练模型
model.fit(x_train, y_train, epochs=1, validation_data=(x_test, y_test))
# 计算模型在验证集上的准确率
_, accuracy = model.evaluate(x_test, y_test)
# 返回适应度
return 1 - accuracy
```
接下来,我们需要定义PSO的参数,包括粒子数量、迭代次数、超参数的上下限等。
``` python
from pyswarm import pso
# 定义PSO的参数
num_particles = 10
max_iterations = 10
learning_rate_range = (0.001, 0.01)
loss_factor_range = (0.1, 1.0)
# 运行PSO算法
lb = [learning_rate_range[0], loss_factor_range[0]]
ub = [learning_rate_range[1], loss_factor_range[1]]
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
best_params, best_fitness = pso(fitness_function, lb, ub, maxiter=max_iterations, swarmsize=num_particles, options=options)
```
最后,我们可以使用得到的最优超参数来重新训练卷积神经网络,并在测试集上进行测试。
``` python
# 创建模型并训练
model = create_model(*best_params)
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
# 在测试集上测试模型
test_loss, test_accuracy = model.evaluate(x_test, y_test)
print("Test Accuracy:", test_accuracy)
```
完整代码如下:
``` python
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import numpy as np
from tensorflow.keras.datasets import mnist
from pyswarm import pso
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
x_train = x_train.reshape(60000, 28, 28, 1) / 255.0
x_test = x_test.reshape(10000, 28, 28, 1) / 255.0
def create_model(learning_rate, loss_factor):
model = tf.keras.Sequential([
Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
MaxPooling2D((2,2)),
Flatten(),
Dense(10, activation='softmax')
])
optimizer = tf.keras.optimizers.Adam(lr=learning_rate)
loss_function = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss_function, metrics=['accuracy'])
return model
def fitness_function(params):
# 解压超参数
learning_rate, loss_factor = params
# 创建模型
model = create_model(learning_rate, loss_factor)
# 训练模型
model.fit(x_train, y_train, epochs=1, validation_data=(x_test, y_test))
# 计算模型在验证集上的准确率
_, accuracy = model.evaluate(x_test, y_test)
# 返回适应度
return 1 - accuracy
# 定义PSO的参数
num_particles = 10
max_iterations = 10
learning_rate_range = (0.001, 0.01)
loss_factor_range = (0.1, 1.0)
# 运行PSO算法
lb = [learning_rate_range[0], loss_factor_range[0]]
ub = [learning_rate_range[1], loss_factor_range[1]]
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
best_params, best_fitness = pso(fitness_function, lb, ub, maxiter=max_iterations, swarmsize=num_particles, options=options)
# 创建模型并训练
model = create_model(*best_params)
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
# 在测试集上测试模型
test_loss, test_accuracy = model.evaluate(x_test, y_test)
print("Test Accuracy:", test_accuracy)
```