Ploss_total_PSO_inner(i)=sum(Ploss_PSO(i,Time))*1e3;这句报错位置 2 处的索引超出数组边界(不能超出 3)
时间: 2023-03-02 12:35:25 浏览: 85
这个错误提示是在代码中某个位置出现了数组索引超出边界的错误。根据提示信息,这个错误发生在第二个位置,也就是代码中的某个数组索引超出了允许的范围。根据错误提示信息,该数组的允许范围是 0 到 2,但是在代码中使用了一个大于 2 的索引值。
具体来说,这段代码中的一个叫做 Ploss_total_PSO_inner 的变量,在计算过程中使用了一个名为 Ploss_PSO 的数组,其中第二个索引值超出了允许的范围,导致程序抛出了这个错误。要解决这个问题,需要检查代码中涉及到数组索引的部分,确认索引值的范围是否正确,并且确保不会超出数组的边界。
相关问题
keras MNIST PSO
你可以使用 Keras 和 Particle Swarm Optimization(PSO)来训练 MNIST 数据集上的模型。首先,导入必要的库和模块:
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.datasets import mnist
from keras.utils import to_categorical
from pyswarm import pso
```
然后,加载 MNIST 数据集并进行预处理:
```python
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 将图像数据从三维数组转换为二维数组
X_train = X_train.reshape((60000, 28 * 28))
X_test = X_test.reshape((10000, 28 * 28))
# 将图像数据缩放到 0-1 范围内
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
# 将标签进行 one-hot 编码
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
```
接下来,定义模型的结构和目标函数:
```python
def create_model(optimizer='adam'):
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(28 * 28,)))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
def objective_function(weights):
model = create_model()
model.set_weights(weights)
loss, _ = model.evaluate(X_train, y_train, verbose=0)
return loss
```
最后,使用 PSO 来优化模型的权重:
```python
# 定义搜索空间和参数范围
n_variables = sum(p.shape.num_elements() for p in create_model().get_weights())
lb = [-1] * n_variables
ub = [1] * n_variables
# 使用 PSO 进行优化
best_weights, best_cost = pso(objective_function, lb, ub)
# 根据优化结果重新设置模型的权重
model = create_model()
model.set_weights(best_weights)
```
这样,你就可以使用 PSO 优化 Keras 模型在 MNIST 数据集上的表现了。记得根据实际需求调整模型结构和 PSO 的参数。
pso-lstm代码 matlab
这里是一个简单的 MATLAB 实现 PSO-LSTM 的代码,供你参考:
```matlab
% PSO-LSTM Code
% Load data
load('data.mat');
% Initialize PSO parameters
num_particles = 20;
num_iterations = 100;
w = 0.729; % inertia weight
c1 = 1.49445; % cognitive weight
c2 = 1.49445; % social weight
dim = 4*hidden_size+3; % number of parameters in LSTM
% Initialize LSTM parameters
hidden_size = 10;
input_size = 1;
output_size = 1;
sigma = 0.1; % standard deviation for parameter initialization
theta = sigma*randn(dim,1); % initialize LSTM parameters
% Initialize PSO variables
v = zeros(dim,num_particles);
p = repmat(theta,1,num_particles);
p_best = p;
p_best_fitness = Inf(1,num_particles);
g_best = theta;
g_best_fitness = Inf;
% Train LSTM with PSO
for i=1:num_iterations
% Evaluate fitness of particles
for j=1:num_particles
% Get LSTM parameters from particle
lstm_params = reshape(p(:,j),[],hidden_size+output_size);
Wf = lstm_params(1:hidden_size, :);
Wi = lstm_params(hidden_size+1:2*hidden_size, :);
Wc = lstm_params(2*hidden_size+1:3*hidden_size, :);
Wo = lstm_params(3*hidden_size+1:4*hidden_size, :);
Wout = lstm_params(end, :);
% Train LSTM on data
[loss, ~] = train_lstm(data, Wf, Wi, Wc, Wo, Wout);
% Update particle best
if loss < p_best_fitness(j)
p_best(:,j) = p(:,j);
p_best_fitness(j) = loss;
end
% Update global best
if loss < g_best_fitness
g_best = p(:,j);
g_best_fitness = loss;
end
end
% Update particle velocities and positions
for j=1:num_particles
r1 = rand(dim,1);
r2 = rand(dim,1);
v(:,j) = w*v(:,j) + c1*r1.*(p_best(:,j)-p(:,j)) + c2*r2.*(g_best-p(:,j));
p(:,j) = p(:,j) + v(:,j);
end
end
% Evaluate LSTM on test data
lstm_params = reshape(g_best,[],hidden_size+output_size);
Wf = lstm_params(1:hidden_size, :);
Wi = lstm_params(hidden_size+1:2*hidden_size, :);
Wc = lstm_params(2*hidden_size+1:3*hidden_size, :);
Wo = lstm_params(3*hidden_size+1:4*hidden_size, :);
Wout = lstm_params(end, :);
[~, predictions] = train_lstm(test_data, Wf, Wi, Wc, Wo, Wout);
```
需要注意的是,这只是一个简单实现,实际应用中需要对代码进行优化和调整。