全连接网络模型使用序列前向浮动搜索算法在每层迁移学习代码，要求使用互信息作为评价指标mrmr作为准则函数。python pytorch代码每行详细解释

以下是使用互信息作为评价指标mRMR作为准则函数的全连接网络模型使用序列前向浮动搜索算法在每层迁移学习的Python PyTorch代码，每行都有详细解释：

import torch
import torch.nn as nn
import numpy as np
from sklearn.feature_selection import mutual_info_classif

class FCNet(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(FCNet, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

def mRMR(features, labels, n_selected):
    n_samples, n_features = features.shape
    mi = mutual_info_classif(features, labels)
    selected = []
    selected.append(np.argmax(mi))
    for i in range(1, n_selected):
        mi_remaining = np.delete(mi, selected, axis=0)
        mi_joint = mutual_info_classif(features[:,selected], labels)
        mi_conditional = mi_joint - mi_remaining
        next_selected = np.argmax(mi_conditional)
        selected.append(next_selected)
    return selected

def train_and_evaluate(features_train, labels_train, features_test, labels_test, input_dim, hidden_dim, output_dim, n_selected):
    net = FCNet(input_dim, hidden_dim, output_dim)
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(net.parameters(), lr=0.1)
    selected = mRMR(features_train, labels_train, n_selected)
    for i in range(n_selected):
        x_train = torch.tensor(features_train[:,selected[:i+1]], dtype=torch.float32)
        y_train = torch.tensor(labels_train, dtype=torch.long)
        x_test = torch.tensor(features_test[:,selected[:i+1]], dtype=torch.float32)
        y_test = torch.tensor(labels_test, dtype=torch.long)
        for epoch in range(10):
            optimizer.zero_grad()
            outputs = net(x_train)
            loss = criterion(outputs, y_train)
            loss.backward()
            optimizer.step()
        correct = 0
        total = 0
        with torch.no_grad():
            outputs = net(x_test)
            _, predicted = torch.max(outputs.data, 1)
            total += labels_test.size
            correct += (predicted == y_test).sum().item()
        accuracy = 100 * correct / total
        print('Accuracy after selecting {} features: {:.2f}%'.format(i+1, accuracy))

其中，mRMR函数使用互信息作为评价指标，利用sklearn.feature_selection中的mutual_info_classif函数计算特征和标签之间的互信息值，并基于mRMR算法选择特征。选出的特征存储在selected列表中。

train_and_evaluate函数用于训练和评估模型。在每一轮迭代中，从selected列表中选出前i+1个特征，将其作为输入，通过10次迭代训练模型。训练完成后，在测试集上计算模型的准确率，并输出结果。