作为一个作业，我想尝试使用python创建一个基于TempCNN的半监督分类
我试图在本教程中找到一些使用逻辑回归模型的代码。
# prepare semi-supervised learning dataset
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# define dataset
X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=1)
# split into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=1, stratify=y)
# split train into labeled and unlabeled
X_train_lab, X_test_unlab, y_train_lab, y_test_unlab = train_test_split(X_train, y_train, test_size=0.50, random_state=1, stratify=y_train)
# summarize training set size
print('Labeled Train Set:', X_train_lab.shape, y_train_lab.shape)
print('Unlabeled Train Set:', X_test_unlab.shape, y_test_unlab.shape)
# summarize test set size
print('Test Set:', X_test.shape, y_test.shape)
# baseline performance on the semi-supervised learning dataset
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
# define dataset
X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, random_state=1)
# split into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=1, stratify=y)
# split train into labeled and unlabeled
X_train_lab, X_test_unlab, y_train_lab, y_test_unlab = train_test_split(X_train, y_train, test_size=0.50, random_state=1, stratify=y_train)
# define model
model = LogisticRegression()
# fit model on labeled dataset
model.fit(X_train_lab, y_train_lab)
# make predictions on hold out test set
yhat = model.predict(X_test)
# calculate score for test set
score = accuracy_score(y_test, yhat)
# summarize score
print('Accuracy: %.3f' % (score*100))
我的问题是：
如果我想使用 CNN 架构执行分类，是否可以直接将分类器模型 = LogisticRegression() 替换为我的新代码而不做任何修改？
如果我使用卫星图像时间序列作为数据，我的标记数据是否成为真实数据，而我的未标记数据是图像内容？