When I use the cnn-lstm model, after training, I want to extract features and then reduce dimensionality for visualization, I can’t extract them
# 提取特定参数的数据 x_downsampled = x[downsampled_indices][:, :, selected_params] y_downsampled = y[downsampled_indices] # 数据标准化 scaler = MinMaxScaler() n_samples, time_steps, n_features = x_downsampled.shape x_downsampled_flat = x_downsampled.reshape(n_samples, -1) x_downsampled_flat = scaler.fit_transform(x_downsampled_flat) x_downsampled = x_downsampled_flat.reshape(n_samples, time_steps, n_features) # 划分训练集和测试集 x_train, x_test, y_train, y_test = train_test_split(x_downsampled, y_downsampled, test_size=0.2, random_state=42) # 定义CNN-LSTM模型 model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation=’relu’, input_shape=(time_steps, n_features))) model.add(MaxPooling1D(pool_size=2)) model.add(LSTM(64, […]