我有一维序列数据，我想将其用作Keras VGG 分类模型的输入，分为 x_train 和 x_test。对于每个序列，我还有一些自定义特征，存储在 feats_train 和 feats_test 中，我不希望这些特征输入到卷积层，而是希望它们输入到第一个全连接层。
因此，一个完整的训练或测试样本将包括一个一维序列加上n个浮点特征。
将自定义特征首先输入到全连接层的最佳方法是什么？我考虑过将输入序列和自定义特征进行拼接，但不知道如何在模型内部将它们分开处理。还有其他选项吗？
没有自定义特征的代码如下：
x_train, x_test, y_train, y_test, feats_train, feats_test = load_balanced_datasets()model = Sequential()model.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))model.add(Conv1D(10, 5, activation='relu'))model.add(MaxPooling1D(pool_size=2))model.add(Dropout(0.5, seed=789))model.add(Conv1D(5, 6, activation='relu'))model.add(Conv1D(5, 6, activation='relu'))model.add(MaxPooling1D(pool_size=2))model.add(Dropout(0.5, seed=789))model.add(Flatten())model.add(Dense(512, activation='relu'))model.add(Dropout(0.5, seed=789))model.add(Dense(2, activation='softmax'))model.compile(loss='logcosh', optimizer='adam', metrics=['accuracy'])model.fit(x_train, y_train, batch_size=batch_size, epochs=20, shuffle=False, verbose=1)y_pred = model.predict(x_test)回答：
Sequential 模型的灵活性较差。你应该考虑使用函数式API。
我会尝试这样做：
from keras.layers import (Conv1D, MaxPool1D, Dropout, Flatten, Dense, Input, concatenate)from keras.models import Model, Sequentialtimesteps = 50n = 5def network(): sequence = Input(shape=(timesteps, 1), name='Sequence') features = Input(shape=(n,), name='Features') conv = Sequential() conv.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1))) conv.add(Conv1D(10, 5, activation='relu')) conv.add(MaxPool1D(2)) conv.add(Dropout(0.5, seed=789)) conv.add(Conv1D(5, 6, activation='relu')) conv.add(Conv1D(5, 6, activation='relu')) conv.add(MaxPool1D(2)) conv.add(Dropout(0.5, seed=789)) conv.add(Flatten()) part1 = conv(sequence) merged = concatenate([part1, features]) final = Dense(512, activation='relu')(merged) final = Dropout(0.5, seed=789)(final) final = Dense(2, activation='softmax')(final) model = Model(inputs=[sequence, features], outputs=[final]) model.compile(loss='logcosh', optimizer='adam', metrics=['accuracy']) return modelm = network()