在下面的模型中，代码行 X_train_1 = X_train[:,0:10081,:] 似乎涉及到三维数据的切片操作，而数据集的实际维度是 921 x 10161。请问为什么我们可以进行三维切片操作？谢谢。

inputs_1 = keras.Input(shape=(10081,1))layer1 = Conv1D(64,14)(inputs_1)layer2 = layers.MaxPool1D(5)(layer1)layer3 = Conv1D(64, 14)(layer2)layer4 = layers.GlobalMaxPooling1D()(layer3)inputs_2 = keras.Input(shape=(4,))layer5 = layers.concatenate([layer4, inputs_2])layer6 = Dense(128, activation='relu')(layer5)layer7 = Dense(2, activation='softmax')(layer6)model_2 = keras.models.Model(inputs = [inputs_1, inputs_2], output = [layer7])model_2.summary()X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:10085], df[['Result1','Result2']].values, test_size=0.2) X_train = X_train.to_numpy()X_train = X_train.reshape([X_train.shape[0], X_train.shape[1], 1])X_train_1 = X_train[:,0:10081,:]X_train_2 = X_train[:,10081:10085,:].reshape(736,4)X_test = X_test.to_numpy()X_test = X_test.reshape([X_test.shape[0], X_test.shape[1], 1]) X_test_1 = X_test[:,0:10081,:]X_test_2 = X_test[:,10081:10085,:].reshape(185,4)adam = keras.optimizers.Adam(lr = 0.0005)model_2.compile(loss = 'binary_crossentropy', optimizer = adam, metrics = ['acc'])history = model_2.fit([X_train_1,X_train_2], y_train, epochs = 100, batch_size = 256, validation_split = 0.2, callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)])

回答：

请注意，在切片操作之前，变量被重塑为三维：X_train = X_train.reshape([X_train.shape[0], X_train.shape[1], 1])，实际上是在位置2添加了一个额外的维度。

有关 reshape 的更多信息，请参阅 Numpy 的文档：https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html