簡介

當我們的任務涉及到多個維度不同的資料來擬合一個目標時，我們需要構建多輸入模型。

模型構建 

假設我們需要搭建如下的模型，輸入資料分別為100維和50維的向量，輸出為0或1：

from keras.layers import Conv1D, Dense, MaxPool1D, concatenate, Flatten
from keras import Input, Model
from keras.utils import plot_model
import numpy as np


def multi_input_model():
    """構建多輸入模型"""
    input1_= Input(shape=(100, 1), name='input1')
    input2_ = Input(shape=(50, 1), name='input2')

    x1 = Conv1D(16, kernel_size=3, strides=1, activation='relu', padding='same')(input1_)
    x1 = MaxPool1D(pool_size=10, strides=10)(x1)

    x2 = Conv1D(16, kernel_size=3, strides=1, activation='relu', padding='same')(input2_)
    x2 = MaxPool1D(pool_size=5, strides=5)(x2)

    x = concatenate([x1, x2])
    x = Flatten()(x)

    x = Dense(10, activation='relu')(x)
    output_ = Dense(1, activation='sigmoid', name='output')(x)

    model = Model(inputs=[input1_, input2_], outputs=[output_])
    model.summary()

    return model

if __name__ == '__main__':
    # 產生訓練資料
    x1 = np.random.rand(100, 100, 1)
    x2 = np.random.rand(100, 50, 1)
    # 產生標籤
    y = np.random.randint(0, 2, (100,))

    model = multi_input_model()
    # 儲存模型圖
    plot_model(model, 'Multi_input_model.png')

    model.compile(optimizer='adam', loss='binary_crossentropy')
    model.fit([x1, x2], y, epochs=10, batch_size=10)