Question

当我尝试训练下面描述的自动编码器时，我收到错误＆＃39;具有形状（256,28,28,1）的目标阵列被传递用于形状（无，0,28,1）的输出，同时使用损失`binary_crossentropy。这种损失要求目标具有与输出相同的形状。＆＃39; 输入和输出尺寸应均为（28,28,1），其中256为批量大小。运行.summary（）确认解码器模型的输出是正确的（28,28,1），但是当编码器和解码器一起编译时，这似乎会改变。知道这里发生了什么吗？生成网络时会连续调用这三个函数。

def buildEncoder():
    input1 = Input(shape=(28,28,1))
    input2 = Input(shape=(28,28,1))
    merge = concatenate([input1,input2])
    convEncode1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(merge)
    maxPoolEncode1 = MaxPooling2D(pool_size=(2, 1))(convEncode1)
    convEncode2 = Conv2D(16, (3,3), activation = 'sigmoid', padding = 'same')(maxPoolEncode1)
    convEncode3 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convEncode2)
    model = Model(inputs = [input1,input2], outputs = convEncode3)
    model.compile(loss='binary_crossentropy', optimizer=adam)
    return model

def buildDecoder():
    input1 = Input(shape=(28,28,1))
    upsample1 = UpSampling2D((2,1))(input1)
    convDecode1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(upsample1)
    crop1 = Cropping2D(cropping = ((0,28),(0,0)))(convDecode1)
    crop2 = Cropping2D(cropping = ((28,0),(0,0)))(convDecode1)
    convDecode2_1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(crop1)
    convDecode3_1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(crop2)
    convDecode2_2 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convDecode2_1)
    convDecode3_2 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convDecode3_1)
    model = Model(inputs=input1, outputs=[convDecode2_2,convDecode3_2])
    model.compile(loss='binary_crossentropy', optimizer=adam)
    return model

def buildAutoencoder():   
    autoInput1 = Input(shape=(28,28,1))
    autoInput2 = Input(shape=(28,28,1))
    encode = encoder([autoInput1,autoInput2])
    decode = decoder(encode)
    model = Model(inputs=[autoInput1,autoInput2], outputs=[decode[0],decode[1]])
    model.compile(loss='binary_crossentropy', optimizer=adam)
    return model

运行model.summary（）函数确认此

的最终输出尺寸

Answer 1

您的编码器看起来有形状错误计算。您假设解码器将获得（无，28,28,1），但您的编码器实际输出（无，14,28,28,1）。

print(encoder) # Tensor("model_1/conv2d_3/Sigmoid:0", shape=(?, 14, 28, 1), dtype=float32)

现在在你的解码器中，你正在裁剪等等，假设你有（28,28,1）可能将它砍成0.模型自己工作，当你连接它们时会发生错配。

多输出Keras模型中消失的维数

1 个答案: