keras中的级联模型（自动编码器+分类器）

Question

我正在构建一个级联模型（一个与分类器堆叠的自动编码器模型）。自编码器的输入是一组图像，自编码器的输出将被输入到预训练的分类器中。

    auto_input= Input(shape=(ch, height, width), name='x_autoen')

    auto_output = autoencoder(auto_input)
    auto_model = Model(input=auto_input, output=auto_output)

    class_output = classifier(auto_output)
    class_model = Model(input=auto_output, output=class_output)

    cascade_model = Model(input=auto_input, output=[auto_output, class_output])

    load_classifier_weights(cascade_model, classifier_weights_path)

    auto_model.compile(optimizer='sgd', loss='mean_squared_error')
    class_model.compile(optimizer='sgd', loss='binary_crossentropy')
    cascade_model.compile(optimizer='sgd', loss='binary_crossentropy') 

但这会返回以下错误。

File "xxxx.py", line 33, in build_model
class_model = Model(input=auto_output, output=class_output)
File "/xxx/local/lib/python2.7/site-packages/keras/engine/topology.py", line 1987, in __init__
str(layers_with_complete_input))
RuntimeError: Graph disconnected: cannot obtain value for tensor x_autoen at layer "x_autoen". The following previous layers were accessed without issue: []

分类代码：

def classifier(inputs):

    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
    conv1 = Dropout(0.2)(conv1)
    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)                                  
    #
    conv2 = Convolution2D(64, 3, 3, activation='relu'', border_mode='same')(pool1)     
    conv2 = Dropout(0.2)(conv2)
    conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)       
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)                                       
    #
    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)     
    conv3 = Dropout(0.2)(conv3)
    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)      

    up1 = merge([UpSampling2D(size=(2, 2))(conv3), conv2], mode='concat', concat_axis=1)#192x24x24
    conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up1)         
    conv4 = Dropout(0.2)(conv4)
    conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv4)      
    #
    up2 = merge([UpSampling2D(size=(2, 2))(conv4), conv1],, mode='concat', concat_axis=1)#96x48x48
    conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up2)         
    conv5 = Dropout(0.2)(conv5)
    conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv5)       
    #
    conv6 = Convolution2D(2, 1, 1, activation='relu', border_mode='same')(conv5)         
    conv6 = core.Reshape((2,patch_height*patch_width))(conv6)                          
    conv6 = core.Permute((2,1))(conv6)                                                  

    conv7 = core.Activation('softmax')(conv6)

    return conv7

根据大牛的评论改正后出错：

ValueError: The model expects 2 input arrays, but only received one array. Found: array with shape (1000, 1, 48, 48)

这是我用来训练级联网络的代码。

cascade_model .fit(imgs_train, imgs_train, nb_epoch=epochs, batch_size=batch_size, verbose=2, shuffle=True, validation_split=0.1, callbacks=[checkpointer])
enter code here

Answer 1

确实，分类器似乎是连接的。

那我猜你可能需要给class_model一个独立的输入。

系统可能无法在图的中间启动模型（图是层的序列，其中一层的输出作为另一层的输入）。

尽管一切似乎都相互关联，但您将图中间的张量作为模型的输入传递。这可能会产生问题。

当我这样做时，我会这样做：

class_input = Input((shapeforclassifierinput))    
class_output = classifier(class_input)
class_model = Model(input=class_input, output=class_output)
#If this gives an error, then your classifier is indeed not connected
#Then I'd suggest using the Concatenate(axis=1)([UpSampling2D(size=(2, 2))(conv3), conv2])    

现在，您可以通过这种方式加入两个模型：

cascade_input = Input(shape=(ch, height, width))
auto_out = auto_model(cascade_input)  
class_out = class_model(auto_out)   
cascade_model = Model(input=cascade_input, output=[auto_out, class_out])