Keras model.pop()不工作

Question

我使用Python3.6.2和Tensorflow-GPU1.3.0运行Keras2.0.6。

为了对Vgg16模型进行微调，在手工构建了一个vgg16体系结构并加载了权重之后，我运行了这段代码，但是我还没有调用vgg16()：

model = self.model
model.pop()
for layer in model.layers: layer.trainable=False
model.add(Dense(num, activation='softmax'))
self.compile()

当我检查Tensorboard中的图形时，我看到(查看附图的左上角) dense_3连接到dropout_2，但它自己在晃动。然后在它旁边我看到dense_4，也连接到dropout_2。

Tensorboard模型图

2016年5月6日，我试图按照joelthchao的建议，用下面的pop_layer()代码替换pop()。不幸的是，Tensorboard中显示的图表变成了无法理解的混乱。

def pop_layer(model):
    if not model.outputs:
        raise Exception('Sequential model cannot be popped: model is empty.')

    model.layers.pop()
    if not model.layers:
        model.outputs = []
        model.inbound_nodes = []
        model.outbound_nodes = []
    else:
        model.layers[-1].outbound_nodes = []
        model.outputs = [model.layers[-1].output]
    model.built = False

我知道有些东西不能正常工作，因为我在Kaggle猫对狗的比赛中运行时精度很低，在比赛中我徘徊在90%左右，而其他运行这段代码(它是从fast.ai改编的)在Theanos之上很容易得到97%。也许我的准确性问题来自其他地方，但我仍然不认为dense_3应该在那里悬空，我想知道这是否可能是我的精度问题的根源。

我如何才能明确地断开和删除dense_3？

在运行代码之前和之后，请参见下面的model.summary()，以便为微调做准备。我们不再看到dense_3了，但是我们确实在张图中看到了它。

跑前

Layer (type)                 Output Shape              Param #   
=================================================================
lambda_1 (Lambda)            (None, 3, 224, 224)       0         
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 3, 226, 226)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 64, 224, 224)      1792      
_________________________________________________________________
zero_padding2d_2 (ZeroPaddin (None, 64, 226, 226)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 224, 224)      36928     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 112, 112)      0         
_________________________________________________________________
zero_padding2d_3 (ZeroPaddin (None, 64, 114, 114)      0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 128, 112, 112)     73856     
_________________________________________________________________
zero_padding2d_4 (ZeroPaddin (None, 128, 114, 114)     0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 128, 112, 112)     147584    
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 128, 56, 56)       0         
_________________________________________________________________
zero_padding2d_5 (ZeroPaddin (None, 128, 58, 58)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 256, 56, 56)       295168    
_________________________________________________________________
zero_padding2d_6 (ZeroPaddin (None, 256, 58, 58)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 256, 56, 56)       590080    
_________________________________________________________________
zero_padding2d_7 (ZeroPaddin (None, 256, 58, 58)       0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 256, 56, 56)       590080    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 256, 28, 28)       0         
_________________________________________________________________
zero_padding2d_8 (ZeroPaddin (None, 256, 30, 30)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 512, 28, 28)       1180160   
_________________________________________________________________
zero_padding2d_9 (ZeroPaddin (None, 512, 30, 30)       0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 512, 28, 28)       2359808   
_________________________________________________________________
zero_padding2d_10 (ZeroPaddi (None, 512, 30, 30)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 512, 28, 28)       2359808   
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 512, 14, 14)       0         
_________________________________________________________________
zero_padding2d_11 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
zero_padding2d_12 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
zero_padding2d_13 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 512, 7, 7)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 25088)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              102764544 
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dropout_2 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 1000)              4097000   
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0

跑后

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lambda_1 (Lambda)            (None, 3, 224, 224)       0         
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 3, 226, 226)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 64, 224, 224)      1792      
_________________________________________________________________
zero_padding2d_2 (ZeroPaddin (None, 64, 226, 226)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 224, 224)      36928     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 112, 112)      0         
_________________________________________________________________
zero_padding2d_3 (ZeroPaddin (None, 64, 114, 114)      0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 128, 112, 112)     73856     
_________________________________________________________________
zero_padding2d_4 (ZeroPaddin (None, 128, 114, 114)     0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 128, 112, 112)     147584    
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 128, 56, 56)       0         
_________________________________________________________________
zero_padding2d_5 (ZeroPaddin (None, 128, 58, 58)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 256, 56, 56)       295168    
_________________________________________________________________
zero_padding2d_6 (ZeroPaddin (None, 256, 58, 58)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 256, 56, 56)       590080    
_________________________________________________________________
zero_padding2d_7 (ZeroPaddin (None, 256, 58, 58)       0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 256, 56, 56)       590080    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 256, 28, 28)       0         
_________________________________________________________________
zero_padding2d_8 (ZeroPaddin (None, 256, 30, 30)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 512, 28, 28)       1180160   
_________________________________________________________________
zero_padding2d_9 (ZeroPaddin (None, 512, 30, 30)       0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 512, 28, 28)       2359808   
_________________________________________________________________
zero_padding2d_10 (ZeroPaddi (None, 512, 30, 30)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 512, 28, 28)       2359808   
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 512, 14, 14)       0         
_________________________________________________________________
zero_padding2d_11 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
zero_padding2d_12 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
zero_padding2d_13 (ZeroPaddi (None, 512, 16, 16)       0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 512, 14, 14)       2359808   
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 512, 7, 7)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 25088)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              102764544 
_________________________________________________________________
dropout_1 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dropout_2 (Dropout)          (None, 4096)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 2)                 8194      
=================================================================
Total params: 134,268,738
Trainable params: 8,194
Non-trainable params: 134,260,544

Answer 1

我相信当使用tensorflow后端时，这是一个在Keras中实现layers.pop()的layers.pop。现在，这里是一个按名称删除最后一层的工作：

name_last_layer = str(model1.layers[-1])

model2 = Sequential()
for layer in model1.layers:
    if str(layer) != name_last_layer:
        model2.add(layer)

其中，model1是您的原始模型，而model2是相同的模型，没有最后一层。在本例中，我将model2设置为顺序模型，但您当然可以更改这个模型。