CODEWITHSUNDEEP
pythontensorflowkeras
Mohamed Amine Ouali
Mohamed Amine Ouali

Reputation: 615

Delete layers of keras pretrained model

I want to use vgg16 pre-trained model of keras. I have notice some strange behavior when trying to change the model.

1) I have add some layers of the per-trained model. My problem is that tensorboard is showing the layers of the model that I didn't add into the sequence model. This is strange because I have also deleted the imported model. I think this have to do with the dependency between layers so I want to remove this dependencies. How can I do this?

enter image description here

For example in this picture there is two layers that I didn't add but they are showing in the graph

vgg16_model = keras.applications.vgg16.VGG16()


cnnModel = keras.models.Sequential()

for layer in vgg16_model.layers[0:13]:
    cnnModel.add(layer)

for layer in vgg16_model.layers[14:16]:
    cnnModel.add(layer)

for layer in vgg16_model.layers[17:21]:
    cnnModel.add(layer)

cnnModel.add(keras.layers.Dense(2048, name="compress_1"))
cnnModel.add(keras.layers.Dense(1024, name="compress_2"))
cnnModel.add(keras.layers.Dense(512, name="compress_3"))


for layer in cnnModel.layers[0:4]:
    layer.trainable = False

del vgg16_model

2) the second problem occurs when using cnnModel.pop(). In fact I have add all the layers but I do a pop to the layer I don't want before adding the next one this is the error I get.

Layer block4_conv2 has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `get_output_at(node_index)` instead.

And this is the code I am using:

for layer in vgg16_model.layers[0:14]:
    cnnModel.add(layer)

cnnModel.pop()

for layer in vgg16_model.layers[14:17]:
    cnnModel.add(layer)

cnnModel.pop()

for layer in vgg16_model.layers[17:21]:
    cnnModel.add(layer)

cnnModel.pop() is working the problem only occurs when trying to add the next layer.

Thank you for your help.

Upvotes: 6

Views: 5115

Answers (1)

ebeneditos
ebeneditos

Reputation: 2612

You can try using Model instead of Sequential, like:

vgg16_model = keras.applications.vgg16.VGG16()

drop_layers = [13, 16]

input_layer = x = vgg16_model.input

for i, layer in enumerate(vgg16_model.layers[1:], 1):
    if i not in drop_layers:
        x = layer(x)

x = keras.layers.Dense(2048, name="compress_1")(x)
x = keras.layers.Dense(1024, name="compress_2")(x)
x = keras.layers.Dense(512, name="compress_3")(x)

cnnModel = keras.models.Model(inputs = input_layer, outputs = x)

for layer in cnnModel.layers[0:4]:
    layer.trainable = False

del vgg16_model

Upvotes: 3

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