Loading a trained model, popping the last two layers, and then saving it

So I’m working with this architecture for a facial point network:

n_kpts = 68 # number of keypoints

input_shape = (1,100,100)

input_1 = Input(shape=input_shape)

conv_1 = Conv2D(34, kernel_size=(9,9),
				activation='tanh',
				input_shape=input_shape,
				padding='same',
				data_format='channels_first')(input_1)

conv_2 = Conv2D(34, kernel_size=(9,9),
				activation='tanh',
				padding='same',
				data_format='channels_first')(conv_1)

conv_3 = Conv2D(34, kernel_size=(9,9),
				activation='tanh',
				padding='same',
				data_format='channels_first')(conv_2)

conv_4 = Conv2D(34, kernel_size=(9,9),
				activation='tanh',
				padding='same',
				data_format='channels_first')(conv_3)

conv_5 = Conv2D(34, kernel_size=(9,9),
				activation='tanh',
				padding='same',
				data_format='channels_first')(conv_4)

softargmax = spatial_softArgmax(68)(conv_4)

reshape = Reshape((68,2))(softargmax)

model = Model(inputs=input_1, outputs=reshape)

I need to get rid of the reshape and softargmax (it’s a custom layer) - and just save the model as the input and conv_1 - conv_5; I want the output to just be the output of that last convolutional layer. I have a model that’s trained as an h5 with all of these layers, but i run into some trouble when trying to pop and resave - here’s the script I wrote for that:

def get_weights_without_softargmax(fname):
	model = load_model(fname, custom_objects={'spatial_softArgmax':spatial_softArgmax})
	
	model.summary()
	model.layers.pop() # reshape layer
	model.layers.pop() # spatial softargmax

	model.summary()
	
	model.save("no_softargmax_" + str(fname))

The first model.summary returns a summary of the whole network;

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 1, 100, 100)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 34, 100, 100)      2788      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 100, 100)      93670     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 34, 100, 100)      93670     
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 34, 100, 100)      93670     
_________________________________________________________________
spatial_soft_argmax_1 (spati (None, 68)                0         
_________________________________________________________________
reshape_1 (Reshape)          (None, 34, 2)             0         
=================================================================
Total params: 283,798
Trainable params: 283,798
Non-trainable params: 0

and the second one returns the now popped network

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 1, 100, 100)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 34, 100, 100)      2788      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 100, 100)      93670     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 34, 100, 100)      93670     
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 34, 100, 100)      93670     
=================================================================
Total params: 283,798
Trainable params: 283,798
Non-trainable params: 0

but when I try to do model.save() - i get this error:

Traceback (most recent call last):
  File "weight_chopper.py", line 18, in <module>
    get_weights_without_softargmax("34pts_94percent.h5")
  File "weight_chopper.py", line 16, in get_weights_without_softargmax
    model.save("no_softargmax_" + str(fname))
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2553, in save
    save_model(self, filepath, overwrite, include_optimizer)
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 107, in save_model
    'config': model.get_config()
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2390, in get_config
    new_node_index = node_conversion_map[node_key]
KeyError: u'reshape_1_ib-0'

Note how it’s referencing the old reshape layer? When I defined the model; I said model = Model(inputs=input_1, outputs=reshape) - so does it still think that the model has that reshape output? How can I convince it otherwise? I’ve tried doing another Model(inputs=..., outputs=...) type command; but there aren’t any appropriate values to plug in for the inputs and outputs!

How can I get the model to save (preferably as a compiled model) with just the convolutional layers?

[fchollet]

there aren’t any values i can give for inputs= and outputs= because I haven’t defined the model in that script…

You can retrieve these from your model:

new_model = Model(model.inputs, model.layers[-3].output)  # assuming you want the 3rd layer from the last

[fchollet]

Wait, I got confused. I thought you were using the pop method of a Sequential model, but that’s not what you are doing. Please post your full code.

Note that pop is not possible with the functional API, it’s only implemented for Sequential. If you want to drop some layers in the functional API, you’d do:

new_model = Model(inputs=input_1, outputs=conv_5)

In you case.