I am interested in updating existing layer parameters in Keras (not removing a layer and inserting a new one instead, rather just modifying existing parameters).
I will give an example of a function I'm writing:
def add_filters(self, model):
conv_indices = [i for i, layer in enumerate(model.layers) if 'convolution' in layer.get_config()['name']]
random_conv_index = random.randint(0, len(conv_indices)-1)
factor = 2
conv_layer = model.layers[random_conv_index]
conv_layer.filters = conv_layer.filters * factor
print('new conv layer filters after transform is:', conv_layer.filters)
print('just to make sure, its:', model.layers[random_conv_index].filters)
return model
so what's basically happening here is me taking a random convolutional layer from my network (all my conv layers have 'convolution' in their name) and trying to double the filters. As far as I know this shouldn't cause any 'compilation issues' with input/output size compatibility in any case.
The thing is, my model doesn't change at all. The 2 print-outs I added in the end print the correct number (double the previous amount of filters). But when I compile the model and print model.summary(), I still see the previous filter amount.
BTW, I'm not constricted to Keras. If anyone has an idea how to pull this off with PyTorch for example I'll also buy it :D
Well, if you would like to create the architecture of a new model based on an existing model, though with some modifications, you can use to_json and model_from_json() functions. Here is an example:
model = Sequential()
model.add(Conv2D(10, (3,3), input_shape=(100,100,3)))
model.add(Conv2D(40, (3,3)))
model.summary()
Model summary:
Layer (type) Output Shape Param #
=================================================================
conv2d_12 (Conv2D) (None, 98, 98, 10) 280
_________________________________________________________________
conv2d_13 (Conv2D) (None, 96, 96, 40) 3640
=================================================================
Total params: 3,920
Trainable params: 3,920
Non-trainable params: 0
_________________________________________________________________
Now we modify the number of filters of the first layer and create a new model based on the modified architecture:
from keras.models import model_from_json
model.layers[0].filters *= 2
new_model = model_from_json(model.to_json())
new_model.summary()
New model summary:
Layer (type) Output Shape Param #
=================================================================
conv2d_12 (Conv2D) (None, 98, 98, 20) 560
_________________________________________________________________
conv2d_13 (Conv2D) (None, 96, 96, 40) 7240
=================================================================
Total params: 7,800
Trainable params: 7,800
Non-trainable params: 0
_________________________________________________________________
You can also modify the output of model.to_json() directly without modifying the model instance.
You can easily use get_weights() method to get the current weights of the convolution layer. It would return a list of two numpy arrays. The first one corresponds to filter weights and the second one corresponds to bias parameters. Then you can use set_weights() method to set the new weights:
conv_layer = model.layers[random_conv_index]
weights = conv_layer.get_weights()
weights[0] *= factor # multiply filter weights by `factor`
conv_layer.set_weights(weights)
As a side note, the filters attribute of a convolution layer which you have used in your code corresponds to the number of filters in this layer and not their weights.
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