Change the input size in Keras

Question

I have trained a fully convolutional neural network with Keras. I have used the Functional API and have defined the input layer as Input(shape=(128,128,3)), corresponding to the size of the images in my training set.

However, I want to use the trained model on images of variable sizes (which should be ok because the network is fully convolutional). To do this, I need to change my input layer to Input(shape=(None,None,3)). The obvious way to solve the problem would have been to train my model directly with an input shape of (None,None,3) but I use a custom loss function where I need to specify the size of my training images.

I have tried to define a new input layer and assign it to my model like this :

from keras.engine import InputLayer

input_layer = InputLayer(input_shape=(None, None, 3), name="input")
model.layers[0] = input_layer

This actually changes the size of the input layers accordingly but the following layers still expect (128,128,filters) inputs.

Is there a way to change all of the inputs values at once ?

Answer 1

Create a new model, exactly the same, except for new input shape; and tranfer weights:

newModel.set_weights(oldModel.get_weights())

If anything goes wrong, then it might not be fully convolutional (ex: contains a Flatten layer).