Variable shaped input to convolutional layers not working anymore

After some months I updated keras (I was using keras 1.0.5 i think) and now my model can no longer work with input images with different shapes.

Take this small code as an example:

import numpy as np
from keras.models import Sequential
from keras.layers import Convolution2D
m = Sequential()
m.add(Convolution2D(1,3,3, input_shape=(1,10,10)))
m.compile(loss="mae", optimizer="sgd")
c = m.predict(np.random.rand(1,1,10,10).astype('float32'))
c = m.predict(np.random.rand(1,1,20,20).astype('float32'))
>>>Exception: Error when checking : expected convolution2d_input_1 to have shape (None, 1, 10, 10) but got array with shape (1, 1, 20, 20)

I managed to find out that a “image_dim_ordering”:“th” had to be added to the keras.json so that It could work with an input size matching the defined one, however I am unable to find any way to correct the problem when actual input size is different. This used to work in previous versions, generating an output of the correct shape.

[fchollet]

It has nothing to do with any new syntax. In your own code, you should use

m.add(Convolution2D(1,3,3, input_shape=(1, None, None)))

instead of m.add(Convolution2D(1,3,3, input_shape=(1, 10, 10)))

Your code could never have worked on variable-size inputs in the first place…

[carlthome]

If you want variable sizes use None, like:

import numpy as np
from keras.models import Model
from keras.layers import Input, Conv2D

i = Input((None, None, 1))
o = Conv2D(1, 3, 3)(i)
model = Model(i, o)
model.compile('sgd', 'mse')

print(model.predict(np.random.rand(1, 10, 10, 1)))
print(model.predict(np.random.rand(1, 32, 16, 1)))

Note that I’m assuming image_dim_ordering: 'tf', meaning that the image channel is the last dimension.