input_shape在keras中的2D卷积层

Question

In the Keras Documentation for Convolution2D the input_shape a 128x128 RGB pictures is given by input_shape=(3, 128, 128) , thus I figured the first component should be the number of planes (or feature layers).

If I run the following code:

model = Sequential()
model.add(Convolution2D(4, 5,5, border_mode='same', input_shape=(3, 19, 19), activation='relu'))
print(model.output_shape)

I get an output_shape of (None, 3, 19, 4) , whereas in my understanding this should be (None, 4, 19, 19) with 4 the number of filters.

Is this an error in the example from the keras documentation or am I missing something?

(I am trying to recreate a part of AlphaGo so the 19x19 is the board size which would correspond to the images size. )

Answer 1

You are using the Theano dimension ordering (channels, rows, cols) as input but your Keras seems to use the Tensorflow one which is (rows, cols, channels) .

So either you can switch to the Theano dimension ordering, directly in your code with :

import keras.backend as K K.set_image_dim_ordering('th')

Or editing the keras.json file in (usually in ~\\.keras ) and switching

"image_dim_ordering": "tf" to "image_dim_ordering": "th"

Or you can keep the Tensorflow dimension ordering and switch your input_shape to (19,19,3)

Answer 2

Yes it should be (None, 4, 19, 19). There is something called dim_ordering in keras that decides in which index should one place the number of input channels . Check the documentation of "dim_ordering" parameter in the documentation . Mine is set to 'tf'.

So; just change the input shape to (19, 19, 3) like so

model.add(Convolution2D(4, 5,5, border_mode='same', input_shape=(19, 19,3), activation='relu'))

Then check the output shape.

You can also modify the dim_ordering in the file usually at ~/.keras/keras.json to your liking