If you remember my earlier post about switching Keras between TensorFlow and Theano backends, you would have seen that we switched the image_dim_ordering parameter also when switching the backend. For TensorFlow, image_dim_ordering should be "tf", while for Theano, it should be "th".
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| The keras.json file contains the Keras configuration options |
So, what is this parameter, and where does it affect?
It has to do with how each of the backends treat the data dimensions when working with multi-dimensional convolution layers (such as Convolution2D, Convolution3D, UpSampling2D, Copping2D, … and any other 2D or 3D layer). Specifically, it defines where the 'channels' dimension is in the input data.
Both TensorFlow and Theano expects a 4 dimensional tensor as input. But where TensorFlow expects the 'channels' dimension as the last dimension (index 3, where the first is index 0) of the tensor – i.e. tensor with shape (samples, rows, cols, channels) – Theano will expect 'channels' at the second dimension (index 1) – i.e. tensor with shape (samples, channels, rows, cols). The outputs of the convolutional layers will also follow this pattern.
So, the image_dim_ordering parameter, once set in ~/.keras/keras.json, will tell Keras which dimension ordering to use in its convolutional layers.
However, if you like to override the dimension ordering programmatically, you do it by using the dim_ordering parameter when initializing a convolutional layer:
The dim_ordering parameter is available in all the multi-dimensional convolution layers.
Related posts:
image_data_format vs. image_dim_ordering in Keras v2
Related links:
https://keras.io/layers/convolutional/#convolution2d
model = Sequential()
model.add(Convolution2D(64, 3, 3, border_mode='same', input_shape=(3, 256, 256), dim_ordering='th'))
The dim_ordering parameter is available in all the multi-dimensional convolution layers.
Related posts:
image_data_format vs. image_dim_ordering in Keras v2
Related links:
https://keras.io/layers/convolutional/#convolution2d
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"TensorFlow expects the 'channels' dimension to be at index 4 of the tensor – i.e. tensor with shape (samples, rows, cols, channels) – Theano will expect 'channels' at index 1 – i.e. tensor with shape (samples, channels, rows, cols)"
ReplyDeleteAt the risk of sounding pedantic, it seems to me that you're using two different indexing techniques (base 1 and base 0). I believe you mean to say one of the following:
a) TF uses index 4, and Theano uses index 2
b) TF uses index 3, and Theano uses index 1
Yes, you're right. It sounds confusing.
DeleteI have now updated it to (hopefully) make it clear.
Thanks,
Nice!!
ReplyDelete