Using a custom filter in convolution layer for tensorflow

[ivans....@gmail.com]

I've been learning Tensorflow from a variety of tutorials and am wondering if it's possible to define a custom filter for convolution nets to use. For example, if I know there is meaningful structure in the features, such that every other feature is related, I want to define a filter that looks like [0 1 0  1].

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)

All the examples I've seen so far use:

tf.random_normal

or

tf.truncated_normal

for the filter argument. Can I and does it make sense for me to put [0 1 0 1] in the filter argument instead?

Many thanks!

[Frédéric Branchaud-Charron]

Hi Ivan!

Your filter doesn't make sense since it's a 1d Tensor. But, if you have a filter with a shape that could fit in the conv2d layer (for exemple shape could be [3,3,64]) you could totally use it.

Generate your numpy array : w = np.array[,,,]

Create a variable 

kernel = tf.Variable(w.astype(np.float32))

and create your layer

conv = tf.nn.conv2d(input.kernel,...)

Hope this help!