CODEWITHSUNDEEP
tensorflow
yazfield
yazfield

Reputation: 1283

Variable size Convolutional Neural Network Input and Fixed output

I am trying to make a CNN model that takes variable size input (sentence matrix) and produce a fixed size output for a subsequent fully connected layer (similar to this paper).

I am trying to implement a dynamic kernel size for a max pooling layer so I need the shape of the input at runtime to achieve this.

input = tf.placeholder(tf.float32)
# convolution layer here .... 

tf.nn.max_pool(convolution_output, ksize=[1, s, 1, 1],
                      strides=[1, 1, 1, 1], padding='VALID')

s in ksize=[1, s, 1, 1] should be inferred from the input shape.

However, I can't find a way to do it with Tensorflow.

Anyone knows a way to do it?

Upvotes: 3

Views: 4790

Answers (2)

AhlyM
AhlyM

Reputation: 520

I know it's an old thread, but for people who are looking for a solution. It has been implemented in tensorflow 1.4.0

tf.nn.max_pool() now takes 1d tensor as an input as opposed to a list of ints in the older versions. So you can use a placeholder as the argument of ksize.

Upvotes: 4

yazfield
yazfield

Reputation: 1283

I didn't find a way to do it in Tensorflow and had to reimplement my model in theano and use a different solution: Dynamic KMax Pooling.

Upvotes: 2

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