Tensorflow's tf.nn.conv2d_transpose parameters

Question

Recently I have been trying to understand tensorflow's tf.nn.conv2d_transpose , however I have a hard time understanding the input parameters for it. It's defined as:

tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME')

For example, let's say I have a image of size [batch_size, 7, 7, 128] and want to transform it to [batch_size, 14, 14, 64] . Then output_shape=[batch_size, 14, 14, 64] , strides=[2,2] , however I can't figure out how to get the shape of the filter. Any thoughts?

Furthermore how does padding="SAME" works for conv2d_transpose ? Is it applied to the output image or the input?

Answer 1

For the first question on filter shapes, I'd use the object oriented version tf.layers.Conv2DTranspose and look at the kernel property to figure out the filter shapes:

>>> import tensorflow as tf
>>> l = tf.layers.Conv2DTranspose(filters=64, kernel_size=1, padding='SAME', strides=[2, 2])
>>> l(tf.ones([12, 7, 7, 128]))
<tf.Tensor 'conv2d_transpose/BiasAdd:0' shape=(12, 14, 14, 64) dtype=float32>
>>> l.kernel
<tf.Variable 'conv2d_transpose/kernel:0' shape=(1, 1, 64, 128) dtype=float32_ref>
>>> 

On second padding question, conv2d_transpose computes the gradient of conv2d . Since conv2d pads its inputs, conv2d_transpose needs to pad its output to fit the gradient.