text: Title: snt.Conv2DTranspose and tf.layers.conv2d_transpose yield different results
  Body: The sonnet documentation states the about the Conv2DTranspose:
  
  > This acts as a light wrapper around the TensorFlow op `tf.nn.conv2d_transpose`
  > abstracting away variable creation and sharing.
  
  ```tf.layers.conv2d_transpose(inputs, filters=64, kernel_size=1, strides=5, padding='valid')```
  --> yields shape `(?, 5, 5, 64)`
  
  `snt.Conv2DTranspose(64, 1, 5, padding='VALID')(inputs)`
  --> yields shape `(?, 1, 1, 64)`
  
  for `inputs` shape `(?, 1, 1, 218)`
  
  Changing the semantics of the called parameters from `input_channels` (tensorflow) to `output_channel` (Sonnet) in the API to me does not state not a 'light' wrapper and either the wording or the code should therefore be adjusted in Sonnet.
  