shapeless placeholder

examples/GAN/InfoGAN-mnist.py

@@ -32,7 +32,6 @@ class GaussianWithUniformSample(GaussianDistribution):
 
 
 class Model(GANModelDesc):
-
     def _get_input_vars(self):
         return [InputVar(tf.float32, (None, 28, 28), 'input')]
 
@@ -75,10 +74,10 @@ class Model(GANModelDesc):
         prior = tf.constant([0.1] * 10 + [0, 0], tf.float32, [12], name='prior')
         batch_prior = tf.tile(tf.expand_dims(prior, 0), [BATCH, 1], name='batch_prior')
 
-        # sample the latent code zc:
-        zc = symbf.remove_shape(self.factors.sample(BATCH, prior), 0, name='z_code')
-
-        z_noise = symbf.remove_shape(
+        # sample the latent code:
+        zc = symbf.shapeless_placeholder(
+            self.factors.sample(BATCH, prior), 0, name='z_code')
+        z_noise = symbf.shapeless_placeholder(
             tf.random_uniform([BATCH, NOISE_DIM], -1, 1), 0, name='z_noise')
         z = tf.concat_v2([zc, z_noise], 1, name='z')
 

tensorpack/tfutils/symbolic_functions.py

@@ -363,21 +363,24 @@ def soft_triplet_loss(anchor, positive, negative, extra=True):
             return loss
 
 
-# TODO not a good name.
-def remove_shape(x, axis, name):
+def shapeless_placeholder(x, axis, name):
     """
-    Make the static shape of a tensor less specific, by
-    using :func:`tf.placeholder_with_default`.
+    Make the static shape of a tensor less specific.
+
+    If you want to feed to a tensor, the shape of the feed value must match
+    the tensor's static shape. This function creates a placeholder which
+    defaults to x if not fed, but has a less specific static shape.
     See `tensorflow#5680
     <https://github.com/tensorflow/tensorflow/issues/5680>`_.
 
     Args:
         x: a tensor
-        axis(int or list of ints): the axes to reset shape to None.
+        axis(int or list of ints): these axes of ``x.get_shape()`` will become
+            None in the output.
         name(str): name of the output tensor
 
     Returns:
-        a tensor equal to x, but shape information is partially cleared
+        a tensor equal to x, but shape information is partially cleared.
     """
     shp = x.get_shape().as_list()
     if not isinstance(axis, list):