1 files changed, 27 insertions, 4 deletions
diff --git a/inversion/live.py b/inversion/live.py
index cf7f761..ad4b80f 100644
--- a/inversion/live.py
+++ b/inversion/live.py
@@ -42,6 +42,24 @@ BATCH_SIZE = 1
 Z_DIM = input_info['z'].get_shape().as_list()[1]
 N_CLASS = input_info['y'].get_shape().as_list()[1]
 
+# --------------------------
+# Initializers
+# --------------------------
+
+def label_sampler(shape=[BATCH_SIZE, N_CLASS]):
+  num_classes = 2
+  label = np.zeros(shape)
+  for i in range(shape[0]):
+    for _ in range(random.randint(1, shape[1])):
+      j = random.randint(0, shape[1]-1)
+      label[i, j] = random.random()
+    label[i] /= label[i].sum()
+  return label
+
+# --------------------------
+# More complex ops
+# --------------------------
+
 def sin(opts, key, shape):
   noise = lerp(opts, key + '_noise', shape)
   scale = InterpolatorParam(name=key + '_scale')
@@ -61,17 +79,22 @@ def lerp(opts, key, shape):
   return out
 
 class InterpolatorParam:
-  def __init__(self, name, dtype=tf.float32, shape=(), value=None):
+  def __init__(self, name, dtype=tf.float32, shape=(), value=None, type="noise"):
     self.scalar = shape == ()
     self.shape = shape
+    self.type = type
     self.value = value or np.zeros(shape)
     self.variable = tf.placeholder(dtype=dtype, shape=shape)
 
   def assign(self, value):
     self.value = value
 
-  def randomize(self):
-    return self.assign(np.random.normal(size=self.shape))
+  def randomize(self, num_classes):
+    if self.type == 'noise':
+      val = np.random.normal(size=self.shape)
+    elif self.type == 'label':
+      val = label_sampler(shape=self.shape)
+    self.assign(val)
 
 class Interpolator:
   def __init__(self):
@@ -108,7 +131,7 @@ class Interpolator:
     return opt
 
   def set_value(self, key, value):
-    return self.opts[key].assign(value)
+    self.opts[key].assign(value)
 
   def on_step(self, i, sess):
     gen_images = sess.run(self.gen_img, feed_dict=self.get_feed_dict())