tensorflow_probability/python/distributions/vector_diffeomixture.py
def _expand_base_distribution_mean(self):
    """Ensures `self.distribution.mean()` has `[batch, event]` shape."""
    single_draw_shape = concat_vectors(self.batch_shape_tensor(),
                                       self.event_shape_tensor())
    m = tf.reshape(
        self.distribution.mean(),  # A scalar.
        shape=tf.ones_like(single_draw_shape, dtype=tf.int32))
    m = tf.tile(m, multiples=single_draw_shape)
    tensorshape_util.set_shape(
        m, tensorshape_util.concatenate(self.batch_shape, self.event_shape))
    return m
def _expand_base_distribution_mean(self):
    """Ensures `self.distribution.mean()` has `[batch, event]` shape."""
    single_draw_shape = concat_vectors(self.batch_shape_tensor(),
                                       self.event_shape_tensor())
    m = tf.reshape(
        self.distribution.mean(),  # A scalar.
        shape=tf.ones_like(single_draw_shape, dtype=tf.int32))
    m = tf.tile(m, multiples=single_draw_shape)
    tensorshape_util.set_shape(
        m, tensorshape_util.concatenate(self.batch_shape, self.event_shape))
    return m
def _expand_base_distribution_mean ( self ) : single_draw_shape = concat_vectors ( self . batch_shape_tensor ( ) , self . event_shape_tensor ( ) ) m = tf . reshape ( self . distribution . mean ( ) , # A scalar. shape = tf . ones_like ( single_draw_shape , dtype = tf . int32 ) ) m = tf . tile ( m , multiples = single_draw_shape ) tensorshape_util . set_shape ( m , tensorshape_util . concatenate ( self . batch_shape , self . event_shape ) ) return m
Ensures `self.distribution.mean()` has `[batch, event]` shape.
Ensures self . distribution . mean () has [ batch event ] shape .