def _add(converter, node: Any, inputs: List[str]) -> Any:
a = converter.outputs[inputs[0]]
b = converter.outputs[inputs[1]]
if isinstance(a, tf.NodeDef):
a_out = _nodef_to_public_pond(converter, a)
else:
a_out = a
if isinstance(b, tf.NodeDef):
b_out = _nodef_to_public_pond(converter, b)
else:
b_out = b
return tfe.add(a_out, b_out)
def set_weights(self, weights, sess=None):
"""Update layer weights from numpy array or Public Tensors
including denom.
Arguments:
weights: A list of Numpy arrays with shapes and types
matching the output of layer.get_weights() or a list
of private variables
sess: tfe session"""
if not sess:
sess = KE.get_session()
if isinstance(weights[0], np.ndarray):
for i, w in enumerate(self.weights):
if isinstance(w, PondPublicTensor):
shape = w.shape.as_list()
tfe_weights_pl = tfe.define_public_placeholder(shape)
fd = tfe_weights_pl.feed(weights[i].reshape(shape))
sess.run(tfe.assign(w, tfe_weights_pl), feed_dict=fd)
else:
raise TypeError(
(
"Don't know how to handle weights "
"of type {}. Batchnorm expects public tensors"
"as weights"
).format(type(w))
)
elif isinstance(weights[0], PondPublicTensor):
for i, w in enumerate(self.weights):
shape = w.shape.as_list()
sess.run(tfe.assign(w, weights[i].reshape(shape)))
# Compute denom on public tensors before being lifted to private tensor
denomtemp = tfe.reciprocal(
tfe.sqrt(tfe.add(self.moving_variance, self.epsilon))
)
# Update denom as well when moving variance gets updated
sess.run(tfe.assign(self.denom, denomtemp))