def _reset_batch_norm(self):
for layer in self.batch_norm_layers:
# to get properly initialized moving mean and moving variance weights
# we initialize a new batch norm layer from the config of the existing
# layer, build that layer, retrieve its reinitialized moving mean and
# moving var weights and then delete the layer
bn_config = layer.get_config()
new_batch_norm = BatchNormalization(**bn_config)
new_batch_norm.build(layer.input_shape)
new_moving_mean, new_moving_var = new_batch_norm.get_weights()[-2:]
# get rid of the new_batch_norm layer
del new_batch_norm
# get the trained gamma and beta from the current batch norm layer
trained_weights = layer.get_weights()
new_weights = []
# get gamma if exists
if bn_config['scale']:
new_weights.append(trained_weights.pop(0))
# get beta if exists
if bn_config['center']:
new_weights.append(trained_weights.pop(0))
new_weights += [new_moving_mean, new_moving_var]
# set weights to trained gamma and beta, reinitialized mean and variance
layer.set_weights(new_weights)
def _reset_batch_norm(self):
for layer in self.batch_norm_layers:
# to get properly initialized moving mean and moving variance weights
# we initialize a new batch norm layer from the config of the existing
# layer, build that layer, retrieve its reinitialized moving mean and
# moving var weights and then delete the layer
new_batch_norm = BatchNormalization(**layer.get_config())
new_batch_norm.build(layer.input_shape)
_, _, new_moving_mean, new_moving_var = new_batch_norm.get_weights(
)
# get rid of the new_batch_norm layer
del new_batch_norm
# get the trained gamma and beta from the current batch norm layer
trained_gamma, trained_beta, _, _ = layer.get_weights()
# set weights to trained gamma and beta, reinitialized mean and variance
layer.set_weights(
[trained_gamma, trained_beta, new_moving_mean, new_moving_var])
