def test_return_l2_regularizer_weights(self): conv_hyperparams_text_proto = """ regularizer { l2_regularizer { weight: 0.42 } } initializer { truncated_normal_initializer { } } """ conv_hyperparams_proto = hyperparams_pb2.Hyperparams() text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto) scope_fn = hyperparams_builder.build(conv_hyperparams_proto, is_training=True) scope = scope_fn() conv_scope_arguments = scope[_get_scope_key(slim.conv2d)] regularizer = conv_scope_arguments['weights_regularizer'] weights = np.array([1., -1, 4., 2.]) with self.test_session() as sess: result = sess.run(regularizer(tf.constant(weights))) self.assertAllClose(np.power(weights, 2).sum() / 2.0 * 0.42, result)
def _build_arg_scope_with_hyperparams(self, hyperparams_text_proto, is_training): hyperparams = hyperparams_pb2.Hyperparams() text_format.Merge(hyperparams_text_proto, hyperparams) return hyperparams_builder.build(hyperparams, is_training=is_training)
def _build_keras_layer_hyperparams(self, hyperparams_text_proto): hyperparams = hyperparams_pb2.Hyperparams() text_format.Merge(hyperparams_text_proto, hyperparams) return hyperparams_builder.KerasLayerHyperparams(hyperparams)