def main(args):
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = args.learning_rate
learner_config.num_classes = args.num_classes
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = args.l2 / args.examples_per_layer
learner_config.constraints.max_tree_depth = args.depth
learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.multi_class_strategy = learner_pb2.LearnerConfig.DIAGONAL_HESSIAN
head = custom_loss_head.CustomLossHead(
loss_fn=functools.partial(losses.per_example_maxent_loss, num_classes=args.num_classes),
link_fn=tf.identity,
logit_dimension=args.num_classes,
metrics_fn=_multiclass_metrics)
estimator = GradientBoostedDecisionTreeEstimator(
learner_config=learner_config,
head=head,
examples_per_layer=args.examples_per_layer,
num_trees=args.num_trees,
center_bias=False)
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_train = (X_train/255.).reshape(-1, 28*28).astype(np.float32)
X_test = (X_test/255.).reshape(-1, 28*28).astype(np.float32)
y_train = y_train.astype(np.int32)
y_test = y_test.astype(np.int32)
estimator.fit(input_fn=tf.estimator.inputs.numpy_input_fn(
x={'_':X_train}, y=y_train, batch_size=args.batch_size, num_epochs=10, shuffle=True))
estimator.evaluate(input_fn=tf.estimator.inputs.numpy_input_fn(
x={'_':X_test}, y=y_test, batch_size=args.batch_size, shuffle=False))
def _quantile_regression_head(quantile):
# Use quantile regression.
head = custom_loss_head.CustomLossHead(
loss_fn=functools.partial(
losses.per_example_quantile_regression_loss, quantile=quantile),
link_fn=array_ops.identity,
logit_dimension=label_dimension)
return head
def _get_tfbt(output_dir):
"""Configures TF Boosted Trees estimator based on flags."""
learner_config = learner_pb2.LearnerConfig()
num_classes = 10
learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate
learner_config.num_classes = num_classes
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer
learner_config.constraints.max_tree_depth = FLAGS.depth
growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
# Use Cross Entropy loss (the impl in losses is twice differentiable).
loss_fn = functools.partial(losses.per_example_maxent_loss,
num_classes=num_classes)
logit_dim = num_classes
learner_config.multi_class_strategy = (
learner_pb2.LearnerConfig.DIAGONAL_HESSIAN)
# Since we use custom head, we need to tell how accuracy is calculated.
def _multiclass_metrics(predictions, labels, weights):
"""Prepares eval metrics for multiclass eval."""
metrics = dict()
logits = predictions["scores"]
classes = math_ops.argmax(logits, 1)
metrics["accuracy"] = metrics_lib.streaming_accuracy(
classes, labels, weights)
return metrics
metrics_fn = _multiclass_metrics
# Use custom loss head so we can provide our loss (cross entropy for
# multiclass).
head = custom_loss_head.CustomLossHead(loss_fn=loss_fn,
link_fn=tf.identity,
logit_dimension=logit_dim,
metrics_fn=metrics_fn)
# Create a TF Boosted trees estimator that can take in custom loss.
estimator = GradientBoostedDecisionTreeEstimator(
learner_config=learner_config,
head=head,
examples_per_layer=FLAGS.examples_per_layer,
model_dir=output_dir,
num_trees=FLAGS.num_trees,
center_bias=False,
config=run_config)
return estimator
parser.add_argument("--num_trees", type=int, default=100)
parser.add_argument("--num_classes", type=int, default=10)
args = parser.parse_args()
learner_config = learner_pb2.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = args.learning_rate
learner_config.num_classes = args.num_classes
learner_config.regularization.l1 = 0.0
learner_config.regularization.l2 = args.l2 / args.examples_per_layer
learner_config.constraints.max_tree_depth = args.depth
learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER
learner_config.multi_class_strategy = learner_pb2.LearnerConfig.DIAGONAL_HESSIAN
head = custom_loss_head.CustomLossHead(loss_fn=functools.partial(
losses.per_example_maxent_loss, num_classes=args.num_classes),
link_fn=tf.identity,
logit_dimension=args.num_classes,
metrics_fn=_multiclass_metrics)
estimator = GradientBoostedDecisionTreeEstimator(
learner_config=learner_config,
head=head,
examples_per_layer=args.examples_per_layer,
num_trees=args.num_trees,
center_bias=False)
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_train = (X_train / 255.).reshape(-1, 28 * 28).astype(np.float32)
X_test = (X_test / 255.).reshape(-1, 28 * 28).astype(np.float32)
y_train = y_train.astype(np.int32)
y_test = y_test.astype(np.int32)