def test_head_weights_wrong_size(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') with self.assertRaisesRegexp( ValueError, r'heads and head_weights must have the same size\. ' r'Given len\(heads\): 2. Given len\(head_weights\): 1\.'): multi_head_lib.multi_head([head1, head2], head_weights=[1.])
def test_train_create_loss_logits_tensor_wrong_shape(self): """Tests create_loss with logits Tensor of the wrong shape.""" weights1 = np.array([[1.], [2.]], dtype=np.float32) weights2 = np.array([[2.], [3.]]) head1 = head_lib.multi_label_head(n_classes=2, name='head1', weight_column='weights1') head2 = head_lib.multi_label_head(n_classes=3, name='head2', weight_column='weights2') multi_head = multi_head_lib.multi_head([head1, head2], head_weights=[1., 2.]) # logits tensor is 2x6 instead of 2x5 logits = np.array([[-10., 10., 20., -20., 20., 70.], [-15., 10., -30., 20., -20., 80.]], dtype=np.float32) labels = { 'head1': np.array([[1, 0], [1, 1]], dtype=np.int64), 'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64), } with self.assertRaisesRegexp(ValueError, r'Could not split logits'): multi_head.create_loss(features={ 'x': np.array(((42, ), ), dtype=np.int32), 'weights1': weights1, 'weights2': weights2 }, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)
def test_train_create_loss_logits_tensor_multi_dim(self): """Tests create_loss with multi-dimensional logits of shape [2, 2, 5].""" head1 = head_lib.regression_head(label_dimension=2, name='head1') head2 = head_lib.regression_head(label_dimension=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) logits = np.array( [[[-1., 1., 2., -2., 2.], [-1., 1., 2., -2., 2.]], [[-1.5, 1.5, -2., 2., -2.], [-1.5, 1.5, -2., 2., -2.]]], dtype=np.float32) labels = { 'head1': np.array([[[1., 0.], [1., 0.]], [[1.5, 1.5], [1.5, 1.5]]], dtype=np.float32), 'head2': np.array([[[0., 1., 0.], [0., 1., 0.]], [[2., 2., 0.], [2., 2., 0.]]], dtype=np.float32), } # Loss for the first head: # loss1 = ((1+1)^2 + (0-1)^2 + (1+1)^2 + (0-1)^2 + # (1.5+1.5)^2 + (1.5-1.5)^2 + (1.5+1.5)^2 + (1.5-1.5)^2) / 8 # = 3.5 # Loss for the second head: # loss2 = ((0-2)^2 + (1+2)^2 + (0-2)^2 + (0-2)^2 + (1+2)^2 + (0-2)^2 + # (2+2)^2 + (2-2)^2 + (0+2)^2 + (2+2)^2 + (2-2)^2 + (0+2)^2) / 12 # = 6.167 expected_training_loss = 3.5 + 6.167 training_loss = multi_head.create_loss( features={}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)[0] tol = 1e-3 with self.cached_session(): self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=tol, atol=tol)
def test_train_one_head(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') multi_head = multi_head_lib.multi_head([head1]) logits = { 'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32) } labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)} # For large logits, sigmoid cross entropy loss is approximated as: # loss = labels * (logits < 0) * (-logits) + # (1 - labels) * (logits > 0) * logits => # expected_unweighted_loss = [[10., 10.], [15., 0.]] # loss = ( (10 + 10) / 2 + (15 + 0) / 2 ) / 2 = 8.75 expected_loss = 8.75 expected_train_result = 'my_train_op' def _train_op_fn(loss): return string_ops.string_join([ constant_op.constant(expected_train_result), string_ops.as_string(loss, precision=3) ]) spec = multi_head.create_estimator_spec( features={'x': np.array(((42, ), ), dtype=np.int32)}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels, train_op_fn=_train_op_fn) self.assertIsNotNone(spec.loss) self.assertEqual({}, spec.eval_metric_ops) self.assertIsNotNone(spec.train_op) self.assertIsNone(spec.export_outputs) _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. tol = 1e-3 with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) predictions = sess.run(spec.predictions) self.assertAllClose( logits['head1'], predictions[('head1', prediction_keys.PredictionKeys.LOGITS)]) self.assertAllClose( _sigmoid(logits['head1']), predictions[('head1', prediction_keys.PredictionKeys.PROBABILITIES)]) loss, train_result, summary_str = sess.run( (spec.loss, spec.train_op, spec.scaffold.summary_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) self.assertEqual( six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)), train_result) _assert_simple_summaries( self, { metric_keys.MetricKeys.LOSS: expected_loss, metric_keys.MetricKeys.LOSS + '/head1': expected_loss, }, summary_str, tol)
def test_train_create_loss_logits_tensor(self): """Tests create_loss with logits Tensor.""" weights1 = np.array([[1.], [2.]], dtype=np.float32) weights2 = np.array([[2.], [3.]]) head1 = head_lib.multi_label_head(n_classes=2, name='head1', weight_column='weights1') head2 = head_lib.multi_label_head(n_classes=3, name='head2', weight_column='weights2') multi_head = multi_head_lib.multi_head([head1, head2], head_weights=[1., 2.]) logits = np.array( [[-10., 10., 20., -20., 20.], [-15., 10., -30., 20., -20.]], dtype=np.float32) labels = { 'head1': np.array([[1, 0], [1, 1]], dtype=np.int64), 'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64), } training_loss, unreduced_losses, weights, _ = multi_head.create_loss( features={ 'x': np.array(((42, ), ), dtype=np.int32), 'weights1': weights1, 'weights2': weights2 }, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) tol = 1e-3 with self.cached_session(): # loss of the first head is [[(10 + 10) / 2], [(15 + 0) / 2]] # = [10, 7.5] # training_loss = (1 * 10 + 2 * 7.5) / 2 = 12.5 # head-weighted unreduced_loss = 1 * [10, 7.5] self.assertAllClose([[10.], [7.5]], unreduced_losses['head1'].eval(), rtol=tol, atol=tol) # loss of the second head is [[(20 + 20 + 20) / 3], [(30 + 0 + 0) / 3]] # = [20, 10] # training_loss = (2 * 20 + 3 * 10) / 2 = 35 # head-weighted unreduced_loss = 2 * [20, 10] self.assertAllClose([[40.], [20.]], unreduced_losses['head2'].eval(), rtol=tol, atol=tol) # head-weighted training_loss = 1 * 12.5 + 2 * 35 = 82.5 self.assertAllClose(82.5, training_loss.eval(), rtol=tol, atol=tol) # head-weighted example weights self.assertAllClose([[1.], [2.]], weights['head1'].eval(), rtol=tol, atol=tol) self.assertAllClose([[4.], [6.]], weights['head2'].eval(), rtol=tol, atol=tol)
def test_predict_two_heads_logits_tensor(self): """Tests predict with logits as Tensor.""" head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) logits = np.array([[-1., 1., 2., -2., 2.], [-1.5, 1., -3., 2., -2.]], dtype=np.float32) expected_logits1 = np.array([[-1., 1.], [-1.5, 1.]], dtype=np.float32) expected_logits2 = np.array([[2., -2., 2.], [-3., 2., -2.]], dtype=np.float32) expected_probabilities = { 'head1': _sigmoid(expected_logits1), 'head2': _sigmoid(expected_logits2), } spec = multi_head.create_estimator_spec( features={'x': np.array(((42, ), ), dtype=np.int32)}, mode=model_fn.ModeKeys.PREDICT, logits=logits) self.assertItemsEqual( (_DEFAULT_SERVING_KEY, 'predict', 'head1', 'head1/classification', 'head1/predict', 'head2', 'head2/classification', 'head2/predict'), spec.export_outputs.keys()) # Assert predictions and export_outputs. with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) predictions = sess.run(spec.predictions) self.assertAllClose( expected_logits1, predictions[('head1', prediction_keys.PredictionKeys.LOGITS)]) self.assertAllClose( expected_logits2, predictions[('head2', prediction_keys.PredictionKeys.LOGITS)]) self.assertAllClose( expected_probabilities['head1'], predictions[('head1', prediction_keys.PredictionKeys.PROBABILITIES)]) self.assertAllClose( expected_probabilities['head2'], predictions[('head2', prediction_keys.PredictionKeys.PROBABILITIES)]) self.assertAllClose( expected_probabilities['head1'], sess.run(spec.export_outputs[_DEFAULT_SERVING_KEY].scores)) self.assertAllClose(expected_probabilities['head1'], sess.run(spec.export_outputs['head1'].scores)) self.assertAllClose(expected_probabilities['head2'], sess.run(spec.export_outputs['head2'].scores))
def test_predict_two_heads_logits_tensor_multi_dim(self): """Tests predict with multi-dimensional logits of shape [2, 2, 5].""" head1 = head_lib.regression_head(label_dimension=2, name='head1') head2 = head_lib.regression_head(label_dimension=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) logits = np.array( [[[-1., 1., 2., -2., 2.], [-1., 1., 2., -2., 2.]], [[-1.5, 1., -3., 2., -2.], [-1.5, 1., -3., 2., -2.]]], dtype=np.float32) expected_logits1 = np.array( [[[-1., 1.], [-1., 1.]], [[-1.5, 1.], [-1.5, 1.]]], dtype=np.float32) expected_logits2 = np.array( [[[2., -2., 2.], [2., -2., 2.]], [[-3., 2., -2.], [-3., 2., -2.]]], dtype=np.float32) spec = multi_head.create_estimator_spec( features={'x': np.array(((42,),), dtype=np.int32)}, mode=model_fn.ModeKeys.PREDICT, logits=logits) self.assertItemsEqual( (_DEFAULT_SERVING_KEY, 'predict', 'head1', 'head1/regression', 'head1/predict', 'head2', 'head2/regression', 'head2/predict'), spec.export_outputs.keys()) # Assert predictions and export_outputs. with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) predictions = sess.run(spec.predictions) self.assertAllClose( expected_logits1, predictions[('head1', prediction_keys.PredictionKeys.PREDICTIONS)]) self.assertAllClose( expected_logits2, predictions[('head2', prediction_keys.PredictionKeys.PREDICTIONS)]) self.assertAllClose( expected_logits1, sess.run(spec.export_outputs[_DEFAULT_SERVING_KEY].value)) self.assertAllClose( expected_logits1, sess.run(spec.export_outputs['head1'].value)) self.assertAllClose( expected_logits2, sess.run(spec.export_outputs['head2'].value))
def test_train_create_loss_one_head(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') multi_head = multi_head_lib.multi_head([head1]) logits = {'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32)} labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)} loss = multi_head.create_loss( features={'x': np.array(((42,),), dtype=np.int32)}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)[0] tol = 1e-3 with self.cached_session(): # Unreduced loss of the head is [[(10 + 10) / 2], (15 + 0) / 2] # (averaged over classes, averaged over examples). self.assertAllClose(8.75, loss.eval(), rtol=tol, atol=tol)
def test_train_one_head_with_optimizer(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') multi_head = multi_head_lib.multi_head([head1]) logits = { 'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32) } labels = {'head1': np.array([[1, 0], [1, 1]], dtype=np.int64)} # For large logits, sigmoid cross entropy loss is approximated as: # loss = labels * (logits < 0) * (-logits) + # (1 - labels) * (logits > 0) * logits => # expected_unweighted_loss = [[10., 10.], [15., 0.]] # loss = ( (10 + 10) / 2 + (15 + 0) / 2 ) / 2 = 8.75 expected_loss = 8.75 expected_train_result = 'my_train_op' class _Optimizer(object): def minimize(self, loss, global_step): del global_step return string_ops.string_join([ constant_op.constant(expected_train_result), string_ops.as_string(loss, precision=3) ]) spec = multi_head.create_estimator_spec( features={'x': np.array(((42, ), ), dtype=np.int32)}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels, optimizer=_Optimizer()) tol = 1e-3 with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) predictions = sess.run(spec.predictions) self.assertAllClose( logits['head1'], predictions[('head1', prediction_keys.PredictionKeys.LOGITS)]) self.assertAllClose( _sigmoid(logits['head1']), predictions[('head1', prediction_keys.PredictionKeys.PROBABILITIES)]) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) self.assertEqual( six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)), train_result)
def test_train_create_loss_logits_tensor_multi_dim_wrong_shape(self): """Tests create_loss with a multi-dimensional logits tensor of wrong shape.""" head1 = head_lib.regression_head(label_dimension=2, name='head1') head2 = head_lib.regression_head(label_dimension=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) # logits tensor is 2x2x4 instead of 2x2x5 logits = np.array([[[-1., 1., 2., -2.], [-1., 1., 2., -2.]], [[-1.5, 1.5, -2., 2.], [-1.5, 1.5, -2., 2.]]], dtype=np.float32) labels = { 'head1': np.array([[[1., 0.], [1., 0.]], [[1.5, 1.5], [1.5, 1.5]]], dtype=np.float32), 'head2': np.array( [[[0., 1., 0.], [0., 1., 0.]], [[2., 2., 0.], [2., 2., 0.]]], dtype=np.float32), } with self.assertRaisesRegexp(ValueError, r'Could not split logits'): multi_head.create_loss(features={}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)
def test_head_name_missing(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3) with self.assertRaisesRegexp( ValueError, r'All given heads must have name specified\.'): multi_head_lib.multi_head([head1, head2])
def test_no_heads(self): with self.assertRaisesRegexp(ValueError, r'Must specify heads\. Given: \[\]'): multi_head_lib.multi_head(heads=[])
def test_train_two_heads_with_weights(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2], head_weights=[1., 2.]) logits = { 'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32), 'head2': np.array([[20., -20., 20.], [-30., 20., -20.]], dtype=np.float32), } labels = { 'head1': np.array([[1, 0], [1, 1]], dtype=np.int64), 'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64), } # For large logits, sigmoid cross entropy loss is approximated as: # loss = labels * (logits < 0) * (-logits) + # (1 - labels) * (logits > 0) * logits => # head1: expected_unweighted_loss = [[10., 10.], [15., 0.]] # loss = ( (10 + 10) / 2 + (15 + 0) / 2 ) / 2 = 8.75 # head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]] # loss = ( (20 + 20 + 20) / 3 + (30 + 0 + 0) / 3 ) / 2 = 15 # Average over classes, weighted sum over batch and heads. expected_loss_head1 = 8.75 expected_loss_head2 = 15.0 expected_loss = 1. * expected_loss_head1 + 2. * expected_loss_head2 expected_train_result = 'my_train_op' def _train_op_fn(loss): return string_ops.string_join([ constant_op.constant(expected_train_result), string_ops.as_string(loss, precision=3) ]) spec = multi_head.create_estimator_spec( features={'x': np.array(((42, ), ), dtype=np.int32)}, mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels, train_op_fn=_train_op_fn) self.assertIsNotNone(spec.loss) self.assertEqual({}, spec.eval_metric_ops) self.assertIsNotNone(spec.train_op) self.assertIsNone(spec.export_outputs) _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. tol = 1e-3 with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run( (spec.loss, spec.train_op, spec.scaffold.summary_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) self.assertEqual( six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)), train_result) _assert_simple_summaries( self, { metric_keys.MetricKeys.LOSS: expected_loss, metric_keys.MetricKeys.LOSS + '/head1': expected_loss_head1, metric_keys.MetricKeys.LOSS + '/head2': expected_loss_head2, }, summary_str, tol)
def test_eval_two_heads_with_weights(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2], head_weights=[1., 2.]) logits = { 'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32), 'head2': np.array([[20., -20., 20.], [-30., 20., -20.]], dtype=np.float32), } labels = { 'head1': np.array([[1, 0], [1, 1]], dtype=np.int64), 'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64), } # For large logits, sigmoid cross entropy loss is approximated as: # loss = labels * (logits < 0) * (-logits) + # (1 - labels) * (logits > 0) * logits => # head1: expected_unweighted_loss = [[10., 10.], [15., 0.]] # loss = ( (10 + 10) / 2 + (15 + 0) / 2 ) / 2 = 8.75 # head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]] # loss = ( (20 + 20 + 20) / 3 + (30 + 0 + 0) / 3 ) / 2 = 15 expected_loss_head1 = 8.75 expected_loss_head2 = 15. expected_loss = 1. * expected_loss_head1 + 2. * expected_loss_head2 spec = multi_head.create_estimator_spec( features={'x': np.array(((42, ), ), dtype=np.int32)}, mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels) keys = metric_keys.MetricKeys expected_metrics = { keys.LOSS + '/head1': expected_loss_head1, keys.LOSS + '/head2': expected_loss_head2, # Average loss over examples. keys.LOSS_MEAN + '/head1': expected_loss_head1, keys.LOSS_MEAN + '/head2': expected_loss_head2, # auc and auc_pr cannot be reliably calculated for only 4-6 samples, but # this assert tests that the algorithm remains consistent. keys.AUC + '/head1': 0.1667, keys.AUC + '/head2': 0.3333, keys.AUC_PR + '/head1': 0.6667, keys.AUC_PR + '/head2': 0.5000, } # Assert spec contains expected tensors. self.assertIsNotNone(spec.loss) self.assertItemsEqual(expected_metrics.keys(), spec.eval_metric_ops.keys()) self.assertIsNone(spec.train_op) self.assertIsNone(spec.export_outputs) _assert_no_hooks(self, spec) # Assert predictions, loss, and metrics. tol = 1e-3 with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = { k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops } update_ops = { k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops } loss, metrics = sess.run((spec.loss, update_ops)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) # Check results of both update (in `metrics`) and value ops. self.assertAllClose(expected_metrics, metrics, rtol=tol, atol=tol) self.assertAllClose(expected_metrics, {k: value_ops[k].eval() for k in value_ops}, rtol=tol, atol=tol)
def test_name(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) self.assertEqual('head1_head2', multi_head.name)
def simple_multi_head(export_path, eval_export_path): """Trains and exports a simple multi-headed model.""" def eval_input_receiver_fn(): """Eval input receiver function.""" serialized_tf_example = tf.compat.v1.placeholder( dtype=tf.string, shape=[None], name='input_example_tensor') language = tf.feature_column.categorical_column_with_vocabulary_list( 'language', ['english', 'chinese', 'other']) age = tf.feature_column.numeric_column('age') english_label = tf.feature_column.numeric_column('english_label') chinese_label = tf.feature_column.numeric_column('chinese_label') other_label = tf.feature_column.numeric_column('other_label') all_features = [age, language, english_label, chinese_label, other_label] feature_spec = tf.feature_column.make_parse_example_spec(all_features) receiver_tensors = {'examples': serialized_tf_example} features = tf.io.parse_example( serialized=serialized_tf_example, features=feature_spec) labels = { 'english_head': features['english_label'], 'chinese_head': features['chinese_label'], 'other_head': features['other_label'], } return export.EvalInputReceiver( features=features, receiver_tensors=receiver_tensors, labels=labels) def input_fn(): """Train input function.""" labels = { 'english_head': tf.constant([[1], [1], [0], [0], [0], [0]]), 'chinese_head': tf.constant([[0], [0], [1], [1], [0], [0]]), 'other_head': tf.constant([[0], [0], [0], [0], [1], [1]]) } features = { 'age': tf.constant([[1], [2], [3], [4], [5], [6]]), 'language': tf.SparseTensor( values=[ 'english', 'english', 'chinese', 'chinese', 'other', 'other' ], indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0], [5, 0]], dense_shape=[6, 1]), } return features, labels language = tf.feature_column.categorical_column_with_vocabulary_list( 'language', ['english', 'chinese', 'other']) age = tf.feature_column.numeric_column('age') all_features = [age, language] feature_spec = tf.feature_column.make_parse_example_spec(all_features) # TODO(b/130299739): Update with tf.estimator.BinaryClassHead and # tf.estimator.MultiHead english_head = head.binary_classification_head(name='english_head') chinese_head = head.binary_classification_head(name='chinese_head') other_head = head.binary_classification_head(name='other_head') combined_head = multi_head.multi_head( [english_head, chinese_head, other_head]) estimator = tf.estimator.DNNLinearCombinedEstimator( head=combined_head, dnn_feature_columns=[], dnn_optimizer=tf.compat.v1.train.AdagradOptimizer(learning_rate=0.01), dnn_hidden_units=[], linear_feature_columns=[language, age], linear_optimizer=tf.compat.v1.train.FtrlOptimizer(learning_rate=0.05)) estimator.train(input_fn=input_fn, steps=1000) return util.export_model_and_eval_model( estimator=estimator, serving_input_receiver_fn=( tf.estimator.export.build_parsing_serving_input_receiver_fn( feature_spec)), eval_input_receiver_fn=eval_input_receiver_fn, export_path=export_path, eval_export_path=eval_export_path)