def test_warm_starting_selective_variables(self, fc_impl): """Tests selecting variables to warm-start.""" age = fc_impl.numeric_column('age') city = fc_impl.embedding_column( fc_impl.categorical_column_with_vocabulary_list( 'city', vocabulary_list=['Mountain View', 'Palo Alto']), dimension=5) # Create a DNNLinearCombinedClassifier and train to save a checkpoint. dnn_lc_classifier = dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=[age], dnn_feature_columns=[city], dnn_hidden_units=[256, 128], model_dir=self._ckpt_and_vocab_dir, n_classes=4, linear_optimizer='SGD', dnn_optimizer='SGD') dnn_lc_classifier.train(input_fn=self._input_fn, max_steps=1) # Create a second DNNLinearCombinedClassifier, warm-started from the first. # Use a learning_rate = 0.0 optimizer to check values (use SGD so we don't # have accumulator values that change). warm_started_dnn_lc_classifier = ( dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=[age], dnn_feature_columns=[city], dnn_hidden_units=[256, 128], n_classes=4, linear_optimizer=gradient_descent.GradientDescentOptimizer( learning_rate=0.0), dnn_optimizer=gradient_descent.GradientDescentOptimizer( learning_rate=0.0), # The provided regular expression will only warm-start the deep # portion of the model. warm_start_from=estimator.WarmStartSettings( ckpt_to_initialize_from=dnn_lc_classifier.model_dir, vars_to_warm_start='.*(dnn).*'))) warm_started_dnn_lc_classifier.train(input_fn=self._input_fn, max_steps=1) for variable_name in warm_started_dnn_lc_classifier.get_variable_names( ): if 'dnn' in variable_name: self.assertAllClose( dnn_lc_classifier.get_variable_value(variable_name), warm_started_dnn_lc_classifier.get_variable_value( variable_name)) elif 'linear' in variable_name: linear_values = warm_started_dnn_lc_classifier.get_variable_value( variable_name) # Since they're not warm-started, the linear weights will be # zero-initialized. self.assertAllClose(np.zeros_like(linear_values), linear_values)
def test_train_op_calls_both_dnn_and_linear(self): opt = gradient_descent.GradientDescentOptimizer(1.) x_column = feature_column.numeric_column('x') input_fn = numpy_io.numpy_input_fn(x={'x': np.array([[0.], [1.]])}, y=np.array([[0.], [1.]]), batch_size=1, shuffle=False) est = dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=[x_column], # verifies linear_optimizer is used only for linear part. linear_optimizer=self._mock_optimizer(opt, 'linear'), dnn_hidden_units=(2, 2), dnn_feature_columns=[x_column], # verifies dnn_optimizer is used only for linear part. dnn_optimizer=self._mock_optimizer(opt, 'dnn'), model_dir=self._model_dir) est.train(input_fn, steps=1) # verifies train_op fires linear minimize op self.assertEqual( 100., checkpoint_utils.load_variable( self._model_dir, 'binary_logistic_head/linear_called')) # verifies train_op fires dnn minimize op self.assertEqual( 100., checkpoint_utils.load_variable(self._model_dir, 'binary_logistic_head/dnn_called'))
def test_classifier_basic_warm_starting(self, fc_impl): """Tests correctness of DNNLinearCombinedClassifier default warm-start.""" age = fc_impl.numeric_column('age') city = fc_impl.embedding_column( fc_impl.categorical_column_with_vocabulary_list( 'city', vocabulary_list=['Mountain View', 'Palo Alto']), dimension=5) # Create a DNNLinearCombinedClassifier and train to save a checkpoint. dnn_lc_classifier = dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=[age], dnn_feature_columns=[city], dnn_hidden_units=[256, 128], model_dir=self._ckpt_and_vocab_dir, n_classes=4, linear_optimizer='SGD', dnn_optimizer='SGD') dnn_lc_classifier.train(input_fn=self._input_fn, max_steps=1) # Create a second DNNLinearCombinedClassifier, warm-started from the first. # Use a learning_rate = 0.0 optimizer to check values (use SGD so we don't # have accumulator values that change). warm_started_dnn_lc_classifier = ( dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=[age], dnn_feature_columns=[city], dnn_hidden_units=[256, 128], n_classes=4, linear_optimizer=gradient_descent.GradientDescentOptimizer( learning_rate=0.0), dnn_optimizer=gradient_descent.GradientDescentOptimizer( learning_rate=0.0), warm_start_from=dnn_lc_classifier.model_dir)) warm_started_dnn_lc_classifier.train(input_fn=self._input_fn, max_steps=1) for variable_name in warm_started_dnn_lc_classifier.get_variable_names( ): self.assertAllClose( dnn_lc_classifier.get_variable_value(variable_name), warm_started_dnn_lc_classifier.get_variable_value( variable_name))
def _dnn_classifier_fn(hidden_units, feature_columns, model_dir=None, n_classes=2, weight_feature_key=None, optimizer='Adagrad', config=None, input_layer_partitioner=None): return dnn_linear_combined.DNNLinearCombinedClassifier( model_dir=model_dir, dnn_hidden_units=hidden_units, dnn_feature_columns=feature_columns, dnn_optimizer=optimizer, n_classes=n_classes, weight_feature_key=weight_feature_key, input_layer_partitioner=input_layer_partitioner, config=config)
def _linear_classifier_fn(feature_columns, model_dir=None, n_classes=2, weight_column=None, label_vocabulary=None, optimizer='Ftrl', config=None, partitioner=None): return dnn_linear_combined.DNNLinearCombinedClassifier( model_dir=model_dir, linear_feature_columns=feature_columns, linear_optimizer=optimizer, n_classes=n_classes, weight_column=weight_column, label_vocabulary=label_vocabulary, input_layer_partitioner=partitioner, config=config)
def _test_complete_flow(self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension, n_classes, batch_size): linear_feature_columns = [ feature_column.numeric_column('x', shape=(input_dimension, )) ] dnn_feature_columns = [ feature_column.numeric_column('x', shape=(input_dimension, )) ] feature_columns = linear_feature_columns + dnn_feature_columns est = dnn_linear_combined.DNNLinearCombinedClassifier( linear_feature_columns=linear_feature_columns, dnn_hidden_units=(2, 2), dnn_feature_columns=dnn_feature_columns, n_classes=n_classes, model_dir=self._model_dir) # TRAIN num_steps = 10 est.train(train_input_fn, steps=num_steps) # EVALUTE scores = est.evaluate(eval_input_fn) self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP]) self.assertIn('loss', six.iterkeys(scores)) # PREDICT predicted_proba = np.array([ x[prediction_keys.PredictionKeys.PROBABILITIES] for x in est.predict(predict_input_fn) ]) self.assertAllEqual((batch_size, n_classes), predicted_proba.shape) # EXPORT feature_spec = feature_column.make_parse_example_spec(feature_columns) serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn( feature_spec) export_dir = est.export_savedmodel(tempfile.mkdtemp(), serving_input_receiver_fn) self.assertTrue(gfile.Exists(export_dir))