diff options
Diffstat (limited to 'tensorflow/python/estimator/keras_test.py')
| -rw-r--r-- | tensorflow/python/estimator/keras_test.py | 174 |
1 files changed, 157 insertions, 17 deletions
diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 5e094ae92b..cf4ec7f4da 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -32,13 +32,14 @@ from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils -from tensorflow.python.keras.applications import mobilenet from tensorflow.python.keras.optimizers import SGD +from tensorflow.python.ops import variable_scope from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import rmsprop +from tensorflow.python.training import session_run_hook try: @@ -51,6 +52,8 @@ _TRAIN_SIZE = 200 _INPUT_SIZE = (10,) _NUM_CLASS = 2 +_TMP_DIR = '/tmp' + def simple_sequential_model(): model = keras.models.Sequential() @@ -60,9 +63,9 @@ def simple_sequential_model(): return model -def simple_functional_model(): +def simple_functional_model(activation='relu'): a = keras.layers.Input(shape=_INPUT_SIZE) - b = keras.layers.Dense(16, activation='relu')(a) + b = keras.layers.Dense(16, activation=activation)(a) b = keras.layers.Dropout(0.1)(b) b = keras.layers.Dense(_NUM_CLASS, activation='softmax')(b) model = keras.models.Model(inputs=[a], outputs=[b]) @@ -168,6 +171,12 @@ def multi_inputs_multi_outputs_model(): return model +class MyHook(session_run_hook.SessionRunHook): + + def begin(self): + _ = variable_scope.get_variable('temp', [1]) + + class TestKerasEstimator(test_util.TensorFlowTestCase): def setUp(self): @@ -204,6 +213,55 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + # see b/109935364 + @test_util.run_in_graph_and_eager_modes + def test_train_with_hooks(self): + for model_type in ['sequential', 'functional']: + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type=model_type, is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + my_hook = MyHook() + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, hooks=[my_hook], + steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + writer_cache.FileWriterCache.clear() + gfile.DeleteRecursively(self._config.model_dir) + + @test_util.run_in_graph_and_eager_modes + def test_train_with_model_fit_and_hooks(self): + keras_model, (x_train, y_train), _, \ + train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + my_hook = MyHook() + with self.test_session(): + keras_model.fit(x_train, y_train, epochs=1) + + keras_est = keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + before_eval_results = keras_est.evaluate(input_fn=eval_input_fn) + keras_est.train(input_fn=train_input_fn, hooks=[my_hook], + steps=_TRAIN_SIZE / 16) + after_eval_results = keras_est.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + @test_util.run_in_graph_and_eager_modes def test_train_with_tf_optimizer(self): for model_type in ['sequential', 'functional']: keras_model, (_, _), ( @@ -231,6 +289,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + @test_util.run_in_graph_and_eager_modes def test_train_with_subclassed_model(self): keras_model, (_, _), ( _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( @@ -472,23 +531,43 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): est_keras.train(input_fn=invald_output_name_input_fn, steps=100) def test_custom_objects(self): - keras_mobile = mobilenet.MobileNet(weights=None) - keras_mobile.compile(loss='categorical_crossentropy', optimizer='adam') + + def relu6(x): + return keras.backend.relu(x, max_value=6) + + keras_model = simple_functional_model(activation=relu6) + keras_model.compile(loss='categorical_crossentropy', optimizer='adam') custom_objects = { - 'relu6': mobilenet.relu6, - 'DepthwiseConv2D': mobilenet.DepthwiseConv2D + 'relu6': relu6 } + + (x_train, y_train), _ = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(10,), + num_classes=2) + y_train = keras.utils.to_categorical(y_train, 2) + input_name = keras_model.input_names[0] + output_name = keras_model.output_names[0] + train_input_fn = numpy_io.numpy_input_fn( + x=randomize_io_type(x_train, input_name), + y=randomize_io_type(y_train, output_name), + shuffle=False, + num_epochs=None, + batch_size=16) with self.assertRaisesRegexp(ValueError, 'relu6'): with self.test_session(): - keras_lib.model_to_estimator( - keras_model=keras_mobile, + est = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=tempfile.mkdtemp(dir=self._base_dir)) + est.train(input_fn=train_input_fn, steps=1) with self.test_session(): - keras_lib.model_to_estimator( - keras_model=keras_mobile, + est = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=tempfile.mkdtemp(dir=self._base_dir), custom_objects=custom_objects) + est.train(input_fn=train_input_fn, steps=1) def test_tf_config(self): keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() @@ -525,12 +604,73 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3) sess_config = config_pb2.ConfigProto(gpu_options=gpu_options) self._config._session_config = sess_config - keras_lib.model_to_estimator( - keras_model=keras_model, config=self._config) - self.assertEqual( - keras.backend.get_session() - ._config.gpu_options.per_process_gpu_memory_fraction, - gpu_options.per_process_gpu_memory_fraction) + with self.test_session(): + keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + self.assertEqual( + keras.backend.get_session() + ._config.gpu_options.per_process_gpu_memory_fraction, + gpu_options.per_process_gpu_memory_fraction) + + def test_with_empty_config(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=run_config_lib.RunConfig()) + self.assertEqual(run_config_lib.get_default_session_config(), + est_keras._session_config) + self.assertEqual(est_keras._session_config, + est_keras._config.session_config) + self.assertEqual(self._base_dir, est_keras._config.model_dir) + self.assertEqual(self._base_dir, est_keras._model_dir) + + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=None) + self.assertEqual(run_config_lib.get_default_session_config(), + est_keras._session_config) + self.assertEqual(est_keras._session_config, + est_keras._config.session_config) + self.assertEqual(self._base_dir, est_keras._config.model_dir) + self.assertEqual(self._base_dir, est_keras._model_dir) + + def test_with_empty_config_and_empty_model_dir(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + with test.mock.patch.object(tempfile, 'mkdtemp', return_value=_TMP_DIR): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, + config=run_config_lib.RunConfig()) + self.assertEqual(est_keras._model_dir, _TMP_DIR) + + def test_with_conflicting_model_dir_and_config(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + with self.assertRaisesRegexp(ValueError, '`model_dir` are set both in ' + 'constructor and `RunConfig`'): + keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=run_config_lib.RunConfig(model_dir=_TMP_DIR)) def test_pretrained_weights(self): keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() |