diff options
| -rw-r--r-- | tensorflow/contrib/distribute/python/keras_test.py | 119 | ||||
| -rw-r--r-- | tensorflow/python/estimator/keras_test.py | 166 | ||||
| -rw-r--r-- | tensorflow/python/keras/testing_utils.py | 6 |
3 files changed, 246 insertions, 45 deletions
diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index 9e1762d92c..5f35e38189 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -34,6 +34,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils from tensorflow.python.keras.engine import distributed_training_utils +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 @@ -66,6 +67,32 @@ def simple_functional_model(): return model +def multi_inputs_multi_outputs_model(): + input_a = keras.layers.Input(shape=(16,), name='input_a') + input_b = keras.layers.Input(shape=(16,), name='input_b') + input_m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') + dense = keras.layers.Dense(8, name='dense_1') + + interm_a = dense(input_a) + # Read m + interm_m = keras.layers.Lambda(gen_parsing_ops.string_to_number)(input_m) + interm_s = keras.layers.Lambda(lambda k: k[0] * k[1])([interm_m, interm_a]) + interm_b = dense(input_b) + merged = keras.layers.concatenate([interm_s, interm_b], name='merge') + output_c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) + output_d = keras.layers.Dense(2, activation='softmax', name='dense_3')(merged) + model = keras.models.Model( + inputs=[input_a, input_b, input_m], outputs=[output_c, output_d]) + model.compile( + loss='categorical_crossentropy', + optimizer=gradient_descent.GradientDescentOptimizer(0.001), + metrics={ + 'dense_2': 'categorical_accuracy', + 'dense_3': 'categorical_accuracy' + }) + return model + + def get_ds_train_input_fn(): np.random.seed(_RANDOM_SEED) (x_train, y_train), _ = testing_utils.get_test_data( @@ -94,6 +121,49 @@ def get_ds_test_input_fn(): return dataset +def get_multi_inputs_multi_outputs_data(): + (a_train, c_train), (a_test, c_test) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(16,), + num_classes=3, + random_seed=_RANDOM_SEED) + (b_train, d_train), (b_test, d_test) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(16,), + num_classes=2, + random_seed=_RANDOM_SEED) + (m_train, _), (m_test, _) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(8,), + num_classes=2, + random_seed=_RANDOM_SEED) + + c_train = keras.utils.to_categorical(c_train) + c_test = keras.utils.to_categorical(c_test) + d_train = keras.utils.to_categorical(d_train) + d_test = keras.utils.to_categorical(d_test) + + train_data = { + 'input_a': a_train, + 'input_b': b_train, + 'input_m': m_train, + 'output_c': c_train, + 'output_d': d_train + } + test_data = { + 'input_a': a_test, + 'input_b': b_test, + 'input_m': m_test, + 'output_c': c_test, + 'output_d': d_test + } + + return (train_data, test_data) + + def batch_wrapper(dataset, batch_size, distribution): # TPUs currently require fully defined input shapes, drop_remainder ensures # the input will have fully defined shapes. @@ -121,6 +191,8 @@ class TestEstimatorDistributionStrategy(test_util.TensorFlowTestCase): gfile.MakeDirs(self._base_dir) self._config = run_config_lib.RunConfig( tf_random_seed=_RANDOM_SEED, model_dir=self._base_dir) + self._dist = mirrored_strategy.MirroredStrategy( + devices=['/device:GPU:0', '/device:GPU:1']) def tearDown(self): writer_cache.FileWriterCache.clear() @@ -174,6 +246,53 @@ class TestEstimatorDistributionStrategy(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + def test_multi_inputs_multi_outputs_with_input_fn_as_dict(self): + train_data, test_data = get_multi_inputs_multi_outputs_data() + + def train_input_fn(): + input_dict = { + 'input_a': train_data['input_a'], + 'input_b': train_data['input_b'], + 'input_m': train_data['input_m'].astype(np.str) + } + output_dict = { + 'dense_2': train_data['output_c'], + 'dense_3': train_data['output_d'] + } + return dataset_ops.Dataset.from_tensor_slices((input_dict, + output_dict)).batch(16) + + def eval_input_fn(): + input_dict = { + 'input_a': test_data['input_a'], + 'input_b': test_data['input_b'], + 'input_m': test_data['input_m'].astype(np.str) + } + output_dict = { + 'dense_2': test_data['output_c'], + 'dense_3': test_data['output_d'] + } + return dataset_ops.Dataset.from_tensor_slices((input_dict, + output_dict)).batch(16) + + self.do_test_multi_inputs_multi_outputs_with_input_fn( + train_input_fn, eval_input_fn) + + def do_test_multi_inputs_multi_outputs_with_input_fn(self, train_input_fn, + eval_input_fn): + config = run_config_lib.RunConfig( + tf_random_seed=_RANDOM_SEED, + model_dir=self._base_dir, + train_distribute=self._dist) + with self.cached_session(): + model = multi_inputs_multi_outputs_model() + est_keras = keras_lib.model_to_estimator(keras_model=model, config=config) + baseline_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16) + eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(eval_results['loss'], baseline_eval_results['loss']) + def test_keras_optimizer_with_distribution_strategy(self): dist = mirrored_strategy.MirroredStrategy( devices=['/device:GPU:0', '/device:GPU:1']) diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 7e5a0c80a7..3758243d7b 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -102,6 +102,49 @@ def gen_input_fn(x, y=None, batch_size=128, num_epochs=1, shuffle=False): return input_fn +def get_multi_inputs_multi_outputs_data(): + (a_train, c_train), (a_test, c_test) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(16,), + num_classes=3, + random_seed=_RANDOM_SEED) + (b_train, d_train), (b_test, d_test) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(16,), + num_classes=2, + random_seed=_RANDOM_SEED) + (m_train, _), (m_test, _) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(8,), + num_classes=2, + random_seed=_RANDOM_SEED) + + c_train = keras.utils.to_categorical(c_train) + c_test = keras.utils.to_categorical(c_test) + d_train = keras.utils.to_categorical(d_train) + d_test = keras.utils.to_categorical(d_test) + + train_data = { + 'input_a': a_train, + 'input_b': b_train, + 'input_m': m_train, + 'output_c': c_train, + 'output_d': d_train + } + test_data = { + 'input_a': a_test, + 'input_b': b_test, + 'input_m': m_test, + 'output_c': c_test, + 'output_d': d_test + } + + return (train_data, test_data) + + def get_resource_for_simple_model(model_type='sequential', is_evaluate=False,): if model_type == 'sequential': @@ -159,20 +202,21 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): - a = keras.layers.Input(shape=(16,), name='input_a') - b = keras.layers.Input(shape=(16,), name='input_b') - m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') + input_a = keras.layers.Input(shape=(16,), name='input_a') + input_b = keras.layers.Input(shape=(16,), name='input_b') + input_m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') dense = keras.layers.Dense(8, name='dense_1') - a_2 = dense(a) + interm_a = dense(input_a) # Read m - m_2 = keras.layers.Lambda(gen_parsing_ops.string_to_number)(m) - s_2 = keras.layers.Lambda(lambda k: k[0] * k[1])([m_2, a_2]) - b_2 = dense(b) - merged = keras.layers.concatenate([s_2, b_2], name='merge') - c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) - d = keras.layers.Dense(2, activation='softmax', name='dense_3')(merged) - model = keras.models.Model(inputs=[a, b, m], outputs=[c, d]) + interm_m = keras.layers.Lambda(gen_parsing_ops.string_to_number)(input_m) + interm_s = keras.layers.Lambda(lambda k: k[0] * k[1])([interm_m, interm_a]) + interm_b = dense(input_b) + merged = keras.layers.concatenate([interm_s, interm_b], name='merge') + output_c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) + output_d = keras.layers.Dense(2, activation='softmax', name='dense_3')(merged) + model = keras.models.Model( + inputs=[input_a, input_b, input_m], outputs=[output_c, output_d]) model.compile( loss='categorical_crossentropy', optimizer='rmsprop', @@ -414,51 +458,85 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): ] self.assertAllEqual(est_pred, keras_pred) - def test_multi_inputs_multi_outputs(self): - np.random.seed(_RANDOM_SEED) - (a_train, c_train), (a_test, c_test) = testing_utils.get_test_data( - train_samples=_TRAIN_SIZE, - test_samples=50, - input_shape=(16,), - num_classes=3) - np.random.seed(_RANDOM_SEED) - (b_train, d_train), (b_test, d_test) = testing_utils.get_test_data( - train_samples=_TRAIN_SIZE, - test_samples=50, - input_shape=(16,), - num_classes=2) - np.random.seed(_RANDOM_SEED) - (input_m_train, _), (input_m_test, _) = testing_utils.get_test_data( - train_samples=_TRAIN_SIZE, - test_samples=50, - input_shape=(8,), - num_classes=2) - - c_train = keras.utils.to_categorical(c_train) - c_test = keras.utils.to_categorical(c_test) - d_train = keras.utils.to_categorical(d_train) - d_test = keras.utils.to_categorical(d_test) + def test_multi_inputs_multi_outputs_with_input_fn_as_dict(self): + train_data, test_data = get_multi_inputs_multi_outputs_data() def train_input_fn(): - input_dict = {'input_a': a_train, 'input_b': b_train, - 'input_m': input_m_train.astype(np.str)} - output_dict = {'dense_2': c_train, 'dense_3': d_train} + input_dict = { + 'input_a': train_data['input_a'], + 'input_b': train_data['input_b'], + 'input_m': train_data['input_m'].astype(np.str) + } + output_dict = { + 'dense_2': train_data['output_c'], + 'dense_3': train_data['output_d'] + } return input_dict, output_dict def eval_input_fn(): - input_dict = {'input_a': a_test, 'input_b': b_test, - 'input_m': input_m_test.astype(np.str)} - output_dict = {'dense_2': c_test, 'dense_3': d_test} + input_dict = { + 'input_a': test_data['input_a'], + 'input_b': test_data['input_b'], + 'input_m': test_data['input_m'].astype(np.str) + } + output_dict = { + 'dense_2': test_data['output_c'], + 'dense_3': test_data['output_d'] + } return input_dict, output_dict + def pred_input_fn(): + input_dict = { + 'input_a': test_data['input_a'], + 'input_b': test_data['input_b'], + 'input_m': test_data['input_m'].astype(np.str) + } + return input_dict + + self.do_test_multi_inputs_multi_outputs_with_input_fn( + train_input_fn, eval_input_fn, pred_input_fn) + + def test_multi_inputs_multi_outputs_with_input_fn_as_list(self): + train_data, test_data = get_multi_inputs_multi_outputs_data() + + def train_input_fn(): + input_list = [ + train_data['input_a'], train_data['input_b'], + train_data['input_m'].astype(np.str) + ] + output_list = [train_data['output_c'], train_data['output_d']] + return input_list, output_list + + def eval_input_fn(): + input_list = [ + test_data['input_a'], test_data['input_b'], + test_data['input_m'].astype(np.str) + ] + output_list = [test_data['output_c'], test_data['output_d']] + return input_list, output_list + + def pred_input_fn(): + input_list = [ + test_data['input_a'], test_data['input_b'], + test_data['input_m'].astype(np.str) + ] + return input_list + + self.do_test_multi_inputs_multi_outputs_with_input_fn( + train_input_fn, eval_input_fn, pred_input_fn) + + def do_test_multi_inputs_multi_outputs_with_input_fn( + self, train_input_fn, eval_input_fn, pred_input_fn): with self.cached_session(): model = multi_inputs_multi_outputs_model() est_keras = keras_lib.model_to_estimator( keras_model=model, config=self._config) - before_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + baseline_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) est_keras.train(input_fn=train_input_fn, 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']) + eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(eval_results['loss'], baseline_eval_results['loss']) + est_keras.predict(input_fn=pred_input_fn) def test_init_from_file(self): if h5py is None: diff --git a/tensorflow/python/keras/testing_utils.py b/tensorflow/python/keras/testing_utils.py index 58405c550b..501b50ba5f 100644 --- a/tensorflow/python/keras/testing_utils.py +++ b/tensorflow/python/keras/testing_utils.py @@ -29,7 +29,8 @@ from tensorflow.python.util import tf_inspect def get_test_data(train_samples, test_samples, input_shape, - num_classes): + num_classes, + random_seed=None): """Generates test data to train a model on. Arguments: @@ -37,10 +38,13 @@ def get_test_data(train_samples, test_samples: Integer, how many test samples to generate. input_shape: Tuple of integers, shape of the inputs. num_classes: Integer, number of classes for the data and targets. + random_seed: Integer, random seed used by numpy to generate data. Returns: A tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ + if random_seed is not None: + np.random.seed(random_seed) num_sample = train_samples + test_samples templates = 2 * num_classes * np.random.random((num_classes,) + input_shape) y = np.random.randint(0, num_classes, size=(num_sample,)) |