diff options
Diffstat (limited to 'tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py')
| -rw-r--r-- | tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py | 96 |
1 files changed, 11 insertions, 85 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py index 21d5a9c327..10b47fba5a 100644 --- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py @@ -65,35 +65,20 @@ class DNNClassifierIntegrationTest(test_util.TensorFlowTestCase): data = np.linspace( 0., n_classes - 1., batch_size * input_dimension, dtype=np.float32) x_data = data.reshape(batch_size, input_dimension) - categorical_data = np.random.random_integers( - 0, len(x_data), size=len(x_data)) y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1)) train_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data, - 'categories': categorical_data}, + x={'x': x_data}, y=y_data, batch_size=batch_size, num_epochs=None, shuffle=True) eval_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data, - 'categories': categorical_data}, - y=y_data, - batch_size=batch_size, - shuffle=False) + x={'x': x_data}, y=y_data, batch_size=batch_size, shuffle=False) predict_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data, - 'categories': categorical_data}, - batch_size=batch_size, - shuffle=False) + x={'x': x_data}, batch_size=batch_size, shuffle=False) feature_columns = [ - feature_column.numeric_column('x', shape=(input_dimension,)), - feature_column.indicator_column( - feature_column.categorical_column_with_vocabulary_list( - 'categories', - vocabulary_list=np.linspace( - 0., len(x_data), len(x_data), dtype=np.int64))) + feature_column.numeric_column('x', shape=(input_dimension,)) ] estimator = dnn.DNNClassifier( @@ -105,11 +90,14 @@ class DNNClassifierIntegrationTest(test_util.TensorFlowTestCase): def optimizer_fn(): return optimizers.get_optimizer_instance('Adagrad', learning_rate=0.05) + # TODO(isaprykin): Switch Estimator to use allow_soft_placement=True + # during export_savedmodel and then switch this test to replicate over + # GPUs instead of CPUs. estimator = estimator_lib.Estimator( model_fn=replicate_model_fn.replicate_model_fn( estimator.model_fn, optimizer_fn, - devices=['/gpu:0', '/gpu:1', '/gpu:2']), + devices=['/cpu:0', '/cpu:0', '/cpu:0']), model_dir=estimator.model_dir, config=estimator.config, params=estimator.params) @@ -242,7 +230,6 @@ class ReplicateModelTest(test_util.TensorFlowTestCase): accuracy = session.run(accuracy) auc = session.run(auc) - # loss[i] = features[i] * 10 - labels[i]. # Accuracy is 0.0 (no match) in the first tower. # Accuracy is 1.0 (match) in the second tower, since the feature # times weight "c" happened to be equal to the label. @@ -544,7 +531,8 @@ class EvalSpecTest(test_util.TensorFlowTestCase): self.assertEqual('/device:CPU:0', auc.device) session.run([a, b]) - accuracy, auc = session.run([accuracy, auc]) + accuracy = session.run(accuracy) + auc = session.run(auc) self.assertNear((12 - 2) / 12, accuracy, 0.01) self.assertEqual(0, auc) @@ -873,7 +861,7 @@ class LocalDeviceSetterTest(test_util.TensorFlowTestCase): class ComputeSumWithDevicePlacementTest(test_util.TensorFlowTestCase): - def test_vectors(self): + def test_example(self): with self.test_session() as session: total = replicate_model_fn._compute_sum_on_device( [1.0, 2.0, 3.0, 4.0], device='/device:GPU:0', name='test_sum') @@ -882,68 +870,6 @@ class ComputeSumWithDevicePlacementTest(test_util.TensorFlowTestCase): self.assertEqual('test_sum', total.op.name) self.assertEqual(10.0, session.run(total)) - def test_tensors(self): - with self.test_session() as session: - total = replicate_model_fn._compute_sum_on_device( - [[1.0, 2.0], [3.0, 4.0]], device='/device:GPU:0', name='test_sum') - - self.assertEqual('/device:GPU:0', total.device) - self.assertEqual('test_sum', total.op.name) - self.assertAllEqual([4.0, 6.0], session.run(total)) - - def test_indexedslices(self): - with self.test_session() as session: - a = ops_lib.IndexedSlices( - constant_op.constant([1.0, 2.0]), [0, 1], - dense_shape=constant_op.constant([2])) - b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) - - total = replicate_model_fn._compute_sum_on_device( - [a, b], device='/device:GPU:0') - - self.assertEqual('/device:GPU:0', total.device) - self.assertAllEqual([4.0, 6.0], - session.run(ops_lib.convert_to_tensor(total))) - - def test_indexedslices_higher_dimensions(self): - with self.test_session() as session: - a = ops_lib.IndexedSlices( - constant_op.constant([[1.0, 5.0], [2.0, 6.0]]), [0, 1], - dense_shape=constant_op.constant([2, 4])) - b = ops_lib.IndexedSlices( - constant_op.constant([[3.0, 7.0], [4.0, 8.0]]), [0, 1]) - - total = replicate_model_fn._compute_sum_on_device( - [a, b], device='/device:GPU:0') - - self.assertEqual('/device:GPU:0', total.device) - self.assertAllEqual([[4.0, 12.0], [6.0, 14.0]], - session.run(ops_lib.convert_to_tensor(total))) - - def test_indexedslices_some_dont_overlap(self): - with self.test_session() as session: - a = ops_lib.IndexedSlices( - constant_op.constant([1.0, 2.0]), [0, 3], - dense_shape=constant_op.constant([4])) - b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) - - total = replicate_model_fn._compute_sum_on_device( - [a, b], device='/device:GPU:0') - - self.assertEqual('/device:GPU:0', total.device) - self.assertAllEqual([4.0, 4.0, 0.0, 2.0], - session.run(ops_lib.convert_to_tensor(total))) - - def test_no_name_for_indexslices(self): - a = ops_lib.IndexedSlices( - constant_op.constant([1.0, 2.0]), [0, 1], - dense_shape=constant_op.constant([2])) - b = ops_lib.IndexedSlices(constant_op.constant([3.0, 4.0]), [0, 1]) - - with self.assertRaisesRegexp(ValueError, ''): - _ = replicate_model_fn._compute_sum_on_device( - [a, b], device='/device:GPU:0', name='cant_name_indexslices') - class ConcatTensorDictsTest(test_util.TensorFlowTestCase): |