tensorflow_estimator/python/estimator/canned/dnn_testing_utils.py [1590:1727]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._dnn_regressor_fn = dnn_regressor_fn self._fc_impl = fc_impl def setUp(self): self._model_dir = tempfile.mkdtemp() def tearDown(self): if self._model_dir: tf.compat.v1.summary.FileWriterCache.clear() shutil.rmtree(self._model_dir) def test_one_dim(self): """Asserts predictions for one-dimensional input and logits.""" # Create checkpoint: num_inputs=1, hidden_units=(2, 2), num_outputs=1. create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), global_step=0, model_dir=self._model_dir) dnn_regressor = self._dnn_regressor_fn( hidden_units=(2, 2), feature_columns=(self._fc_impl.numeric_column('x'),), model_dir=self._model_dir) input_fn = numpy_io.numpy_input_fn( x={'x': np.array([[10.]])}, batch_size=1, shuffle=False) # Uses identical numbers as DNNModelTest.test_one_dim_logits. # See that test for calculation of logits. # logits = [[-2.08]] => predictions = [-2.08]. self.assertAllClose({ prediction_keys.PredictionKeys.PREDICTIONS: [-2.08], }, next(dnn_regressor.predict(input_fn=input_fn))) def test_multi_dim(self): """Asserts predictions for multi-dimensional input and logits.""" # Create checkpoint: num_inputs=2, hidden_units=(2, 2), num_outputs=3. create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), 100, self._model_dir) dnn_regressor = self._dnn_regressor_fn( hidden_units=(2, 2), feature_columns=(self._fc_impl.numeric_column('x', shape=(2,)),), label_dimension=3, model_dir=self._model_dir) input_fn = numpy_io.numpy_input_fn( # Inputs shape is (batch_size, num_inputs). x={'x': np.array([[10., 8.]])}, batch_size=1, shuffle=False) # Uses identical numbers as # DNNModelFnTest.test_multi_dim_input_multi_dim_logits. # See that test for calculation of logits. # logits = [[-0.48, 0.48, 0.39]] => predictions = [-0.48, 0.48, 0.39] self.assertAllClose( { prediction_keys.PredictionKeys.PREDICTIONS: [-0.48, 0.48, 0.39], }, next(dnn_regressor.predict(input_fn=input_fn))) class _SummaryHook(tf.compat.v1.train.SessionRunHook): """Saves summaries every N steps.""" def __init__(self): self._summaries = [] def begin(self): self._summary_op = tf.compat.v1.summary.merge_all() def before_run(self, run_context): return tf.compat.v1.train.SessionRunArgs({'summary': self._summary_op}) def after_run(self, run_context, run_values): s = tf.compat.v1.summary.Summary() s.ParseFromString(run_values.results['summary']) self._summaries.append(s) def summaries(self): return tuple(self._summaries) def _assert_checkpoint(testcase, global_step, input_units, hidden_units, output_units, model_dir): """Asserts checkpoint contains expected variables with proper shapes. Args: testcase: A TestCase instance. global_step: Expected global step value. input_units: The dimension of input layer. hidden_units: Iterable of integer sizes for the hidden layers. output_units: The dimension of output layer (logits). model_dir: The model directory. """ shapes = {name: shape for (name, shape) in tf.train.list_variables(model_dir)} # Global step. testcase.assertEqual([], shapes[tf.compat.v1.GraphKeys.GLOBAL_STEP]) testcase.assertEqual( global_step, tf.train.load_variable(model_dir, tf.compat.v1.GraphKeys.GLOBAL_STEP)) # Hidden layer weights. prev_layer_units = input_units for i in range(len(hidden_units)): layer_units = hidden_units[i] testcase.assertAllEqual((prev_layer_units, layer_units), shapes[HIDDEN_WEIGHTS_NAME_PATTERN % i]) testcase.assertAllEqual((layer_units,), shapes[HIDDEN_BIASES_NAME_PATTERN % i]) prev_layer_units = layer_units # Output layer weights. testcase.assertAllEqual((prev_layer_units, output_units), shapes[LOGITS_WEIGHTS_NAME]) testcase.assertAllEqual((output_units,), shapes[LOGITS_BIASES_NAME]) def _assert_simple_summary(testcase, expected_values, actual_summary): """Assert summary the specified simple values. Args: testcase: A TestCase instance. expected_values: Dict of expected tags and simple values. actual_summary: `summary_pb2.Summary`. """ testcase.assertAllClose( expected_values, { v.tag: v.simple_value for v in actual_summary.value if (v.tag in expected_values) }) class BaseDNNClassifierTrainTest(object): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tensorflow_estimator/python/estimator/canned/v1/dnn_testing_utils_v1.py [1573:1710]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._dnn_regressor_fn = dnn_regressor_fn self._fc_impl = fc_impl def setUp(self): self._model_dir = tempfile.mkdtemp() def tearDown(self): if self._model_dir: tf.compat.v1.summary.FileWriterCache.clear() shutil.rmtree(self._model_dir) def test_one_dim(self): """Asserts predictions for one-dimensional input and logits.""" # Create checkpoint: num_inputs=1, hidden_units=(2, 2), num_outputs=1. create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), global_step=0, model_dir=self._model_dir) dnn_regressor = self._dnn_regressor_fn( hidden_units=(2, 2), feature_columns=(self._fc_impl.numeric_column('x'),), model_dir=self._model_dir) input_fn = numpy_io.numpy_input_fn( x={'x': np.array([[10.]])}, batch_size=1, shuffle=False) # Uses identical numbers as DNNModelTest.test_one_dim_logits. # See that test for calculation of logits. # logits = [[-2.08]] => predictions = [-2.08]. self.assertAllClose({ prediction_keys.PredictionKeys.PREDICTIONS: [-2.08], }, next(dnn_regressor.predict(input_fn=input_fn))) def test_multi_dim(self): """Asserts predictions for multi-dimensional input and logits.""" # Create checkpoint: num_inputs=2, hidden_units=(2, 2), num_outputs=3. create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), 100, self._model_dir) dnn_regressor = self._dnn_regressor_fn( hidden_units=(2, 2), feature_columns=(self._fc_impl.numeric_column('x', shape=(2,)),), label_dimension=3, model_dir=self._model_dir) input_fn = numpy_io.numpy_input_fn( # Inputs shape is (batch_size, num_inputs). x={'x': np.array([[10., 8.]])}, batch_size=1, shuffle=False) # Uses identical numbers as # DNNModelFnTest.test_multi_dim_input_multi_dim_logits. # See that test for calculation of logits. # logits = [[-0.48, 0.48, 0.39]] => predictions = [-0.48, 0.48, 0.39] self.assertAllClose( { prediction_keys.PredictionKeys.PREDICTIONS: [-0.48, 0.48, 0.39], }, next(dnn_regressor.predict(input_fn=input_fn))) class _SummaryHook(tf.compat.v1.train.SessionRunHook): """Saves summaries every N steps.""" def __init__(self): self._summaries = [] def begin(self): self._summary_op = tf.compat.v1.summary.merge_all() def before_run(self, run_context): return tf.compat.v1.train.SessionRunArgs({'summary': self._summary_op}) def after_run(self, run_context, run_values): s = tf.compat.v1.summary.Summary() s.ParseFromString(run_values.results['summary']) self._summaries.append(s) def summaries(self): return tuple(self._summaries) def _assert_checkpoint(testcase, global_step, input_units, hidden_units, output_units, model_dir): """Asserts checkpoint contains expected variables with proper shapes. Args: testcase: A TestCase instance. global_step: Expected global step value. input_units: The dimension of input layer. hidden_units: Iterable of integer sizes for the hidden layers. output_units: The dimension of output layer (logits). model_dir: The model directory. """ shapes = {name: shape for (name, shape) in tf.train.list_variables(model_dir)} # Global step. testcase.assertEqual([], shapes[tf.compat.v1.GraphKeys.GLOBAL_STEP]) testcase.assertEqual( global_step, tf.train.load_variable(model_dir, tf.compat.v1.GraphKeys.GLOBAL_STEP)) # Hidden layer weights. prev_layer_units = input_units for i in range(len(hidden_units)): layer_units = hidden_units[i] testcase.assertAllEqual((prev_layer_units, layer_units), shapes[HIDDEN_WEIGHTS_NAME_PATTERN % i]) testcase.assertAllEqual((layer_units,), shapes[HIDDEN_BIASES_NAME_PATTERN % i]) prev_layer_units = layer_units # Output layer weights. testcase.assertAllEqual((prev_layer_units, output_units), shapes[LOGITS_WEIGHTS_NAME]) testcase.assertAllEqual((output_units,), shapes[LOGITS_BIASES_NAME]) def _assert_simple_summary(testcase, expected_values, actual_summary): """Assert summary the specified simple values. Args: testcase: A TestCase instance. expected_values: Dict of expected tags and simple values. actual_summary: `summary_pb2.Summary`. """ testcase.assertAllClose( expected_values, { v.tag: v.simple_value for v in actual_summary.value if (v.tag in expected_values) }) class BaseDNNClassifierTrainTest(object): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -