tensorflow_estimator/python/estimator/canned/dnn_testing_utils.py [245:502]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._dnn_model_fn = dnn_model_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_logits(self, mode, hidden_units, logits_dimension, inputs, expected_logits): """Tests that the expected logits are passed to mock head.""" with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={'age': tf.constant(inputs)}, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ self._fc_impl.numeric_column( 'age', shape=np.array(inputs).shape[1:]) ], optimizer=mock_optimizer(self, hidden_units)) with tf.compat.v1.train.MonitoredTrainingSession( checkpoint_dir=self._model_dir) as sess: if mode == ModeKeys.TRAIN: sess.run(estimator_spec.train_op) elif mode == ModeKeys.EVAL: sess.run(estimator_spec.loss) elif mode == ModeKeys.PREDICT: sess.run(estimator_spec.predictions) else: self.fail('Invalid mode: {}'.format(mode)) def test_one_dim_logits(self): """Tests one-dimensional logits. input_layer = [[10]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]] = [[relu(2.38), relu(-0.12)]] = [[2.38, 0]] logits = [[-1*2.38 +1*0 +0.3]] = [[-2.08]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=1, inputs=[[10.]], expected_logits=[[-2.08]]) def test_multi_dim_logits(self): """Tests multi-dimensional logits. input_layer = [[10]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]] = [[relu(2.38), relu(-0.12)]] = [[2.38, 0]] logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38]] = [[-2.08, 2.08, 1.19]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10.]], expected_logits=[[-2.08, 2.08, 1.19]]) def test_multi_example_multi_dim_logits(self): """Tests multiple examples and multi-dimensional logits. input_layer = [[10], [5]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)], [relu(0.6*5 +0.1), relu(0.5*5 -0.1)]] = [[6.1, 4.9], [3.1, 2.4]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)], [relu(1*3.1 -0.8*2.4 +0.2), relu(0.8*3.1 -1*2.4 -0.1)]] = [[2.38, 0], [1.38, 0]] logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38], [-1*1.38 +0.3, 1*1.38 -0.3, 0.5*1.38]] = [[-2.08, 2.08, 1.19], [-1.08, 1.08, 0.69]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10.], [5.]], expected_logits=[[-2.08, 2.08, 1.19], [-1.08, 1.08, .69]]) def test_multi_dim_input_one_dim_logits(self): """Tests multi-dimensional inputs and one-dimensional logits. input_layer = [[10, 8]] hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]] = [[1.3, 0.9]] hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]] = [[0.78, relu(-0.06)]] = [[0.78, 0]] logits = [[-1*0.78 +1*0 +0.3]] = [[-0.48]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=1, inputs=[[10., 8.]], expected_logits=[[-0.48]]) def test_multi_dim_input_multi_dim_logits(self): """Tests multi-dimensional inputs and multi-dimensional logits. input_layer = [[10, 8]] hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]] = [[1.3, 0.9]] hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]] = [[0.78, relu(-0.06)]] = [[0.78, 0]] logits = [[-1*0.78 + 0.3, 1*0.78 -0.3, 0.5*0.78]] = [[-0.48, 0.48, 0.39]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10., 8.]], expected_logits=[[-0.48, 0.48, 0.39]]) def test_multi_feature_column_multi_dim_logits(self): """Tests multiple feature columns and multi-dimensional logits. All numbers are the same as test_multi_dim_input_multi_dim_logits. The only difference is that the input consists of two 1D feature columns, instead of one 2D feature column. """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) hidden_units = (2, 2) logits_dimension = 3 inputs = ([[10.]], [[8.]]) expected_logits = [[-0.48, 0.48, 0.39]] for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={ 'age': tf.constant(inputs[0]), 'height': tf.constant(inputs[1]) }, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ self._fc_impl.numeric_column('age'), self._fc_impl.numeric_column('height') ], optimizer=mock_optimizer(self, hidden_units)) with tf.compat.v1.train.MonitoredTrainingSession( checkpoint_dir=self._model_dir) as sess: if mode == ModeKeys.TRAIN: sess.run(estimator_spec.train_op) elif mode == ModeKeys.EVAL: sess.run(estimator_spec.loss) elif mode == ModeKeys.PREDICT: sess.run(estimator_spec.predictions) else: self.fail('Invalid mode: {}'.format(mode)) def test_multi_feature_column_mix_multi_dim_logits(self): """Tests multiple feature columns and multi-dimensional logits. All numbers are the same as test_multi_dim_input_multi_dim_logits. The only difference is that the input consists of two 1D feature columns, instead of one 2D feature column. """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) hidden_units = (2, 2) logits_dimension = 3 inputs = ([[10.]], [[8.]]) expected_logits = [[-0.48, 0.48, 0.39]] for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={ 'age': tf.constant(inputs[0]), 'height': tf.constant(inputs[1]) }, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tensorflow_estimator/python/estimator/canned/v1/dnn_testing_utils_v1.py [244:501]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._dnn_model_fn = dnn_model_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_logits(self, mode, hidden_units, logits_dimension, inputs, expected_logits): """Tests that the expected logits are passed to mock head.""" with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={'age': tf.constant(inputs)}, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ self._fc_impl.numeric_column( 'age', shape=np.array(inputs).shape[1:]) ], optimizer=mock_optimizer(self, hidden_units)) with tf.compat.v1.train.MonitoredTrainingSession( checkpoint_dir=self._model_dir) as sess: if mode == ModeKeys.TRAIN: sess.run(estimator_spec.train_op) elif mode == ModeKeys.EVAL: sess.run(estimator_spec.loss) elif mode == ModeKeys.PREDICT: sess.run(estimator_spec.predictions) else: self.fail('Invalid mode: {}'.format(mode)) def test_one_dim_logits(self): """Tests one-dimensional logits. input_layer = [[10]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]] = [[relu(2.38), relu(-0.12)]] = [[2.38, 0]] logits = [[-1*2.38 +1*0 +0.3]] = [[-2.08]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=1, inputs=[[10.]], expected_logits=[[-2.08]]) def test_multi_dim_logits(self): """Tests multi-dimensional logits. input_layer = [[10]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]] = [[relu(2.38), relu(-0.12)]] = [[2.38, 0]] logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38]] = [[-2.08, 2.08, 1.19]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10.]], expected_logits=[[-2.08, 2.08, 1.19]]) def test_multi_example_multi_dim_logits(self): """Tests multiple examples and multi-dimensional logits. input_layer = [[10], [5]] hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)], [relu(0.6*5 +0.1), relu(0.5*5 -0.1)]] = [[6.1, 4.9], [3.1, 2.4]] hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)], [relu(1*3.1 -0.8*2.4 +0.2), relu(0.8*3.1 -1*2.4 -0.1)]] = [[2.38, 0], [1.38, 0]] logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38], [-1*1.38 +0.3, 1*1.38 -0.3, 0.5*1.38]] = [[-2.08, 2.08, 1.19], [-1.08, 1.08, 0.69]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10.], [5.]], expected_logits=[[-2.08, 2.08, 1.19], [-1.08, 1.08, .69]]) def test_multi_dim_input_one_dim_logits(self): """Tests multi-dimensional inputs and one-dimensional logits. input_layer = [[10, 8]] hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]] = [[1.3, 0.9]] hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]] = [[0.78, relu(-0.06)]] = [[0.78, 0]] logits = [[-1*0.78 +1*0 +0.3]] = [[-0.48]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1.], [1.]], [.3]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=1, inputs=[[10., 8.]], expected_logits=[[-0.48]]) def test_multi_dim_input_multi_dim_logits(self): """Tests multi-dimensional inputs and multi-dimensional logits. input_layer = [[10, 8]] hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]] = [[1.3, 0.9]] hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]] = [[0.78, relu(-0.06)]] = [[0.78, 0]] logits = [[-1*0.78 + 0.3, 1*0.78 -0.3, 0.5*0.78]] = [[-0.48, 0.48, 0.39]] """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: self._test_logits( mode, hidden_units=(2, 2), logits_dimension=3, inputs=[[10., 8.]], expected_logits=[[-0.48, 0.48, 0.39]]) def test_multi_feature_column_multi_dim_logits(self): """Tests multiple feature columns and multi-dimensional logits. All numbers are the same as test_multi_dim_input_multi_dim_logits. The only difference is that the input consists of two 1D feature columns, instead of one 2D feature column. """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) hidden_units = (2, 2) logits_dimension = 3 inputs = ([[10.]], [[8.]]) expected_logits = [[-0.48, 0.48, 0.39]] for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={ 'age': tf.constant(inputs[0]), 'height': tf.constant(inputs[1]) }, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ self._fc_impl.numeric_column('age'), self._fc_impl.numeric_column('height') ], optimizer=mock_optimizer(self, hidden_units)) with tf.compat.v1.train.MonitoredTrainingSession( checkpoint_dir=self._model_dir) as sess: if mode == ModeKeys.TRAIN: sess.run(estimator_spec.train_op) elif mode == ModeKeys.EVAL: sess.run(estimator_spec.loss) elif mode == ModeKeys.PREDICT: sess.run(estimator_spec.predictions) else: self.fail('Invalid mode: {}'.format(mode)) def test_multi_feature_column_mix_multi_dim_logits(self): """Tests multiple feature columns and multi-dimensional logits. All numbers are the same as test_multi_dim_input_multi_dim_logits. The only difference is that the input consists of two 1D feature columns, instead of one 2D feature column. """ base_global_step = 100 create_checkpoint(( ([[.6, .5], [-.6, -.5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]), ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), ), base_global_step, self._model_dir) hidden_units = (2, 2) logits_dimension = 3 inputs = ([[10.]], [[8.]]) expected_logits = [[-0.48, 0.48, 0.39]] for mode in [ModeKeys.TRAIN, ModeKeys.EVAL, ModeKeys.PREDICT]: with tf.Graph().as_default(): tf.compat.v1.train.create_global_step() head = mock_head( self, hidden_units=hidden_units, logits_dimension=logits_dimension, expected_logits=expected_logits) estimator_spec = self._dnn_model_fn( features={ 'age': tf.constant(inputs[0]), 'height': tf.constant(inputs[1]) }, labels=tf.constant([[1]]), mode=mode, head=head, hidden_units=hidden_units, feature_columns=[ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -