def test_summary(self): class ToString(object): def __init__(self): self.contents = '' def __call__(self, msg): self.contents += msg + '\n' # Single-io model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=4, use_bn=True, use_dp=True) model(np.ones((3, 4))) # need to build model first print_fn = ToString() model.summary(print_fn=print_fn) self.assertIn('Trainable params: 356', print_fn.contents) # Multi-io model = model_util.get_multi_io_subclass_model( num_classes=(5, 6), use_bn=True, use_dp=True) model([np.ones((3, 4)), np.ones((3, 4))]) # need to build model first print_fn = ToString() model.summary(print_fn=print_fn) self.assertIn('Trainable params: 587', print_fn.contents) # Single-io with unused layer model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=4, use_bn=True, use_dp=True) model.unused_layer = keras.layers.Dense(10) model(np.ones((3, 4))) # need to build model first print_fn = ToString() model.summary(print_fn=print_fn) self.assertIn('Trainable params: 356', print_fn.contents) self.assertIn('0 (unused)', print_fn.contents)
def __init__(self, num_classes=2): super(NestedTestModel1, self).__init__(name='nested_model_1') self.num_classes = num_classes self.dense1 = keras.layers.Dense(32, activation='relu') self.dense2 = keras.layers.Dense(num_classes, activation='relu') self.bn = keras.layers.BatchNormalization() self.test_net = testing_utils.SmallSubclassMLP(num_hidden=32, num_classes=4, use_bn=True, use_dp=True)
def test_trainable_custom_model_false(self): """Tests that overall False trainable status of Model is preserved.""" # Set all layers to *not* be trainable. model = testing_utils.SmallSubclassMLP(1, 4, trainable=False) model.compile(loss='mse', optimizer='rmsprop') self._train_model(model, use_dataset=False) loaded = self._save_and_load(model) self._test_evaluation(model, loaded) self.assertEmpty(model.trainable_variables) self.assertEmpty(loaded.trainable_variables)
def test_invalid_input_shape_build(self): num_classes = 2 input_dim = 50 model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=num_classes, use_dp=True, use_bn=True) self.assertFalse(model.built, 'Model should not have been built') self.assertFalse(model.weights, ('Model should have no weights since it ' 'has not been built.')) with self.assertRaisesRegex(ValueError, 'input shape is not one of the valid types'): model.build(input_shape=tf.compat.v1.Dimension(input_dim))
def test_single_io_workflow_with_tensors(self): num_classes = 2 num_samples = 10 input_dim = 50 with tf.Graph().as_default(), self.cached_session(): model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=num_classes, use_dp=True, use_bn=True) model.compile(loss='mse', optimizer='rmsprop') x = tf.ones((num_samples, input_dim)) y = tf.zeros((num_samples, num_classes)) model.fit(x, y, epochs=2, steps_per_epoch=10, verbose=0) _ = model.evaluate(steps=10, verbose=0)
def test_single_io_dimension_subclass_build(self): num_classes = 2 input_dim = tf.compat.v1.Dimension(50) batch_size = tf.compat.v1.Dimension(None) model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=num_classes, use_dp=True, use_bn=True) self.assertFalse(model.built, 'Model should not have been built') self.assertFalse(model.weights, ('Model should have no weights since it ' 'has not been built.')) model.build(input_shape=(batch_size, input_dim)) self.assertTrue(model.weights, ('Model should have weights now that it ' 'has been properly built.')) self.assertTrue(model.built, 'Model should be built after calling `build`.') model(tf.ones((32, input_dim)))
def test_single_io_workflow_with_np_arrays(self): num_classes = 2 num_samples = 100 input_dim = 50 model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=num_classes, use_dp=True, use_bn=True) model.compile( loss='mse', optimizer='rmsprop', metrics=['acc', keras.metrics.CategoricalAccuracy()], run_eagerly=testing_utils.should_run_eagerly()) x = np.ones((num_samples, input_dim)) y = np.zeros((num_samples, num_classes)) model.fit(x, y, epochs=2, batch_size=32, verbose=0) _ = model.evaluate(x, y, verbose=0)
def test_single_io_workflow_with_datasets(self): num_classes = 2 num_samples = 10 input_dim = 50 with self.cached_session(): model = testing_utils.SmallSubclassMLP( num_hidden=32, num_classes=num_classes, use_dp=True, use_bn=True) model.compile( loss='mse', optimizer='rmsprop', run_eagerly=testing_utils.should_run_eagerly()) x = np.ones((num_samples, input_dim), dtype=np.float32) y = np.zeros((num_samples, num_classes), dtype=np.float32) dataset = tf.data.Dataset.from_tensor_slices((x, y)) dataset = dataset.repeat(100) dataset = dataset.batch(10) model.fit(dataset, epochs=2, steps_per_epoch=10, verbose=0) _ = model.evaluate(dataset, steps=10, verbose=0)