def test_dataset_input_shape_validation(self):
with tf.compat.v1.get_default_graph().as_default(
), self.cached_session():
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
model.compile(optimizer='rmsprop', loss='mse')
# User forgets to batch the dataset
inputs = np.zeros((10, 3))
targets = np.zeros((10, 4))
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
with self.assertRaisesRegex(
ValueError,
r'expected (.*?) to have shape \(3,\) but got array with shape \(1,\)'
):
model.train_on_batch(dataset)
# Wrong input shape
inputs = np.zeros((10, 5))
targets = np.zeros((10, 4))
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
with self.assertRaisesRegex(
ValueError, r'expected (.*?) to have shape \(3,\)'):
model.train_on_batch(dataset)
def test_dataset_with_class_weight(self):
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
model.compile('rmsprop', 'mse')
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
class_weight_np = np.array([0.25, 0.25, 0.25, 0.25])
class_weight = dict(enumerate(class_weight_np))
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=1,
class_weight=class_weight)
def test_load_non_keras_saved_model(self):
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
saved_model_dir = self._save_model_dir()
tf.saved_model.save(model, saved_model_dir)
with self.assertRaisesRegex(ValueError, 'Unable to create a Keras model'):
keras_load.load(saved_model_dir)
