def test_mixing_preprocessing_and_regular_layers(self):
x0 = Input(shape=(10, 10, 3))
x1 = Input(shape=(10, 10, 3))
x2 = Input(shape=(10, 10, 3))
y0 = merge.Add()([x0, x1])
y1 = image_preprocessing.CenterCrop(8, 8)(x2)
y1 = convolutional.ZeroPadding2D(padding=1)(y1)
z = merge.Add()([y0, y1])
z = normalization.Normalization()(z)
z = convolutional.Conv2D(4, 3)(z)
stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2], z)
data = [
np.ones((12, 10, 10, 3), dtype='float32'),
np.ones((12, 10, 10, 3), dtype='float32'),
np.ones((12, 10, 10, 3), dtype='float32')
]
stage.adapt(data)
_ = stage(data)
stage.compile('rmsprop', 'mse')
with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
stage.fit(data, np.ones((12, 8, 8, 4)))
ds_x0 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
ds_x1 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
ds_x2 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
ds_x = dataset_ops.Dataset.zip((ds_x0, ds_x1, ds_x2))
ds_y = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 8, 8, 4)))
dataset = dataset_ops.Dataset.zip((ds_x, ds_y)).batch(4)
with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
stage.fit(dataset)
_ = stage.evaluate(data, np.ones((12, 8, 8, 4)))
_ = stage.predict(data)
def test_export_multi_input_functional_keras_model(self):
x1 = input_layer.Input((2,), name="x1")
x2 = input_layer.Input((2,), name="x2")
y1 = core.Dense(4)(merge.Add()([x1, x2]))
y2 = core.Dense(4)(merge.Multiply()([x1, x2]))
model = training.Model([x1, x2], [y1, y2])
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
save.save(model, save_dir)
outputs = model([array_ops.ones([1, 2]), 2. * array_ops.ones([1, 2])])
self.assertAllClose(
{"dense": outputs[0], "dense_1": outputs[1]},
_import_and_infer(
save_dir,
{"x1": [[1., 1.]],
"x2": [[2., 2.]]}))