def testCompositeTypeSpecArgWithoutDtype(self):
for assign_variant_dtype in [False, True]:
# Create a Keras Input
spec = TwoTensorsSpecNoOneDtype(
(1, 2, 3),
tf.float32,
(1, 2, 3),
tf.int64,
assign_variant_dtype=assign_variant_dtype,
)
x = input_layer_lib.Input(type_spec=spec)
def lambda_fn(tensors):
return tf.cast(tensors.x, tf.float64) + tf.cast(
tensors.y, tf.float64)
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, core.Lambda(lambda_fn)(x))
# And that the model works
two_tensors = TwoTensors(
tf.ones((1, 2, 3)) * 2.0, tf.ones(1, 2, 3))
self.assertAllEqual(model(two_tensors), lambda_fn(two_tensors))
# Test serialization / deserialization
model = functional.Functional.from_config(model.get_config())
self.assertAllEqual(model(two_tensors), lambda_fn(two_tensors))
model = model_config.model_from_json(model.to_json())
self.assertAllEqual(model(two_tensors), lambda_fn(two_tensors))
def testInputTensorArg(self):
# Create a Keras Input
x = input_layer_lib.Input(tensor=tf.zeros((7, 32)))
self.assertAllEqual(x.shape.as_list(), [7, 32])
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2.0)
self.assertAllEqual(model(tf.ones(x.shape)), tf.ones(x.shape) * 2.0)
def testBasicOutputShapeWithBatchSize(self):
# Create a Keras Input
x = input_layer_lib.Input(batch_size=6, shape=(32, ), name="input_b")
self.assertAllEqual(x.shape.as_list(), [6, 32])
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2.0)
self.assertAllEqual(model(tf.ones(x.shape)), tf.ones(x.shape) * 2.0)
def testBasicOutputShapeNoBatchSize(self):
# Create a Keras Input
x = input_layer_lib.Input(shape=(32, ), name='input_a')
self.assertAllEqual(x.shape.as_list(), [None, 32])
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2.0)
self.assertAllEqual(model(tf.ones((3, 32))), tf.ones((3, 32)) * 2.0)
def run_model(inp):
if not model_container:
# Create a Keras Input
x = input_layer_lib.Input(tensor=tf.zeros((10, 16)))
self.assertAllEqual(x.shape.as_list(), [10, 16])
# Verify you can construct and use a model w/ this input
model_container["model"] = functional.Functional(x, x * 3.0)
return model_container["model"](inp)
def run_model(inp):
nonlocal model
if not model:
# Create a Keras Input
x = input_layer_lib.Input(shape=(8, ), name="input_a")
self.assertAllEqual(x.shape.as_list(), [None, 8])
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2.0)
return model(inp)
def run_model(inp):
if not model_container:
# Create a Keras Input
x = input_layer_lib.Input(
type_spec=tf.TensorSpec((10, 16), tf.float32))
self.assertAllEqual(x.shape.as_list(), [10, 16])
# Verify you can construct and use a model w/ this input
model_container['model'] = functional.Functional(x, x * 3.0)
return model_container['model'](inp)
def run_model(inp):
if not model_container:
# Create a Keras Input
rt = tf.RaggedTensor.from_row_splits(
values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])
x = input_layer_lib.Input(type_spec=rt._type_spec)
# Verify you can construct and use a model w/ this input
model_container['model'] = functional.Functional(x, x * 3)
return model_container['model'](inp)
def testCompositeInputTensorArg(self):
# Create a Keras Input
rt = tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6],
row_splits=[0, 4, 4, 7, 8, 8])
x = input_layer_lib.Input(tensor=rt)
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2)
# And that the model works
rt = tf.RaggedTensor.from_row_splits(values=[3, 21, 4, 1, 53, 9, 2, 6],
row_splits=[0, 4, 4, 7, 8, 8])
self.assertAllEqual(model(rt), rt * 2)
def testTypeSpecArg(self):
# Create a Keras Input
x = input_layer_lib.Input(type_spec=tf.TensorSpec((7, 32), tf.float32))
self.assertAllEqual(x.shape.as_list(), [7, 32])
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2.0)
self.assertAllEqual(model(tf.ones(x.shape)), tf.ones(x.shape) * 2.0)
# Test serialization / deserialization
model = functional.Functional.from_config(model.get_config())
self.assertAllEqual(model(tf.ones(x.shape)), tf.ones(x.shape) * 2.0)
model = model_config.model_from_json(model.to_json())
self.assertAllEqual(model(tf.ones(x.shape)), tf.ones(x.shape) * 2.0)
def testCompositeTypeSpecArg(self):
# Create a Keras Input
rt = tf.RaggedTensor.from_row_splits(values=[3, 1, 4, 1, 5, 9, 2, 6],
row_splits=[0, 4, 4, 7, 8, 8])
x = input_layer_lib.Input(type_spec=rt._type_spec)
# Verify you can construct and use a model w/ this input
model = functional.Functional(x, x * 2)
# And that the model works
rt = tf.RaggedTensor.from_row_splits(values=[3, 21, 4, 1, 53, 9, 2, 6],
row_splits=[0, 4, 4, 7, 8, 8])
self.assertAllEqual(model(rt), rt * 2)
# Test serialization / deserialization
model = functional.Functional.from_config(model.get_config())
self.assertAllEqual(model(rt), rt * 2)
model = model_config.model_from_json(model.to_json())
self.assertAllEqual(model(rt), rt * 2)