def testMultiIONetworkbuilding(self):
input_a = base_layers.Input(shape=(32, ))
input_b = base_layers.Input(shape=(16, ))
a = core_layers.Dense(16)(input_a)
class AddLayer(base_layers.Layer):
def call(self, inputs):
return inputs[0] + inputs[1]
def _compute_output_shape(self, input_shape):
return input_shape[0]
c = AddLayer()([a, input_b]) # pylint: disable=not-callable
c = core_layers.Dense(2)(c)
network = base_layers.Network([input_a, input_b], [a, c])
if context.in_eager_mode():
a_val = constant_op.constant(
np.random.random((10, 32)).astype('float32'))
b_val = constant_op.constant(
np.random.random((10, 16)).astype('float32'))
outputs = network([a_val, b_val])
self.assertEqual(len(outputs), 2)
self.assertEqual(outputs[0].shape.as_list(), [10, 16])
self.assertEqual(outputs[1].shape.as_list(), [10, 2])
def testCrossDataFlows(self):
# Test the ability to have multi-output layers with outputs that get routed
# to separate layers
class PowersLayer(base_layers.Layer):
def call(self, inputs):
return [inputs**2, inputs**3]
x = base_layers.Input(shape=(32,))
p1, p2 = PowersLayer()(x) # pylint: disable=not-callable
y1 = core_layers.Dense(2)(p1)
y2 = core_layers.Dense(3)(p2)
network = base_layers.Network(x, [y1, y2])
self.assertEqual(len(network.layers), 4) # InputLayer + 2 * Dense + PLayer
# Test callability.
x2 = base_layers.Input(shape=(32,))
outputs = network(x2)
self.assertEqual(type(outputs), list)
self.assertEqual(len(outputs), 2)
self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
self.assertEqual(outputs[1].get_shape().as_list(), [None, 3])
def testMaskingSingleInput(self):
class MaskedLayer(base_layers.Layer):
def call(self, inputs, mask=None):
if mask is not None:
return inputs * mask
return inputs
def compute_mask(self, inputs, mask=None):
return array_ops.ones_like(inputs)
if context.in_graph_mode():
x = base_layers.Input(shape=(32,))
y = MaskedLayer()(x) # pylint: disable=not-callable
network = base_layers.Network(x, y)
# test callability on Input
x_2 = base_layers.Input(shape=(32,))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 32])
# test callability on regular tensor
x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 32])
else:
a = constant_op.constant([2] * 32)
mask = constant_op.constant([0, 1] * 16)
a._keras_mask = mask
b = MaskedLayer().apply(a)
self.assertTrue(hasattr(b, '_keras_mask'))
self.assertAllEqual(self.evaluate(array_ops.ones_like(mask)),
self.evaluate(getattr(b, '_keras_mask')))
self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b))
def testBasicNetwork(self):
# minimum viable network
x = base_layers.Input(shape=(32,))
dense = core_layers.Dense(2)
y = dense(x)
network = base_layers.Network(x, y, name='dense_network')
# test basic attributes
self.assertEqual(network.name, 'dense_network')
self.assertEqual(len(network.layers), 2) # InputLayer + Dense
self.assertEqual(network.layers[1], dense)
self.assertEqual(network.weights, dense.weights)
self.assertEqual(network.trainable_weights, dense.trainable_weights)
self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights)
# test callability on Input
x_2 = base_layers.Input(shape=(32,))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 2])
# test callability on regular tensor
x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 2])
# test network `trainable` attribute
network.trainable = False
self.assertEqual(network.weights, dense.weights)
self.assertEqual(network.trainable_weights, [])
self.assertEqual(network.non_trainable_weights,
dense.trainable_weights + dense.non_trainable_weights)
def test_node_construction(self):
# test graph topology construction basics
a = base_layers.Input(shape=(32,), name='input_a')
b = base_layers.Input(shape=(32,), name='input_b')
self.assertListEqual(a.get_shape().as_list(), [None, 32])
a_layer, a_node_index, a_tensor_index = a._keras_history
b_layer, _, _ = b._keras_history
self.assertEqual(len(a_layer.inbound_nodes), 1)
self.assertEqual(a_tensor_index, 0)
node = a_layer.inbound_nodes[a_node_index]
self.assertEqual(node.outbound_layer, a_layer)
self.assertListEqual(node.inbound_layers, [])
self.assertListEqual(node.input_tensors, [a])
self.assertListEqual(node.input_shapes, [(None, 32)])
self.assertListEqual(node.output_tensors, [a])
self.assertListEqual(node.output_shapes, [(None, 32)])
dense = core_layers.Dense(16, name='dense_1')
dense(a)
dense(b)
self.assertEqual(len(dense.inbound_nodes), 2)
self.assertEqual(len(dense.outbound_nodes), 0)
self.assertListEqual(dense.inbound_nodes[0].inbound_layers, [a_layer])
self.assertEqual(dense.inbound_nodes[0].outbound_layer, dense)
self.assertListEqual(dense.inbound_nodes[1].inbound_layers, [b_layer])
self.assertEqual(dense.inbound_nodes[1].outbound_layer, dense)
self.assertListEqual(dense.inbound_nodes[0].input_tensors, [a])
self.assertListEqual(dense.inbound_nodes[1].input_tensors, [b])
# Test config
config_0 = dense.inbound_nodes[0].get_config()
self.assertEqual(config_0['outbound_layer'], dense.name)
def testTopologicalAttributesMultiInputLayer(self):
class AddLayer(base_layers.Layer):
def call(self, inputs):
assert len(inputs) == 2
return inputs[0] + inputs[1]
a = base_layers.Input(shape=(32, ))
b = base_layers.Input(shape=(32, ))
test_layer = AddLayer()
y = test_layer([a, b]) # pylint: disable=not-callable
self.assertEqual(test_layer.input, [a, b])
self.assertEqual(test_layer.output, y)
self.assertEqual(test_layer.input_shape, [(None, 32), (None, 32)])
self.assertEqual(test_layer.output_shape, (None, 32))
def test_using_tf_layers_in_keras_functional_model(self):
with self.test_session():
np.random.seed(1337)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=200,
test_samples=100,
input_shape=(10, ),
num_classes=2)
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)
inputs = tf_base_layers.Input(shape=(10, ))
x = tf_core_layers.Dense(32, activation=nn.relu)(inputs)
outputs = tf_core_layers.Dense(2, activation=nn.softmax)(x)
model = keras.models.Model(inputs, outputs)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
epochs=10,
batch_size=16,
validation_data=(x_test, y_test),
verbose=0)
self.assertGreater(history.history['val_acc'][-1], 0.85)
def testMultiOutputNetwork(self):
x = base_layers.Input(shape=(32,))
y1 = core_layers.Dense(2)(x)
y2 = core_layers.Dense(3)(x)
network = base_layers.Network(x, [y1, y2])
self.assertEqual(len(network.layers), 3) # InputLayer + 2 * Dense
# Test callability.
x2 = base_layers.Input(shape=(32,))
outputs = network(x2)
self.assertEqual(type(outputs), list)
self.assertEqual(len(outputs), 2)
self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
self.assertEqual(outputs[1].get_shape().as_list(), [None, 3])
def test_get_losses_for(self):
a = base_layers.Input(shape=(2,))
dense_layer = core_layers.Dense(1)
dense_layer.add_loss(0, inputs=a)
dense_layer.add_loss(1, inputs=None)
self.assertListEqual(dense_layer.get_losses_for(a), [0])
self.assertListEqual(dense_layer.get_losses_for(None), [1])
def testNetworkRecursion(self):
# test the ability of networks to be used as layers inside networks.
a = base_layers.Input(shape=(32,))
b = core_layers.Dense(2)(a)
net = base_layers.Network(a, b)
c = base_layers.Input(shape=(32,))
d = net(c)
recursive_net = base_layers.Network(c, d)
self.assertEqual(len(recursive_net.layers), 2)
self.assertEqual(recursive_net.layers[1], net)
self.assertEqual(len(recursive_net.weights), 2)
# test callability
x = array_ops.placeholder(dtype='float32', shape=(None, 32))
y = recursive_net(x)
self.assertEqual(y.get_shape().as_list(), [None, 2])
def testMultiInputNetwork(self):
a = base_layers.Input(shape=(32, ), name='input_a')
b = base_layers.Input(shape=(32, ), name='input_b')
class AddLayer(base_layers.Layer):
def call(self, inputs):
assert len(inputs) == 2
return inputs[0] + inputs[1]
c = AddLayer()([a, b]) # pylint: disable=not-callable
network = base_layers.Network([a, b], c)
self.assertEqual(len(network.layers), 3) # 2 * InputLayer + AddLayer
# Test callability.
a2 = base_layers.Input(shape=(32, ))
b2 = base_layers.Input(shape=(32, ))
c2 = network([a2, b2])
self.assertEqual(c2.get_shape().as_list(), [None, 32])
def testMultiInputMultiOutputNetworkSharedLayer(self):
a = base_layers.Input(shape=(32,), name='input_a')
b = base_layers.Input(shape=(32,), name='input_b')
dense = core_layers.Dense(2)
y1 = dense(a)
y2 = dense(b)
network = base_layers.Network([a, b], [y1, y2])
self.assertEqual(len(network.layers), 3) # 2 * InputLayer + Dense
# Test callability.
a2 = base_layers.Input(shape=(32,))
b2 = base_layers.Input(shape=(32,))
outputs = network([a2, b2])
self.assertEqual(type(outputs), list)
self.assertEqual(len(outputs), 2)
self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
self.assertEqual(outputs[1].get_shape().as_list(), [None, 2])
def testSparseInput(self):
class SparseSoftmax(base_layers.Layer):
def call(self, inputs):
return sparse_ops.sparse_softmax(inputs)
x = base_layers.Input(shape=(32, ), sparse=True)
y = SparseSoftmax()(x) # pylint: disable=not-callable
network = base_layers.Network(x, y)
self.assertEqual(len(network.layers), 2)
self.assertEqual(network.layers[0].sparse, True)
def testTopologicalAttributesMultiOutputLayer(self):
class PowersLayer(base_layers.Layer):
def call(self, inputs):
return [inputs**2, inputs**3]
x = base_layers.Input(shape=(32, ))
test_layer = PowersLayer()
p1, p2 = test_layer(x) # pylint: disable=not-callable
self.assertEqual(test_layer.input, x)
self.assertEqual(test_layer.output, [p1, p2])
self.assertEqual(test_layer.input_shape, (None, 32))
self.assertEqual(test_layer.output_shape, [(None, 32), (None, 32)])
def testMaskingSingleInput(self):
class MaskedLayer(base_layers.Layer):
def call(self, inputs, mask=None):
if mask is not None:
return inputs * mask
return inputs
def compute_mask(self, inputs, mask=None):
return array_ops.ones_like(inputs)
x = base_layers.Input(shape=(32, ))
y = MaskedLayer()(x) # pylint: disable=not-callable
network = base_layers.Network(x, y)
# test callability on Input
x_2 = base_layers.Input(shape=(32, ))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 32])
# test callability on regular tensor
x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
y_2 = network(x_2)
self.assertEqual(y_2.get_shape().as_list(), [None, 32])
def testTopologicalAttributes(self):
# test layer attributes / methods related to cross-layer connectivity.
a = base_layers.Input(shape=(32,), name='input_a')
b = base_layers.Input(shape=(32,), name='input_b')
# test input, output, input_shape, output_shape
test_layer = core_layers.Dense(16, name='test_layer')
a_test = test_layer(a)
self.assertEqual(test_layer.input, a)
self.assertEqual(test_layer.output, a_test)
self.assertEqual(test_layer.input_shape, (None, 32))
self.assertEqual(test_layer.output_shape, (None, 16))
# test `get_*_at` methods
dense = core_layers.Dense(16, name='dense_1')
a_2 = dense(a)
b_2 = dense(b)
self.assertEqual(dense.get_input_at(0), a)
self.assertEqual(dense.get_input_at(1), b)
self.assertEqual(dense.get_output_at(0), a_2)
self.assertEqual(dense.get_output_at(1), b_2)
self.assertEqual(dense.get_input_shape_at(0), (None, 32))
self.assertEqual(dense.get_input_shape_at(1), (None, 32))
self.assertEqual(dense.get_output_shape_at(0), (None, 16))
self.assertEqual(dense.get_output_shape_at(1), (None, 16))
# Test invalid value for attribute retrieval.
with self.assertRaises(ValueError):
dense.get_input_at(2)
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
_ = new_dense.input
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
_ = new_dense.output
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
_ = new_dense.output_shape
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
_ = new_dense.input_shape
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
a = base_layers.Input(shape=(3, 32))
a = base_layers.Input(shape=(5, 32))
a_2 = dense(a)
b_2 = dense(b)
_ = new_dense.input_shape
with self.assertRaises(AttributeError):
new_dense = core_layers.Dense(16)
a = base_layers.Input(shape=(3, 32))
a = base_layers.Input(shape=(5, 32))
a_2 = dense(a)
b_2 = dense(b)
_ = new_dense.output_shape
def testNetworkAttributes(self):
x = base_layers.Input(shape=(32,))
z = core_layers.Dense(2, kernel_regularizer=lambda x: 0.01 * (x**2))(x)
dense = core_layers.Dense(2, name='dense')
dense.add_update(1)
y = dense(z)
net = base_layers.Network(x, y)
# losses
self.assertEqual(len(net.losses), 1)
# updates
self.assertEqual(len(net.updates), 1)
# get_layer
self.assertEqual(net.get_layer('dense'), dense)
self.assertEqual(net.get_layer(index=2), dense)
with self.assertRaises(ValueError):
net.get_layer('dense_unknown')
with self.assertRaises(ValueError):
net.get_layer()
with self.assertRaises(ValueError):
net.get_layer(index=4)
# input, output
self.assertEqual(net.input, x)
self.assertEqual(net.output, y)
# input_shape, output_shape
self.assertEqual(net.input_shape, (None, 32))
self.assertEqual(net.output_shape, (None, 2))
# get_*_at
self.assertEqual(net.get_input_at(0), x)
self.assertEqual(net.get_output_at(0), y)
# _compute_output_shape
self.assertEqual(net._compute_output_shape((3, 32)).as_list(), [3, 2])
def testSimpleNetworkBuilding(self):
inputs = base_layers.Input(shape=(32, ))
if context.in_eager_mode():
self.assertIsInstance(inputs, base_layers._DeferredTensor)
self.assertEqual(inputs.dtype.name, 'float32')
self.assertEqual(inputs.shape.as_list(), [None, 32])
x = core_layers.Dense(2)(inputs)
if context.in_eager_mode():
self.assertIsInstance(x, base_layers._DeferredTensor)
self.assertEqual(x.dtype.name, 'float32')
self.assertEqual(x.shape.as_list(), [None, 2])
outputs = core_layers.Dense(4)(x)
network = base_layers.Network(inputs, outputs)
self.assertIsInstance(network, base_layers.Network)
if context.in_eager_mode():
# It should be possible to call such a network on EagerTensors.
inputs = constant_op.constant(
np.random.random((10, 32)).astype('float32'))
outputs = network(inputs)
self.assertEqual(outputs.shape.as_list(), [10, 4])
def testInvalidNetworks(self):
# redundant inputs
x = base_layers.Input(shape=(32,))
y = core_layers.Dense(2)(x)
with self.assertRaises(ValueError):
base_layers.Network([x, x], y)
# inputs that don't come from Input
x = array_ops.placeholder(dtype='float32', shape=(None, 32))
y = core_layers.Dense(2)(x)
with self.assertRaises(ValueError):
base_layers.Network(x, y)
# inputs that don't come from Input but have a layer history
x = base_layers.Input(shape=(32,))
x = core_layers.Dense(32)(x)
y = core_layers.Dense(2)(x)
with self.assertRaises(ValueError):
base_layers.Network(x, y)
# outputs that don't come from layers
x = base_layers.Input(shape=(32,))
y = core_layers.Dense(2)(x)
y = 2 * y
with self.assertRaises(ValueError):
base_layers.Network(x, y)
# disconnected graphs
x1 = base_layers.Input(shape=(32,))
x2 = base_layers.Input(shape=(32,))
y = core_layers.Dense(2)(x1)
with self.assertRaises(ValueError):
base_layers.Network(x2, y)
# redundant layer names
x = base_layers.Input(shape=(32,))
z = core_layers.Dense(2, name='dense')(x)
y = core_layers.Dense(2, name='dense')(z)
with self.assertRaises(ValueError):
base_layers.Network(x, y)
def testExplicitBatchSize(self): x = base_layers.Input(shape=(32,), batch_size=3) y = core_layers.Dense(2)(x) self.assertEqual(y.get_shape().as_list(), [3, 2])
def testInputTensorWrapping(self): x = array_ops.placeholder(dtype='float32', shape=(None, 32)) x = base_layers.Input(tensor=x) y = core_layers.Dense(2)(x) base_layers.Network(x, y)
