def testCallOnPlaceHolder(self):
inputs = array_ops.placeholder(dtype=dtypes.float32)
dense = core_layers.Dense(4, name='my_dense')
with self.assertRaises(ValueError):
dense(inputs)
inputs = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, None])
dense = core_layers.Dense(4, name='my_dense')
with self.assertRaises(ValueError):
dense(inputs)
inputs = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, None, None])
dense = core_layers.Dense(4, name='my_dense')
with self.assertRaises(ValueError):
dense(inputs)
inputs = array_ops.placeholder(dtype=dtypes.float32, shape=[None, 3])
dense = core_layers.Dense(4, name='my_dense')
dense(inputs)
inputs = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, None, 3])
dense = core_layers.Dense(4, name='my_dense')
dense(inputs)
def test_count_params(self):
dense = core_layers.Dense(16)
dense.build((None, 4))
self.assertEqual(dense.count_params(), 16 * 4 + 16)
dense = core_layers.Dense(16)
with self.assertRaises(ValueError):
dense.count_params()
def testActivation(self):
dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1')
inputs = random_ops.random_uniform((5, 3), seed=1)
outputs = dense(inputs)
if not context.executing_eagerly():
self.assertEqual(outputs.op.name, 'dense1/Relu')
dense = core_layers.Dense(2, name='dense2')
inputs = random_ops.random_uniform((5, 3), seed=1)
outputs = dense(inputs)
if not context.executing_eagerly():
self.assertEqual(outputs.op.name, 'dense2/BiasAdd')
def testVariableInput(self):
with self.cached_session():
v = variable_scope.get_variable(
'X', initializer=init_ops.zeros_initializer(), shape=(1, 1))
x = core_layers.Dense(1)(v)
self.evaluate(variables.global_variables_initializer())
self.assertAllEqual(x, [[0.0]])
def testDenseProperties(self):
dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')
self.assertEqual(dense.units, 2)
self.assertEqual(dense.activation, nn_ops.relu)
self.assertEqual(dense.kernel_regularizer, None)
self.assertEqual(dense.bias_regularizer, None)
self.assertEqual(dense.activity_regularizer, None)
self.assertEqual(dense.use_bias, True)
# Test auto-naming
dense = core_layers.Dense(2, activation=nn_ops.relu)
dense.apply(random_ops.random_uniform((5, 2)))
self.assertEqual(dense.name, 'dense_1')
dense = core_layers.Dense(2, activation=nn_ops.relu)
dense.apply(random_ops.random_uniform((5, 2)))
self.assertEqual(dense.name, 'dense_2')
def testBiasRegularizer(self): regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3 dense = core_layers.Dense(2, name='my_dense', bias_regularizer=regularizer) inputs = random_ops.random_uniform((5, 3), seed=1) _ = dense(inputs) loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) self.assertEqual(len(loss_keys), 1) self.evaluate([v.initializer for v in dense.variables]) self.assertAllEqual(self.evaluate(dense.losses), self.evaluate(loss_keys))
def testActivityRegularizer(self):
regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
dense = core_layers.Dense(
2, name='my_dense', activity_regularizer=regularizer)
inputs = random_ops.random_uniform((5, 3), seed=1)
_ = dense(inputs)
loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
self.assertEqual(len(loss_keys), 1)
self.assertListEqual(dense.losses, loss_keys)
def testConstraints(self):
k_constraint = lambda x: x / math_ops.reduce_sum(x)
b_constraint = lambda x: x / math_ops.reduce_max(x)
dense = core_layers.Dense(2,
kernel_constraint=k_constraint,
bias_constraint=b_constraint)
inputs = random_ops.random_uniform((5, 3), seed=1)
dense(inputs)
self.assertEqual(dense.kernel_constraint, k_constraint)
self.assertEqual(dense.bias_constraint, b_constraint)
def testNonTrainable(self):
dense = core_layers.Dense(2, trainable=False, name='my_dense')
inputs = random_ops.random_uniform((5, 2), seed=1)
_ = dense(inputs)
self.assertListEqual(dense.variables, [dense.kernel, dense.bias])
self.assertListEqual(dense.non_trainable_variables,
[dense.kernel, dense.bias])
self.assertListEqual(dense.trainable_variables, [])
if not context.executing_eagerly():
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0)
def testOutputShape(self):
dense = core_layers.Dense(7, activation=nn_ops.relu, name='my_dense')
inputs = random_ops.random_uniform((5, 3), seed=1)
outputs = dense.apply(inputs)
self.assertEqual(outputs.get_shape().as_list(), [5, 7])
inputs = random_ops.random_uniform((5, 2, 3), seed=1)
outputs = dense(inputs)
self.assertEqual(outputs.get_shape().as_list(), [5, 2, 7])
inputs = random_ops.random_uniform((1, 2, 4, 3), seed=1)
outputs = dense.apply(inputs)
self.assertEqual(outputs.get_shape().as_list(), [1, 2, 4, 7])
def testCall(self):
dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')
inputs = random_ops.random_uniform((5, 4), seed=1)
outputs = dense(inputs)
self.assertListEqual([5, 2], outputs.get_shape().as_list())
self.assertListEqual(dense.variables, [dense.kernel, dense.bias])
self.assertListEqual(dense.trainable_variables,
[dense.kernel, dense.bias])
self.assertListEqual(dense.non_trainable_variables, [])
if not context.executing_eagerly():
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(dense.kernel.name, 'my_dense/kernel:0')
self.assertEqual(dense.bias.name, 'my_dense/bias:0')
def testComputeOutputShape(self):
dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1')
ts = tensor_shape.TensorShape
# pylint: disable=protected-access
with self.assertRaises(ValueError):
dense.compute_output_shape(ts(None))
with self.assertRaises(ValueError):
dense.compute_output_shape(ts([]))
with self.assertRaises(ValueError):
dense.compute_output_shape(ts([1]))
self.assertEqual([None, 2],
dense.compute_output_shape((None, 3)).as_list())
self.assertEqual([None, 2],
dense.compute_output_shape(ts([None, 3])).as_list())
self.assertEqual([None, 4, 2],
dense.compute_output_shape(ts([None, 4,
3])).as_list())
def testCallTensorDot(self):
dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')
inputs = random_ops.random_uniform((5, 4, 3), seed=1)
outputs = dense(inputs)
self.assertListEqual([5, 4, 2], outputs.get_shape().as_list())
def testNoEagerLeak(self):
# Tests that repeatedly constructing and building a Layer does not leak
# Python objects.
inputs = random_ops.random_uniform((5, 4), seed=1)
core_layers.Dense(5)(inputs)
core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')(inputs)
