def test_noisy_dense_remove_noise():
inputs = tf.convert_to_tensor(np.random.randint(low=0, high=7,
size=(2, 2)))
layer = NoisyDense(5, name="noise_dense_manual_reset_noise")
layer(inputs)
initial_eps_kernel = tf.identity(layer.eps_kernel)
initial_eps_bias = tf.identity(layer.eps_bias)
layer.remove_noise()
layer(inputs)
new_eps_kernel = layer.eps_kernel
new_eps_bias = layer.eps_bias
kernel_zeros = tf.zeros(initial_eps_kernel.shape,
dtype=initial_eps_kernel.dtype)
bias_zeros = tf.zeros(initial_eps_bias.shape,
dtype=initial_eps_kernel.dtype)
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_kernel,
new_eps_kernel,
)
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_bias,
new_eps_bias,
)
np.testing.assert_array_equal(kernel_zeros, new_eps_kernel)
np.testing.assert_array_equal(bias_zeros, new_eps_bias)
def test_noisy_dense_reset_noise_independent():
inputs = tf.convert_to_tensor(np.random.randint(low=0, high=7,
size=(2, 2)))
layer = NoisyDense(5,
use_factorised=False,
name="noise_dense_auto_reset_noise_independent")
layer(inputs)
initial_eps_kernel = tf.identity(layer.eps_kernel)
initial_eps_bias = tf.identity(layer.eps_bias)
layer.reset_noise()
layer(inputs)
new_eps_kernel = layer.eps_kernel
new_eps_bias = layer.eps_bias
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_kernel,
new_eps_kernel,
)
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_bias,
new_eps_bias,
)
def test_noisy_dense_with_policy():
inputs = tf.convert_to_tensor(np.random.randint(low=0, high=7,
size=(2, 2)))
layer = NoisyDense(5, dtype="mixed_float16", name="noisy_dense_policy")
outputs = layer(inputs)
output_signature = layer.compute_output_signature(
tf.TensorSpec(dtype="float16", shape=(2, 2)))
np.testing.assert_array_equal(output_signature.dtype, tf.dtypes.float16)
np.testing.assert_array_equal(output_signature.shape, (2, 5))
np.testing.assert_array_equal(outputs.dtype, "float16")
np.testing.assert_array_equal(layer.mu_kernel.dtype, "float32")
np.testing.assert_array_equal(layer.sigma_kernel.dtype, "float32")
def test_noisy_dense_dtype(dtype):
inputs = tf.convert_to_tensor(np.random.randint(low=0, high=7,
size=(2, 2)),
dtype=dtype)
layer = NoisyDense(5, dtype=dtype, name="noisy_dense_" + dtype)
outputs = layer(inputs)
np.testing.assert_array_equal(outputs.dtype, dtype)
def test_noisy_dense_regularization():
layer = NoisyDense(
3,
kernel_regularizer=keras.regularizers.l1(0.01),
bias_regularizer="l1",
activity_regularizer="l2",
name="noisy_dense_reg",
)
layer(keras.backend.variable(np.ones((2, 4))))
np.testing.assert_array_equal(5, len(layer.losses))
def test_noisy_dense_constraints():
k_constraint = keras.constraints.max_norm(0.01)
b_constraint = keras.constraints.max_norm(0.01)
layer = NoisyDense(
3,
kernel_constraint=k_constraint,
bias_constraint=b_constraint,
name="noisy_dense_constriants",
)
layer(keras.backend.variable(np.ones((2, 4))))
np.testing.assert_array_equal(layer.mu_kernel.constraint, k_constraint)
np.testing.assert_array_equal(layer.sigma_kernel.constraint, k_constraint)
np.testing.assert_array_equal(layer.mu_bias.constraint, b_constraint)
np.testing.assert_array_equal(layer.sigma_bias.constraint, b_constraint)
def test_noisy_dense_automatic_reset_noise():
inputs = tf.convert_to_tensor(np.random.randint(low=0, high=7,
size=(2, 2)))
layer = NoisyDense(5, name="noise_dense_auto_reset_noise")
layer(inputs)
initial_eps_kernel = layer.eps_kernel
initial_eps_bias = layer.eps_bias
layer(inputs)
new_eps_kernel = layer.eps_kernel
new_eps_bias = layer.eps_bias
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_kernel,
new_eps_kernel,
)
np.testing.assert_raises(
AssertionError,
np.testing.assert_array_equal,
initial_eps_bias,
new_eps_bias,
)