def alpha_dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name
"""Computes alpha dropout.
Alpha Dropout is a dropout that maintains the self-normalizing property. For
an input with zero mean and unit standard deviation, the output of
Alpha Dropout maintains the original mean and standard deviation of the input.
See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
Args:
x: A tensor.
keep_prob: A scalar `Tensor` with the same type as x. The probability
that each element is kept.
noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
name: A name for this operation (optional).
Returns:
A Tensor of the same shape of `x`.
Raises:
ValueError: If `keep_prob` is not in `(0, 1]`.
"""
with ops.name_scope(name, "alpha_dropout", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1.:
raise ValueError(
"keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(keep_prob,
dtype=x.dtype,
name="keep_prob")
keep_prob.get_shape().assert_has_rank(0)
# Do nothing if we know keep_prob == 1
if tensor_util.constant_value(keep_prob) == 1:
return x
alpha = -1.7580993408473766
noise_shape = noise_shape if noise_shape is not None else array_ops.shape(
x)
random_tensor = random_ops.random_uniform(noise_shape,
seed=seed,
dtype=x.dtype)
kept_idx = gen_math_ops.greater_equal(random_tensor, 1 - keep_prob)
kept_idx = math_ops.cast(kept_idx, x.dtype)
# Mask
x = x * kept_idx + alpha * (1 - kept_idx)
# Affine transformation parameters
a = (keep_prob + keep_prob * (1 - keep_prob) * alpha**2)**-0.5
b = -a * alpha * (1 - keep_prob)
# Affine transformation
return a * x + b
def alpha_dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name
"""Computes alpha dropout.
Alpha Dropout is a dropout that maintains the self-normalizing property. For
an input with zero mean and unit standard deviation, the output of
Alpha Dropout maintains the original mean and standard deviation of the input.
See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
Args:
x: A tensor.
keep_prob: A scalar `Tensor` with the same type as x. The probability
that each element is kept.
noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
seed: A Python integer. Used to create random seeds. See
`tf.compat.v1.set_random_seed` for behavior.
name: A name for this operation (optional).
Returns:
A Tensor of the same shape of `x`.
Raises:
ValueError: If `keep_prob` is not in `(0, 1]`.
"""
with ops.name_scope(name, "alpha_dropout", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1.:
raise ValueError("keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(keep_prob,
dtype=x.dtype,
name="keep_prob")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
# Do nothing if we know keep_prob == 1
if tensor_util.constant_value(keep_prob) == 1:
return x
alpha = -1.7580993408473766
noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x)
random_tensor = random_ops.random_uniform(noise_shape,
seed=seed,
dtype=x.dtype)
kept_idx = gen_math_ops.greater_equal(random_tensor, 1 - keep_prob)
kept_idx = math_ops.cast(kept_idx, x.dtype)
# Mask
x = x * kept_idx + alpha * (1 - kept_idx)
# Affine transformation parameters
a = (keep_prob + keep_prob * (1 - keep_prob) * alpha ** 2) ** -0.5
b = -a * alpha * (1 - keep_prob)
# Affine transformation
return a * x + b