def dropout_selu(
self,
x,
keep_prob,
alpha= DROP_ALPHA,
fixedPointMean=0.0,
fixedPointVar=1.0,
noise_shape=None,
seed=None,
name=None,
training=False):
"""Dropout to a value with rescaling."""
def dropout_selu_impl(x, rate, alpha, noise_shape, seed, name):
keep_prob = 1.0 - rate
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())
alpha = ops.convert_to_tensor(alpha, dtype=x.dtype, name="alpha")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
if tensor_util.constant_value(keep_prob) == 1:
return x
noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x)
random_tensor = keep_prob
random_tensor += random_ops.random_uniform(noise_shape, seed=seed, dtype=x.dtype)
binary_tensor = math_ops.floor(random_tensor)
ret = x * binary_tensor + alpha * (1-binary_tensor)
a = tf.sqrt(fixedPointVar / (keep_prob *((1-keep_prob) * tf.pow(alpha-fixedPointMean,2) + fixedPointVar)))
b = fixedPointMean - a * (keep_prob * fixedPointMean + (1 - keep_prob) * alpha)
ret = a * ret + b
ret.set_shape(x.get_shape())
return ret
with ops.name_scope(name, "dropout", [x]) as name:
return utils.smart_cond(training,
lambda: dropout_selu_impl(x, keep_prob, alpha, noise_shape, seed, name),
lambda: array_ops.identity(x))
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