def drop_layer(x, keep_prob, seed=None, name=None):
"""Computes dropout.
With probability `keep_prob`, outputs the input element scaled up by
`1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected
sum is unchanged.
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
[`set_random_seed`](../../api_docs/python/constant_op.md#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 tf.op_scope([x], name, "drop_layer") as name:
x = tf.convert_to_tensor(x, name="x")
if isinstance(keep_prob, float) 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 = tf.convert_to_tensor(keep_prob,
dtype=x.dtype,
name="keep_prob")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
noise_shape = [ tf.shape(x)[0], 1 ]
# uniform [keep_prob, 1.0 + keep_prob)
random_tensor = keep_prob
random_tensor += tf.random_uniform(
noise_shape,
seed=seed,
dtype=x.dtype
)
# 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
binary_tensor = tf.floor(random_tensor)
ret = x * tf.inv(keep_prob) * binary_tensor
ret.set_shape(x.get_shape())
return ret
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