def scatter(input, index, updates, name=None):
"""
**Scatter Layer**
by Lihang Liu.
There's a bug in Python API scatter for parameter checking,
please refer to (https://github.com/PaddlePaddle/Paddle/issues/12725).
Output is obtained by updating the input on selected indices on the first
axis.
.. math::
Out = X
Out[Ids] = Updates
Args:
input (Variable): The source input with rank>=1.
index (Variable): The index input with rank=1. Its dtype should be
int32 or int64 as it is used as indexes.
updates (Variable): The updated value of scatter op.
name (str|None): The output variable name. Default None.
Returns:
output (Variable): The output is a tensor with the same shape as input.
Examples:
.. code-block:: python
output = fluid.layers.scatter(input, index, updates)
"""
helper = LayerHelper('scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="scatter",
inputs={"X": input,
"Ids": index,
"Updates": updates},
outputs={"Out": out})
return out
def log_loss(input, label, epsilon=1e-4):
"""
**Negative Log Loss Layer**
This layer accepts input predictions and target label and returns the
negative log loss.
.. math::
Out = -label * log(X + epsilon) - (1 - label) * log(1 - X + epsilon)
Args:
input: a 2-D tensor with shape [N x 1], where N is the batch size.
This input is a probability computed by the previous operator.
label: the ground truth which is a 2-D tensor with shape [N x 1],
where N is the batch size.
epsilon: epsilon
Returns:
A 2-D tensor with shape [N x 1], the negative log loss.
Examples:
.. code-block:: python
prob = fluid.layers.sigmoid(net)
cost = fluid.layers.log_loss(input=prob, label=label)
"""
helper = LayerHelper('log_loss', **locals())
loss = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='log_loss',
inputs={'Predicted': [input],
'Labels': [label]},
outputs={'Loss': [loss]},
attrs={'epsilon': epsilon})
return loss
