def auc(input, label, curve='ROC', num_thresholds=200):
"""
**Area Under the Curve (AUC) Layer**
This implementation computes the AUC according to forward output and label.
It is used very widely in binary classification evaluation.
Note: If input label contains values other than 0 and 1, it will be cast
to `bool`. Find the relevant definitions `here <https://en.wikipedia.org\
/wiki/Receiver_operating_characteristic#Area_under_the_curve>`_.
There are two types of possible curves:
1. ROC: Receiver operating characteristic;
2. PR: Precision Recall
Args:
input(Variable): A floating-point 2D Variable, values are in the range
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Variable indicates the probability of each label.
label(Variable): A 2D int Variable indicating the label of the training
data. The height is batch size and width is always 1.
curve(str): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
Returns:
Variable: A scalar representing the current AUC.
Examples:
.. code-block:: python
# network is a binary classification model and label the ground truth
prediction = network(image, is_infer=True)
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
warnings.warn(
"This interface not recommended, fluid.layers.auc compute the auc at every minibatch, \
but can not aggregate them and get the pass AUC, because pass \
auc can not be averaged with weighted from the minibatch auc value. \
Please use fluid.metrics.Auc, it can compute the auc value via Python natively, \
which can get every minibatch and every pass auc value.", Warning)
helper = LayerHelper("auc", **locals())
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64")
topk_out, topk_indices = nn.topk(input, k=k)
auc_out = helper.create_tmp_variable(dtype="float32")
helper.append_op(
type="auc",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
attrs={"curve": curve,
"num_thresholds": num_thresholds},
outputs={"AUC": [auc_out], })
return auc_out
def auc(input, label, curve='ROC', num_thresholds=200):
warnings.warn(
"This interface not recommended, fluid.layers.auc compute the auc at every minibatch, \
but can not aggregate them and get the pass AUC, because pass \
auc can not be averaged with weighted from the minibatch auc value. \
Please use fluid.metrics.Auc, it can compute the auc value via Python natively, \
which can get every minibatch and every pass auc value.", Warning)
helper = LayerHelper("auc", **locals())
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64")
topk_out, topk_indices = nn.topk(input, k=k)
auc_out = helper.create_tmp_variable(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
helper.append_op(type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
attrs={
"curve": curve,
"num_thresholds": num_thresholds
},
outputs={
"AUC": [auc_out],
})
return auc_out
def accuracy(input, label, k=1, correct=None, total=None):
"""
accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network.
Carry LoD information is supported.
label(Variable): The label of dataset.
k(int): The top k predictions for each class will be checked.
correct(Variable): The correct predictions count.
total(Variable): The total entries count.
Returns:
Variable: The correct rate.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32")
predict = fluid.layers.fc(input=data, size=10)
acc = fluid.layers.accuracy(input=predict, label=label, k=5)
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_tmp_variable(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
outputs={
"Accuracy": [acc_out],
"Correct": [correct],
"Total": [total],
})
return acc_out
def accuracy(input, label, k=1, correct=None, total=None):
"""
This function computes the accuracy using the input and label.
The output is the top k inputs and their indices.
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_tmp_variable(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
helper.append_op(type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
outputs={
"Accuracy": [acc_out],
"Correct": [correct],
"Total": [total],
})
return acc_out