飞桨中accuracy的用法
飞桨中accuracy的用法
本文只作为笔记用。
环境：paddle 1.8.5
一直以来不太清楚这个如何调用，只知道例程里是这样用，不知所以然。
下面记录一下它的用法。
源码如下：
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. A LoDTensor or Tensor with type float32,float64.
The shape is ``[sample_number, class_dim]`` .
label(Variable): The label of dataset. LoDTensor or Tensor with type int32,int64. The shape is ``[sample_number, 1]`` .
k(int): The top k predictions for each class will be checked. Data type is int64 or int32.
correct(Variable): The correct predictions count. A Tensor with type int64 or int32.
total(Variable): The total entries count. A tensor with type int64 or int32.
Returns:
Variable: The correct rate. A Tensor with type float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
data = fluid.data(name="input", shape=[-1, 32, 32], dtype="float32")
label = fluid.data(name="label", shape=[-1,1], dtype="int")
fc_out = fluid.layers.fc(input=data, size=10)
predict = fluid.layers.softmax(input=fc_out)
result = fluid.layers.accuracy(input=predict, label=label, k=5)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
x = np.random.rand(3, 32, 32).astype("float32")
y = np.array([[1],[0],[1]])
output= exe.run(feed={"input": x,"label": y},
fetch_list=[result[0]])
print(output)
#[array([0.6666667], dtype=float32)]
"""
if in_dygraph_mode():
if correct is None:
correct = _varbase_creator(dtype="int32")
if total is None:
total = _varbase_creator(dtype="int32")
topk_out, topk_indices = nn.topk(input, k=k)
_acc, _, _ = core.ops.accuracy(topk_out, topk_indices, label, correct,
total)
return _acc
helper = LayerHelper("accuracy", **locals())
check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
'accuracy')
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_variable_for_type_inference(dtype="int32")
if total is None:
total = helper.create_variable_for_type_inference(dtype="int32")
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
一开始我以为，它会像sklearn那样，两个相同的array丢进去就OK了。
但是以下做法是错误的。
batch_data = next(test_loader())
y1 = np.array([1, 2, 3, 4]).reshape(-1, 1).astype(np.int64)
y2 = np.array([1, 2, 3, 4]).reshape(-1, 1).astype(np.int64)
y1 = paddle.fluid.dygraph.to_variable(y1)
y2 = paddle.fluid.dygraph.to_variable(y2)
acc = paddle.fluid.layers.accuracy(y1, y2)
print(acc.numpy())
错误信息：
EnforceNotMet Traceback (most recent call last)
<ipython-input-59-971e31ff0c41> in <module>
8 # pred = paddle.fluid.layers.softmax(y1)
9 # y2 = paddle.fluid.layers.cast(y2, np.int64)
---> 10 acc = paddle.fluid.layers.accuracy(y1, y2)
11 print(acc.numpy())
d:\programdata\anaconda3\envs\parl\lib\site-packages\paddle\fluid\layers\metric_op.py in accuracy(input, label, k, correct, total)
82 topk_out, topk_indices = nn.topk(input, k=k)
83 _acc, _, _ = core.ops.accuracy(topk_out, topk_indices, label, correct,
---> 84 total)
85 return _acc
86
EnforceNotMet:
--------------------------------------------
C++ Call Stacks (More useful to developers):
--------------------------------------------
Windows not support stack backtrace yet.
----------------------
Error Message Summary:
----------------------
NotFoundError: Operator accuracy does not have kernel for data_type[int64_t]:data_layout[ANY_LAYOUT]:place[CUDAPlace(0)]:library_type[PLAIN].
[Hint: Expected kernel_iter != kernels.end(), but received kernel_iter == kernels.end().] at (D:\1.8.5\paddle\paddle\fluid\imperative\prepared_operator.cc:118)
[operator < accuracy > error]
在paddle里，它的accuracy的第一个参数输入的是对应你多种类别分别的概率，第二个参数是类别。
以下是正确的结果的code。
batch_data = next(test_loader())
y1 = np.array([[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91]]).astype(np.float32)
y2 = np.array([1, 2, 3, 4]).reshape(-1, 1).astype(np.int64)
print(y1.numpy(), y1.numpy().shape, y2.numpy(), y2.numpy().shape)
y1 = paddle.fluid.dygraph.to_variable(y1)
y2 = paddle.fluid.dygraph.to_variable(y2)
acc = paddle.fluid.layers.accuracy(y1, y2)
print(acc.numpy())
# 结果：[0.25]
# y1
# [[ 1.22 12.43 0.1 0.91]
# [ 1.22 12.43 0.1 0.91]
# [ 1.22 12.43 0.1 0.91]
# [ 1.22 12.43 0.1 0.91]]
# y2
# [[1]
# [2]
# [3]
# [4]]
或者
batch_data = next(test_loader())
y1 = np.array([[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91],[1.22, 12.43, 0.1, 0.91]]).astype(np.float32)
y2 = np.array([1, 2, 3, 4]).reshape(-1, 1).astype(np.int64)
y1 = paddle.fluid.dygraph.to_variable(y1)
y2 = paddle.fluid.dygraph.to_variable(y2)
y1 = paddle.fluid.layers.softmax(y1)
print(y1.numpy(), y1.numpy().shape, y2.numpy(), y2.numpy().shape)
acc = paddle.fluid.layers.accuracy(y1, y2)
print(acc.numpy())
# 结果：[0.25]
# y1
# [[1.3537758e-05 9.9997211e-01 4.4170970e-06 9.9292220e-06]
# [1.3537758e-05 9.9997211e-01 4.4170970e-06 9.9292220e-06]
# [1.3537758e-05 9.9997211e-01 4.4170970e-06 9.9292220e-06]
# [1.3537758e-05 9.9997211e-01 4.4170970e-06 9.9292220e-06]]
# y2
# [[1]
# [2]
# [3]
# [4]]
对自己的记录，希望对大家有帮助。