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修改 COPY-FROM No.6 metric #5971

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Jul 6, 2023
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修改 COPY-FROM No.6 metric #5971

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02910fd
update code-block to COPY-FROM
RedContritio Jun 26, 2023
7f828d9
fix COPY-FROM format
RedContritio Jul 5, 2023
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46 changes: 6 additions & 40 deletions docs/api/paddle/metric/Accuracy__upper_cn.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -16,49 +16,15 @@ Accuracy
代码示例
:::::::::

**独立使用示例:**
独立使用示例

.. code-block:: python
COPY-FROM paddle.metric.Accuracy:code-standalone-example
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import numpy as np
import paddle
x = paddle.to_tensor(np.array([
[0.1, 0.2, 0.3, 0.4],
[0.1, 0.4, 0.3, 0.2],
[0.1, 0.2, 0.4, 0.3],
[0.1, 0.2, 0.3, 0.4]]))
y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
m = paddle.metric.Accuracy()
correct = m.compute(x, y)
m.update(correct)
res = m.accumulate()
print(res) # 0.75


**在 Model API 中的示例**

.. code-block:: python

import paddle
from paddle.static import InputSpec
import paddle.vision.transforms as T
from paddle.vision.datasets import MNIST

input = InputSpec([None, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = MNIST(mode='train', transform=transform)

model = paddle.Model(paddle.vision.models.LeNet(), input, label)
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=paddle.nn.CrossEntropyLoss(),
metrics=paddle.metric.Accuracy())

model.fit(train_dataset, batch_size=64)
代码示例 2
::::::::::::
在 Model API 中的示例

COPY-FROM paddle.metric.Accuracy:code-model-api-example

compute(pred, label, *args)
:::::::::
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53 changes: 2 additions & 51 deletions docs/api/paddle/metric/Auc_cn.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -26,63 +26,14 @@ Auc

独立使用示例

.. code-block:: python

import numpy as np
import paddle

m = paddle.metric.Auc()

n = 8
class0_preds = np.random.random(size = (n, 1))
class1_preds = 1 - class0_preds

preds = np.concatenate((class0_preds, class1_preds), axis=1)
labels = np.random.randint(2, size = (n, 1))

m.update(preds=preds, labels=labels)
res = m.accumulate()
COPY-FROM paddle.metric.Auc:code-standalone-example

代码示例 2
::::::::::::

在 Model API 中的示例

.. code-block:: python

import numpy as np
import paddle
import paddle.nn as nn

class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('int64')

def __getitem__(self, idx):
return self.x[idx], self.y[idx]

def __len__(self):
return self.n

model = paddle.Model(nn.Sequential(
nn.Linear(10, 2), nn.Softmax())
)
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())

def loss(x, y):
return nn.functional.nll_loss(paddle.log(x), y)

model.prepare(
optim,
loss=loss,
metrics=paddle.metric.Auc())
data = Data()
model.fit(data, batch_size=16)

COPY-FROM paddle.metric.Auc:code-model-api-example

方法
::::::::::::
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21 changes: 2 additions & 19 deletions docs/api/paddle/metric/Metric_cn.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -51,31 +51,14 @@ Python/NumPy，这样可以加速计算。`update` 接口将 `compute` 的输出
可以在 `compute` 中计算每个样本的 top-5 得分，正确预测的矩阵的 shape 是[N, 5]。


.. code-block:: python

def compute(pred, label):
# sort prediction and slice the top-5 scores
pred = paddle.argsort(pred, descending=True)[:, :5]
# calculate whether the predictions are correct
correct = pred == label
return paddle.cast(correct, dtype='float32')
COPY-FROM paddle.metric.Metric:code-compute-example

代码示例 2
::::::::::::

在 `compute` 中的计算，使用内置的算子(可以跑在 GPU 上，使得速度更快)。作为 `update` 的输入，该接口计算如下：

.. code-block:: python

def update(self, correct):
accs = []
for i, k in enumerate(self.topk):
num_corrects = correct[:, :k].sum()
num_samples = len(correct)
accs.append(float(num_corrects) / num_samples)
self.total[i] += num_corrects
self.count[i] += num_samples
return accs
COPY-FROM paddle.metric.Metric:code-update-example

方法
::::::::::::
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47 changes: 2 additions & 45 deletions docs/api/paddle/metric/Precision_cn.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -24,57 +24,14 @@ Precision

独立使用示例

.. code-block:: python

import numpy as np
import paddle

x = np.array([0.1, 0.5, 0.6, 0.7])
y = np.array([0, 1, 1, 1])

m = paddle.metric.Precision()
m.update(x, y)
res = m.accumulate()
print(res) # 1.0
COPY-FROM paddle.metric.Precision:code-standalone-example

代码示例 2
::::::::::::

在 Model API 中的示例

.. code-block:: python

import numpy as np

import paddle
import paddle.nn as nn

class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')

def __getitem__(self, idx):
return self.x[idx], self.y[idx]

def __len__(self):
return self.n

model = paddle.Model(nn.Sequential(
nn.Linear(10, 1),
nn.Sigmoid()
))
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=nn.BCELoss(),
metrics=paddle.metric.Precision())

data = Data()
model.fit(data, batch_size=16)
COPY-FROM paddle.metric.Precision:code-model-api-example

方法
::::::::::::
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47 changes: 2 additions & 45 deletions docs/api/paddle/metric/Recall_cn.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -25,56 +25,13 @@ Recall

独立使用示例

.. code-block:: python

import numpy as np
import paddle

x = np.array([0.1, 0.5, 0.6, 0.7])
y = np.array([1, 0, 1, 1])

m = paddle.metric.Recall()
m.update(x, y)
res = m.accumulate()
print(res) # 2.0 / 3.0
COPY-FROM paddle.metric.Recall:code-standalone-example

代码示例 2
::::::::::::
在 Model API 中的示例

.. code-block:: python

import numpy as np

import paddle
import paddle.nn as nn

class Data(paddle.io.Dataset):
def __init__(self):
super().__init__()
self.n = 1024
self.x = np.random.randn(self.n, 10).astype('float32')
self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')

def __getitem__(self, idx):
return self.x[idx], self.y[idx]

def __len__(self):
return self.n

model = paddle.Model(nn.Sequential(
nn.Linear(10, 1),
nn.Sigmoid()
))
optim = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
model.prepare(
optim,
loss=nn.BCELoss(),
metrics=[paddle.metric.Precision(), paddle.metric.Recall()])

data = Data()
model.fit(data, batch_size=16)
COPY-FROM paddle.metric.Recall:code-model-api-example

方法
::::::::::::
Expand Down