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Update code examples for api2.0

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Update code examples for api2.0
my_2.0rc
Steffy-zxf 4 years ago committed by GitHub
parent 1d95a0fbc3
commit 9215ad96ca
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5 changed files with 70 additions and 103 deletions
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102
python/paddle/fluid/layers/nn.py
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@ -8670,10 +8670,6 @@ def random_crop(x, shape, seed=None):
def log(x, name=None):
"""
:alias_main: paddle.log
:alias: paddle.log,paddle.tensor.log,paddle.tensor.math.log
:old_api: paddle.fluid.layers.log
Calculates the natural log of the given input tensor, element-wise.
.. math::
@ -8681,31 +8677,23 @@ def log(x, name=None):
Out = \\ln(x)
Args:
x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
x (Tensor): Input Tensor. Must be one of the following types: float32, float64.
name (str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
Tensor: The natural log of the input Tensor computed element-wise.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
# Graph Organizing
x = fluid.layers.data(name="x", shape=[1], dtype="float32")
res = fluid.layers.log(x)
# Create an executor using CPU as an example
exe = fluid.Executor(fluid.CPUPlace())
import paddle
# Execute
x_i = np.array([[1], [2]]).astype(np.float32)
res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
print(res_val) # [[0.], [0.6931472]]
x = [[2,3,4], [7,8,9]]
x = paddle.to_tensor(x, dtype='float32')
res = paddle.log(x)
# [[0.693147, 1.09861, 1.38629], [1.94591, 2.07944, 2.19722]]
"""
if in_dygraph_mode():
return core.ops.log(x)
@ -8846,33 +8834,36 @@ def mean_iou(input, label, num_classes):
Parameters:
input (Variable): A n-D Tensor of prediction results for semantic labels with type int32 or int64.
label (Variable): A Tensor of ground truth labels with type int32 or int64.
input (Tensor): A n-D Tensor of prediction results for semantic labels with type int32 or int64.
label (Tensor): A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
num_classes (int32): The possible number of labels.
Returns:
Three Variables.
Three Tensors.
- mean_iou(Variable) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \
- mean_iou(Tensor) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \
Data type is float32.
- out_wrong(Variable) : A 1-D Tensor with shape [num_classes]. Data type is int32. \
- out_wrong(Tensor) : A 1-D Tensor with shape [num_classes]. Data type is int32. \
The wrong numbers of each class.
- out_correct(Variable): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class.
- out_correct(Tensor): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class.
Examples:
.. code-block:: python
import paddle.fluid as fluid
iou_shape = [None, 32, 32]
import paddle
iou_shape = [64, 32, 32]
num_classes = 5
predict = fluid.data(name='predict', shape=iou_shape, dtype='int64')
label = fluid.data(name='label', shape=iou_shape, dtype='int64')
mean_iou, out_wrong, out_correct = fluid.layers.mean_iou(predict, label,
num_classes)
predict = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64')
label = paddle.randint(low=0, high=255, shape=iou_shape, dtype='int64')
mean_iou, out_wrong, out_correct = paddle.metric.mean_iou(predict, label, num_classes)
"""
if in_dygraph_mode():
return core.ops.mean_iou(input, label, 'num_classes', num_classes)
helper = LayerHelper('mean_iou', **locals())
check_variable_and_dtype(input, 'Predictions', ['int32', 'int64'],
'mean_iou')
@ -11387,10 +11378,6 @@ def _elementwise_op(helper):
def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
"""
:alias_main: paddle.scale
:alias: paddle.scale,paddle.tensor.scale,paddle.tensor.math.scale
:old_api: paddle.fluid.layers.scale
Scale operator.
Putting scale and bias to the input Tensor as following:
@ -11406,52 +11393,33 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
Out=scale*(X+bias)
Args:
x(Variable): Input N-D Tensor of scale operator. Data type can be float32, float64, int8, int16, int32, int64, uint8.
scale(float|Variable): The scale factor of the input, it should be a float number or a Variable with shape [1] and data type as float32.
x(Tensor): Input N-D Tensor of scale operator. Data type can be float32, float64, int8, int16, int32, int64, uint8.
scale(float|Tensor): The scale factor of the input, it should be a float number or a Tensor with shape [1] and data type as float32.
bias(float): The bias to be put on the input.
bias_after_scale(bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances.
act(str, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable(Tensor|LoDTensor): Output tensor of scale operator, with shape and data type same as input.
Tensor: Output tensor of scale operator, with shape and data type same as input.
Examples:
.. code-block:: python
# scale as a float32 number
import paddle
import paddle.fluid as fluid
import numpy as np
inputs = fluid.layers.data(name="x", shape=[2, 3], dtype='float32')
output = fluid.layers.scale(inputs, scale = 2.0, bias = 1.0)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output])
print(res) # [array([[ 3., 5., 7.], [ 9., 11., 13.]], dtype=float32)]
data = paddle.randn(shape=[2,3], dtype='float32')
res = paddle.scale(data, scale=2.0, bias=1.0)
.. code-block:: python
# scale with parameter scale as Variable
import paddle.fluid as fluid
import numpy as np
inputs = fluid.layers.data(name="x", shape=[2, 3], dtype='float32')
scale = fluid.layers.data(name="scale", shape=[1], dtype='float32',
append_batch_size=False)
output = fluid.layers.scale(inputs, scale = scale, bias = 1.0)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
scale_np = np.array([2.]).astype(np.float32)
# scale with parameter scale as a Tensor
import paddle
res = exe.run(fluid.default_main_program(), feed={'x':img, 'scale':scale_np}, fetch_list=[output])
print(res) # [array([[ 3., 5., 7.], [ 9., 11., 13.]], dtype=float32)]
data = paddle.randn(shape=[2, 3], dtype='float32')
factor = paddle.to_tensor([2], dtype='float32')
res = paddle.scale(data, scale=factor, bias=1.0)
"""

2
python/paddle/fluid/layers/ops.py
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@ -190,11 +190,9 @@ Examples:
.. code-block:: python
import paddle
paddle.disable_static()
x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4])
out = paddle.rsqrt(x)
print(out.numpy())
# [3.16227766 2.23606798 1.82574186 1.58113883]
""")

36
python/paddle/fluid/layers/tensor.py
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@ -1237,26 +1237,26 @@ def load_combine(out, file_path):
def has_inf(x):
"""
:alias_main: paddle.has_inf
:alias: paddle.has_inf,paddle.tensor.has_inf,paddle.tensor.search.has_inf
:old_api: paddle.fluid.layers.has_inf
Test if any of x contains an infinity number
Args:
x (Variable): The Tensor/LoDTensor to be checked.
x (Tensor): The Tensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value, indicating that whether there is infinity number in x or not.
Tensor: The tensor storing the output, only a bool value, indicating that whether there is infinity number in x or not.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
res = fluid.layers.has_inf(data)
import paddle
data = paddle.randn(shape=[4, 32, 32], dtype="float32")
res = paddle.has_inf(data)
# [False]
"""
if in_dygraph_mode():
return core.ops.isinf(x)
check_type(x, 'x', (Variable), 'has_inf')
helper = LayerHelper("isinf", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
@ -1266,26 +1266,26 @@ def has_inf(x):
def has_nan(x):
"""
:alias_main: paddle.has_nan
:alias: paddle.has_nan,paddle.tensor.has_nan,paddle.tensor.search.has_nan
:old_api: paddle.fluid.layers.has_nan
Test if any of x contains a NAN
Args:
x (Variable): The Tensor/LoDTensor to be checked.
x (Tensor): The Tensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value, indicating that whether there is NAN in x or not.
Tensor: The tensor variable storing the output, only a bool value, indicating that whether there is NAN in x or not.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
res = fluid.layers.has_nan(data)
import paddle
data = paddle.randn(shape=[2,3], dtype="float32")
res = paddle.has_nan(data)
# [False]
"""
if in_dygraph_mode():
return core.ops.isnan(x)
check_type(x, 'x', (Variable), 'has_nan')
helper = LayerHelper("isnan", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)

9
python/paddle/fluid/tests/unittests/test_isfinite_op.py
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@ -14,6 +14,7 @@
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from op_test import OpTest
@ -132,6 +133,14 @@ class BadInputTest(unittest.TestCase):
self.assertRaises(TypeError, test_has_nan_bad_x)
with fluid.dygraph.guard():
data = paddle.zeros([2, 3])
result = paddle.has_inf(data)
expect_value = np.array([False])
self.assertEqual((result.numpy() == expect_value).all(), True)
result = paddle.has_nan(data)
self.assertEqual((result.numpy() == expect_value).all(), True)
if __name__ == '__main__':
unittest.main()

24
python/paddle/tensor/math.py
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@ -1308,33 +1308,25 @@ def min(x, axis=None, keepdim=False, name=None):
def log1p(x, name=None):
"""
:alias_main: paddle.log1p
:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
Calculates the natural log of the given input tensor, element-wise.
.. math::
Out = \\ln(x+1)
Args:
x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
x (Tensor): Input Tensor. Must be one of the following types: float32, float64.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
Tensor, the natural log of the input Tensor computed element-wise.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
# Graph Organizing
x = fluid.data(name="x", shape=[2,1], dtype="float32")
res = paddle.log1p(x)
# Create an executor using CPU as an example
exe = fluid.Executor(fluid.CPUPlace())
# Execute
x_i = np.array([[0], [1]]).astype(np.float32)
res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
print(res_val) # [[0.], [0.6931472]]
data = paddle.to_tensor([[0], [1]], dtype='float32')
res = paddle.log1p(data)
# [[0.], [0.6931472]]
"""
if in_dygraph_mode():

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