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fix numpy issue in codeblock examples (test=document_fix) #47042

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Oct 21, 2022
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fix numpy issue in codeblock examples (test=document_fix) #47042

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1 change: 0 additions & 1 deletion python/paddle/distributed/fleet/fleet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1074,7 +1074,6 @@ def amp_init(self,
Examples:
.. code-block:: python

import numpy as np
import paddle
import paddle.nn.functional as F
paddle.enable_static()
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2 changes: 1 addition & 1 deletion python/paddle/regularizer.py
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Expand Up @@ -43,7 +43,7 @@ class L1Decay(fluid.regularizer.L1Decay):
# Example1: set Regularizer in optimizer
import paddle
from paddle.regularizer import L1Decay
import numpy as np

linear = paddle.nn.Linear(10, 10)
inp = paddle.rand(shape=[10, 10], dtype="float32")
out = linear(inp)
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106 changes: 66 additions & 40 deletions python/paddle/tensor/manipulation.py
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Expand Up @@ -2128,21 +2128,33 @@ def unique_consecutive(x,

x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1, 2])
output = paddle.unique_consecutive(x) #
np_output = output.numpy() # [1 2 3 1 2]
print(output)
# Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [1, 2, 3, 1, 2])

_, inverse, counts = paddle.unique_consecutive(x, return_inverse=True, return_counts=True)
np_inverse = inverse.numpy() # [0 0 1 1 2 3 3 4]
np_counts = inverse.numpy() # [2 2 1 2 1]
print(inverse)
# Tensor(shape=[8], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [0, 0, 1, 1, 2, 3, 3, 4])
print(counts)
# Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [2, 2, 1, 2, 1])

x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]])
output = paddle.unique_consecutive(x, axis=0) #
np_output = output.numpy() # [2 1 3 0 1 2 1 3 2 1 3]
print(output)
# Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [[2, 1, 3],
# [3, 0, 1],
# [2, 1, 3]])

x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]])
output = paddle.unique_consecutive(x, axis=0) #
np_output = output.numpy()
# [[2 1 3]
# [3 0 1]
# [2 1 3]]
print(output)
# Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [[2, 1, 3],
# [3, 0, 1],
# [2, 1, 3]])
"""

if axis is None:
Expand Down Expand Up @@ -2247,18 +2259,27 @@ def unique(x,
unique = paddle.unique(x)
np_unique = unique.numpy() # [1 2 3 5]
_, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True)
np_indices = indices.numpy() # [3 0 1 4]
np_inverse = inverse.numpy() # [1 2 2 0 3 2]
np_counts = counts.numpy() # [1 1 3 1]
print(indices)
# Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [3, 0, 1, 4])
print(inverse)
# Tensor(shape=[6], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [1, 2, 2, 0, 3, 2])
print(counts)
# Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [1, 1, 3, 1])

x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]])
unique = paddle.unique(x)
np_unique = unique.numpy() # [0 1 2 3]
print(unique)
# Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [0, 1, 2, 3])

unique = paddle.unique(x, axis=0)
np_unique = unique.numpy()
# [[2 1 3]
# [3 0 1]]
print(unique)
# Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [[2, 1, 3],
# [3, 0, 1]])
"""
if axis is None:
axis = []
Expand Down Expand Up @@ -2848,12 +2869,10 @@ def scatter_nd(index, updates, shape, name=None):
.. code-block:: python

import paddle
import numpy as np

index_data = np.array([[1, 1],
[0, 1],
[1, 3]]).astype(np.int64)
index = paddle.to_tensor(index_data)
index = paddle.to_tensor([[1, 1],
[0, 1],
[1, 3]], dtype="int64")
updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
shape = [3, 5, 9, 10]

Expand Down Expand Up @@ -2925,19 +2944,22 @@ def tile(x, repeat_times, name=None):

data = paddle.to_tensor([1, 2, 3], dtype='int32')
out = paddle.tile(data, repeat_times=[2, 1])
np_out = out.numpy()
# [[1, 2, 3]
# [1, 2, 3]]
print(out)
# Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True,
# [[1, 2, 3],
# [1, 2, 3]])

out = paddle.tile(data, repeat_times=(2, 2))
np_out = out.numpy()
# [[1, 2, 3, 1, 2, 3]
# [1, 2, 3, 1, 2, 3]]
print(out)
# Tensor(shape=[2, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
# [[1, 2, 3, 1, 2, 3],
# [1, 2, 3, 1, 2, 3]])

repeat_times = paddle.to_tensor([1, 2], dtype='int32')
out = paddle.tile(data, repeat_times=repeat_times)
np_out = out.numpy()
# [[1, 2, 3, 1, 2, 3]]
print(out)
# Tensor(shape=[1, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True,
# [[1, 2, 3, 1, 2, 3]])
"""
if in_dygraph_mode():
if isinstance(repeat_times, core.eager.Tensor):
Expand Down Expand Up @@ -3030,8 +3052,10 @@ def expand_as(x, y, name=None):
data_x = paddle.to_tensor([1, 2, 3], 'int32')
data_y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], 'int32')
out = paddle.expand_as(data_x, data_y)
np_out = out.numpy()
# [[1, 2, 3], [1, 2, 3]]
print(out)
# Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True,
# [[1, 2, 3],
# [1, 2, 3]])
"""
if in_dygraph_mode():
return _C_ops.expand_as(x, None, y.shape)
Expand Down Expand Up @@ -3987,10 +4011,11 @@ def as_complex(x, name=None):
import paddle
x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2])
y = paddle.as_complex(x)
print(y.numpy())
print(y)

# [[ 0. +1.j 2. +3.j 4. +5.j]
# [ 6. +7.j 8. +9.j 10.+11.j]]
# Tensor(shape=[2, 3], dtype=complex64, place=Place(gpu:0), stop_gradient=True,
# [[1j , (2+3j) , (4+5j) ],
# [(6+7j) , (8+9j) , (10+11j)]])
"""
if in_dygraph_mode():
return _C_ops.as_complex(x)
Expand Down Expand Up @@ -4033,15 +4058,16 @@ def as_real(x, name=None):
x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2])
y = paddle.as_complex(x)
z = paddle.as_real(y)
print(z.numpy())
print(z)

# [[[ 0. 1.]
# [ 2. 3.]
# [ 4. 5.]]
# Tensor(shape=[2, 3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[0. , 1. ],
# [2. , 3. ],
# [4. , 5. ]],

# [[ 6. 7.]
# [ 8. 9.]
# [10. 11.]]]
# [[6. , 7. ],
# [8. , 9. ],
# [10., 11.]]])
"""
if in_dygraph_mode():
return _C_ops.as_real(x)
Expand Down