diff --git a/python/paddle/incubate/nn/functional/fused_transformer.py b/python/paddle/incubate/nn/functional/fused_transformer.py index dffddb8b9eca21f13403a5db660271fa0159e792..0887cd56aefe42e166d61c67ac570f0fef6d20da 100644 --- a/python/paddle/incubate/nn/functional/fused_transformer.py +++ b/python/paddle/incubate/nn/functional/fused_transformer.py @@ -947,7 +947,6 @@ def fused_multi_transformer( # required: gpu import paddle import paddle.incubate.nn.functional as F - import numpy as np # input: [batch_size, seq_len, embed_dim] x = paddle.rand(shape=(2, 4, 128), dtype="float32") diff --git a/python/paddle/nn/layer/pooling.py b/python/paddle/nn/layer/pooling.py index a9b5af5199faf0d9af79ca5bceb6d5a8554bd861..3c3abe5e3903f4b2a459c65a52e1f84952e655f6 100755 --- a/python/paddle/nn/layer/pooling.py +++ b/python/paddle/nn/layer/pooling.py @@ -1171,7 +1171,6 @@ class MaxUnPool1D(Layer): import paddle import paddle.nn.functional as F - import numpy as np data = paddle.rand(shape=[1, 3, 16]) pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_mask=True) @@ -1351,7 +1350,6 @@ class MaxUnPool3D(Layer): import paddle import paddle.nn.functional as F - import numpy as np data = paddle.rand(shape=[1, 1, 4, 4, 6]) pool_out, indices = F.max_pool3d(data, kernel_size=2, stride=2, padding=0, return_mask=True) diff --git a/python/paddle/optimizer/adagrad.py b/python/paddle/optimizer/adagrad.py index a4d9416e93bcc2ff10a444a1d7b4af37582da350..522ca753a99767a25d77058764ba70e6fbf24960 100644 --- a/python/paddle/optimizer/adagrad.py +++ b/python/paddle/optimizer/adagrad.py @@ -70,7 +70,6 @@ class Adagrad(Optimizer): .. code-block:: python import paddle - import numpy as np inp = paddle.rand(shape=[10, 10]) linear = paddle.nn.Linear(10, 10) diff --git a/python/paddle/regularizer.py b/python/paddle/regularizer.py index 395ec08a36848c1aed215fe84f7c3d7594e24f73..38060b8233fdbaf286876a0f9e408153b627e72f 100644 --- a/python/paddle/regularizer.py +++ b/python/paddle/regularizer.py @@ -105,7 +105,6 @@ class L2Decay(fluid.regularizer.L2Decay): # Example1: set Regularizer in optimizer import paddle from paddle.regularizer import L2Decay - import numpy as np linear = paddle.nn.Linear(10, 10) inp = paddle.rand(shape=[10, 10], dtype="float32") out = linear(inp)