o #j@s`ddlmZddlmZgZGdddeZGdddeZGdd d eZGd d d eZ d S) )Layer) functionalc2eZdZdZd fdd ZddZddZZS) ReLUay Sparse ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: ReLU(x) = max(x, 0) Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Sparse Tensor with any shape. - output: Sparse Tensor with the same shape as input. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> relu = paddle.sparse.nn.ReLU() >>> out = relu(sparse_x) >>> print(out) Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[0., 1.]) Nct||_dSNsuper__init___nameselfname __class__b/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/sparse/nn/layer/activation.pyr 6  z ReLU.__init__cCt||jSr)FZrelur rxrrrforward:z ReLU.forwardcC|jr d|j}|Sd}|SNzname=r rZname_strrrr extra_repr=zReLU.extra_reprr__name__ __module__ __qualname____doc__r rr __classcell__rrrrrs rc2eZdZdZd fdd ZddZdd ZZS) Softmaxa= Sparse Softmax Activation, requiring x to be a SparseCooTensor or SparseCsrTensor. Note: Only support axis=-1 for SparseCsrTensor, which is faster when read data by row (axis=-1). Transform x to dense matix, and :math:`i` is row index, :math:`j` is column index. If axis=-1, We have: .. math:: softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))} Parameters: axis (int, optional): The axis along which to perform softmax calculations. Only support -1 for SparseCsrTensor. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: SparseCooTensor / SparseCsrTensor with any shape. - output: Sparse Tensor with the same shape as input. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2022) >>> mask = paddle.rand((3, 4)) < 0.7 >>> x = paddle.rand((3, 4)) * mask >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.88156885, 0.14463395, 0.17831714, 0.43818203], [0.07617740, 0.75576496, 0. , 0.61921930], [0. , 0. , 0.42460245, 0.03001321]]) >>> csr = x.to_sparse_csr() >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, crows=[0, 4, 7, 9], cols=[0, 1, 2, 3, 0, 1, 3, 2, 3], values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740, 0.75576496, 0.61921930, 0.42460245, 0.03001321]) >>> softmax = paddle.sparse.nn.Softmax() >>> out = softmax(csr) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, crows=[0, 4, 7, 9], cols=[0, 1, 2, 3, 0, 1, 3, 2, 3], values=[0.38234913, 0.18298410, 0.18925257, 0.24541418, 0.21302439, 0.42031071, 0.36666498, 0.59738696, 0.40261301]) >>> coo = x.to_sparse_coo(sparse_dim=2) >>> print(coo) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2], [0, 1, 2, 3, 0, 1, 3, 2, 3]], values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740, 0.75576496, 0.61921930, 0.42460245, 0.03001321]) >>> out = softmax(coo) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2], [0, 1, 2, 3, 0, 1, 3, 2, 3]], values=[0.38234913, 0.18298411, 0.18925257, 0.24541420, 0.21302438, 0.42031071, 0.36666498, 0.59738696, 0.40261301]) Nct||_||_dSr)r r _axisr )rZaxisrrrrr   zSoftmax.__init__cCt||j|jSr)rZsoftmaxr,r rrrrrzSoftmax.forwardcCrrrrrrrr r!zSoftmax.extra_repr)r*Nr"rrrrr)Bs Hr)cr) ReLU6a Sparse ReLU6 Activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: ReLU6(x) = min(max(0,x), 6) Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Sparse Tensor with any shape. - output: Sparse Tensor with the same shape as input. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 8.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> relu6 = paddle.sparse.nn.ReLU6() >>> out = relu6(sparse_x) Ncrrr r rrrr rzReLU6.__init__cCrr)rZrelu6r rrrrrrz ReLU6.forwardcCrrrrrrrr r!zReLU6.extra_reprrr"rrrrr0s r0cr() LeakyReLUa Sparse Leaky ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: LeakyReLU(x)= \left\{ \begin{array}{rcl} x, & & if \ x >= 0 \\ negative\_slope * x, & & otherwise \\ \end{array} \right. Parameters: negative_slope (float, optional): Slope of the activation function at :math:`x < 0` . Default is 0.01. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Sparse Tensor with any shape. - output: Sparse Tensor with the same shape as input. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 5.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> leaky_relu = paddle.sparse.nn.LeakyReLU(0.5) >>> out = leaky_relu(sparse_x) {Gz?Ncr+r)r r _negative_sloper )rZnegative_sloperrrrr r-zLeakyReLU.__init__cCr.r)rZ leaky_relur3r rrrrrr/zLeakyReLU.forwardcCrrrrrrrr r!zLeakyReLU.extra_repr)r2Nr"rrrrr1s $r1N) Z paddle.nnrrrr__all__rr)r0r1rrrrs  ,V(