o #j@s`gZddlmZmZddlmZddlmZdddZddd Z edd d Z edd dZ dS))_C_opsin_dynamic_mode) dygraph_only) LayerHelperNcCsFtrt|Sd}t|}||j}|j|d|id|iid|S)a sparse relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = max(x, 0) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.nn.functional.relu(sparse_x) >>> print(out) Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[0., 1.]) sparse_reluxouttypeZinputsZoutputsattrs)rrrr)create_sparse_variable_for_type_inferencedtype append_op)rnameop_typehelperrrg/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/sparse/nn/functional/activation.pyrelus  rcCsLtr t||Sd}t|}||j}|j|d|id|id|id|S)a 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). From the point of view of dense matrix, for each row :math:`i` and each column :math:`j` in the matrix, we have: .. math:: softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))} Parameters: x (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64. 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`. Returns: Tensor: SparseCoo or SparseCsr, whose layout is the same with `x` . Examples: .. code-block:: python >>> import paddle >>> paddle.seed(100) >>> mask = paddle.rand((3, 4)) < 0.5 >>> x = paddle.rand((3, 4)) * mask >>> print(x) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0. , 0.95717543, 0.43864486, 0. ], [0.84765935, 0.45680618, 0.39412445, 0. ], [0.59444654, 0. , 0.78364515, 0. ]]) >>> csr = x.to_sparse_csr() >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, crows=[0, 2, 5, 7], cols=[1, 2, 0, 1, 2, 0, 2], values=[0.95717543, 0.43864486, 0.84765935, 0.45680618, 0.39412445, 0.59444654, 0.78364515]) >>> out = paddle.sparse.nn.functional.softmax(csr) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, crows=[0, 2, 5, 7], cols=[1, 2, 0, 1, 2, 0, 2], values=[0.62680405, 0.37319586, 0.43255258, 0.29261294, 0.27483448, 0.45284089, 0.54715902]) >>> 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, 1, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0, 2]], values=[0.95717543, 0.43864486, 0.84765935, 0.45680618, 0.39412445, 0.59444654, 0.78364515]) >>> out = paddle.sparse.nn.functional.softmax(coo) >>> print(out) Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 0, 1, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0, 2]], values=[0.62680405, 0.37319589, 0.43255258, 0.29261294, 0.27483445, 0.45284092, 0.54715902]) sparse_softmaxrraxisr )rrrrr r r)rrrrrrrrrsoftmax?sF  rcCs t|S)a sparse relu6 activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: relu6(x) = min(max(0, x), 6) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 8.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.nn.functional.relu6(sparse_x) )rZ sparse_relu6)rrrrrrelu6s r{Gz?cCs t||S)a sparse leaky_relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: leaky\_relu(x)= \left\{ \begin{array}{rcl} x, & & if \ x >= 0 \\ negative\_slope * x, & & otherwise \\ \end{array} \right. Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. 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`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 5.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.nn.functional.leaky_relu(sparse_x, 0.5) )rZsparse_leaky_relu)rZnegative_sloperrrr leaky_relus !r)N)rN)rN) __all__ZpaddlerrZpaddle.base.frameworkrZpaddle.base.layer_helperrrrrrrrrrs    )U