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Doc fix (#1615)
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Awni Hannun
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@ -12,6 +12,7 @@ Layers
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ALiBi
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AvgPool1d
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AvgPool2d
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AvgPool3d
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BatchNorm
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CELU
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Conv1d
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@ -41,6 +42,7 @@ Layers
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LSTM
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MaxPool1d
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MaxPool2d
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MaxPool3d
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Mish
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MultiHeadAttention
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PReLU
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@ -185,16 +185,8 @@ class _Pool3d(_Pool):
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class MaxPool1d(_Pool1d):
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r"""Applies 1-dimensional max pooling.
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Assuming an input of shape :math:`(N, L, C)` and ``kernel_size`` is
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:math:`k`, the output is a tensor of shape :math:`(N, L_{out}, C)`, given
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by:
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.. math::
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\text{out}(N_i, t, C_j) = \max_{m=0, \ldots, k - 1}
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\text{input}(N_i, \text{stride} \times t + m, C_j),
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where :math:`L_{out} = \left\lfloor \frac{L + 2 \times \text{padding} -
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\text{kernel\_size}}{\text{stride}}\right\rfloor + 1`.
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Spatially downsamples the input by taking the maximum of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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Args:
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kernel_size (int or tuple(int)): The size of the pooling window kernel.
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@ -224,16 +216,8 @@ class MaxPool1d(_Pool1d):
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class AvgPool1d(_Pool1d):
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r"""Applies 1-dimensional average pooling.
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Assuming an input of shape :math:`(N, L, C)` and ``kernel_size`` is
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:math:`k`, the output is a tensor of shape :math:`(N, L_{out}, C)`, given
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by:
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.. math::
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\text{out}(N_i, t, C_j) = \frac{1}{k} \sum_{m=0, \ldots, k - 1}
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\text{input}(N_i, \text{stride} \times t + m, C_j),
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where :math:`L_{out} = \left\lfloor \frac{L + 2 \times \text{padding} -
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\text{kernel\_size}}{\text{stride}}\right\rfloor + 1`.
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Spatially downsamples the input by taking the average of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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Args:
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kernel_size (int or tuple(int)): The size of the pooling window kernel.
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@ -263,25 +247,14 @@ class AvgPool1d(_Pool1d):
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class MaxPool2d(_Pool2d):
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r"""Applies 2-dimensional max pooling.
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Assuming an input of shape :math:`(N, H, W, C)` and ``kernel_size`` is
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:math:`(k_H, k_W)`, the output is a tensor of shape :math:`(N, H_{out},
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W_{out}, C)`, given by:
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Spatially downsamples the input by taking the maximum of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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.. math::
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\begin{aligned}
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\text{out}(N_i, h, w, C_j) = & \max_{m=0, \ldots, k_H-1} \max_{n=0, \ldots, k_W-1} \\
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& \text{input}(N_i, \text{stride[0]} \times h + m,
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\text{stride[1]} \times w + n, C_j),
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\end{aligned}
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The parameters ``kernel_size``, ``stride``, and ``padding`` can either be:
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where :math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
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:math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`.
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The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
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- a single ``int`` -- in which case the same value is used for both the
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height and width axis;
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- a ``tuple`` of two ``int`` s -- in which case, the first ``int`` is
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* a single ``int`` -- in which case the same value is used for both the
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height and width axis.
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* a ``tuple`` of two ``int`` s -- in which case, the first ``int`` is
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used for the height axis, the second ``int`` for the width axis.
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Args:
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@ -312,25 +285,14 @@ class MaxPool2d(_Pool2d):
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class AvgPool2d(_Pool2d):
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r"""Applies 2-dimensional average pooling.
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Assuming an input of shape :math:`(N, H, W, C)` and ``kernel_size`` is
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:math:`(k_H, k_W)`, the output is a tensor of shape :math:`(N, H_{out},
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W_{out}, C)`, given by:
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Spatially downsamples the input by taking the average of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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.. math::
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\begin{aligned}
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\text{out}(N_i, h, w, C_j) = & \frac{1}{k_H k_W} \sum_{m=0, \ldots, k_H-1} \sum_{n=0, \ldots, k_W-1} \\
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& \text{input}(N_i, \text{stride[0]} \times h + m,
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\text{stride[1]} \times w + n, C_j),
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\end{aligned}
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The parameters ``kernel_size``, ``stride``, and ``padding`` can either be:
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where :math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
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:math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`.
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The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
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- a single ``int`` -- in which case the same value is used for both the
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height and width axis;
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- a ``tuple`` of two ``int`` s -- in which case, the first ``int`` is
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* a single ``int`` -- in which case the same value is used for both the
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height and width axis.
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* a ``tuple`` of two ``int`` s -- in which case, the first ``int`` is
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used for the height axis, the second ``int`` for the width axis.
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Args:
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@ -359,28 +321,16 @@ class AvgPool2d(_Pool2d):
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class MaxPool3d(_Pool3d):
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"""
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Assuming an input of shape :math:`(N, D, H, W, C)` and ``kernel_size`` is
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:math:`(k_D, k_H, k_W)`, the output is a tensor of shape :math:`(N, D_{out},
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H_{out}, W_{out}, C)`, given by:
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r"""Applies 3-dimensional max pooling.
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.. math::
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\begin{aligned}
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\text{out}(N_i, d, h, w, C_j) = & \max_{l=0, \ldots, k_D-1} \max_{m=0, \ldots, k_H-1} \max_{n=0, \ldots, k_W-1} \\
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& \text{input}(N_i, \text{stride[0]} \times d + l,
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\text{stride[1]} \times h + m,
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\text{stride[2]} \times w + n, C_j),
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\end{aligned}
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Spatially downsamples the input by taking the maximum of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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where :math:`D_{out} = \left\lfloor\frac{D + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
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:math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`,
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:math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[2]} - \text{kernel\_size[2]}}{\text{stride[2]}}\right\rfloor + 1`.
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The parameters ``kernel_size``, ``stride``, and ``padding`` can either be:
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The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
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- a single ``int`` -- in which case the same value is used for the depth,
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height and width axis;
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- a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
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* a single ``int`` -- in which case the same value is used for the depth,
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height, and width axis.
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* a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
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for the depth axis, the second ``int`` for the height axis, and the third
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``int`` for the width axis.
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@ -410,28 +360,16 @@ class MaxPool3d(_Pool3d):
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class AvgPool3d(_Pool3d):
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"""
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Assuming an input of shape :math:`(N, D, H, W, C)` and ``kernel_size`` is
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:math:`(k_D, k_H, k_W)`, the output is a tensor of shape :math:`(N, D_{out},
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H_{out}, W_{out}, C)`, given by:
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r"""Applies 3-dimensional average pooling.
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.. math::
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\begin{aligned}
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\text{out}(N_i, d, h, w, C_j) = & \frac{1}{k_D k_H k_W} \sum_{l=0, \ldots, k_D-1} \sum_{m=0, \ldots, k_H-1} \sum_{n=0, \ldots, k_W-1} \\
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& \text{input}(N_i, \text{stride[0]} \times d + l,
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\text{stride[1]} \times h + m,
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\text{stride[2]} \times w + n, C_j),
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\end{aligned}
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Spatially downsamples the input by taking the average of a sliding window
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of size ``kernel_size`` and sliding stride ``stride``.
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where :math:`D_{out} = \left\lfloor\frac{D + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1`,
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:math:`H_{out} = \left\lfloor\frac{H + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1`,
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:math:`W_{out} = \left\lfloor\frac{W + 2 * \text{padding[2]} - \text{kernel\_size[2]}}{\text{stride[2]}}\right\rfloor + 1`.
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The parameters ``kernel_size``, ``stride``, and ``padding`` can either be:
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The parameters ``kernel_size``, ``stride``, ``padding``, can either be:
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- a single ``int`` -- in which case the same value is used for the depth,
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height and width axis;
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- a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
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* a single ``int`` -- in which case the same value is used for the depth,
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height, and width axis.
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* a ``tuple`` of three ``int`` s -- in which case, the first ``int`` is used
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for the depth axis, the second ``int`` for the height axis, and the third
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``int`` for the width axis.
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