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@ -1182,19 +1182,19 @@ def conv2d(input,
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- Input:
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
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Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
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Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
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- Output:
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Output shape: $(N, C_{out}, H_{out}, W_{out})$
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Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
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Where
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.. math::
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H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
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W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
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H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
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W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
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Args:
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input(Variable): The input image with [N, C, H, W] format.
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