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TensorFlow 1 version
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View source on GitHub
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Computes the grayscale erosion of 4-D value and 3-D filters tensors.
tf.nn.erosion2d(
value, filters, strides, padding, data_format, dilations, name=None
)
The value tensor has shape [batch, in_height, in_width, depth] and the
filters tensor has shape [filters_height, filters_width, depth], i.e.,
each input channel is processed independently of the others with its own
structuring function. The output tensor has shape
[batch, out_height, out_width, depth]. The spatial dimensions of the
output tensor depend on the padding algorithm. We currently only support the
default "NHWC" data_format.
In detail, the grayscale morphological 2-D erosion is given by:
output[b, y, x, c] =
min_{dy, dx} value[b,
strides[1] * y - dilations[1] * dy,
strides[2] * x - dilations[2] * dx,
c] -
filters[dy, dx, c]
Duality: The erosion of value by the filters is equal to the negation of
the dilation of -value by the reflected filters.
Args | |
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value
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A Tensor. 4-D with shape [batch, in_height, in_width, depth].
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filters
|
A Tensor. Must have the same type as value.
3-D with shape [filters_height, filters_width, depth].
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strides
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A list of ints that has length >= 4.
1-D of length 4. The stride of the sliding window for each dimension of
the input tensor. Must be: [1, stride_height, stride_width, 1].
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padding
|
A string from: "SAME", "VALID".
The type of padding algorithm to use.
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data_format
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A string, only "NHWC" is currently supported.
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dilations
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A list of ints that has length >= 4.
1-D of length 4. The input stride for atrous morphological dilation.
Must be: [1, rate_height, rate_width, 1].
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name
|
A name for the operation (optional). If not specified "erosion2d" is used. |
Returns | |
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A Tensor. Has the same type as value.
4-D with shape [batch, out_height, out_width, depth].
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Raises | |
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ValueError
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If the value depth does not match filters' shape, or if
padding is other than 'VALID' or 'SAME'.
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