# tf.nn.erosion2d

tf.nn.erosion2d(
value,
kernel,
strides,
rates,
name=None
)


Defined in tensorflow/python/ops/nn_ops.py.

See the guide: Neural Network > Morphological filtering

Computes the grayscale erosion of 4-D value and 3-D kernel tensors.

The value tensor has shape [batch, in_height, in_width, depth] and the kernel tensor has shape [kernel_height, kernel_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 - rates[1] * dy,
strides[2] * x - rates[2] * dx,
c] -
kernel[dy, dx, c]


Duality: The erosion of value by the kernel is equal to the negation of the dilation of -value by the reflected kernel.

#### Args:

• value: A Tensor. 4-D with shape [batch, in_height, in_width, depth].
• kernel: A Tensor. Must have the same type as value. 3-D with shape [kernel_height, kernel_width, depth].
• strides: 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].
• rates: 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].
• padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
• name: A name for the operation (optional). If not specified "erosion2d" is used.

#### Returns:

A Tensor. Has the same type as value. 4-D with shape [batch, out_height, out_width, depth].

#### Raises:

• ValueError: If the value depth does not match kernel' shape, or if padding is other than 'VALID' or 'SAME'.