tf.compat.v1.nn.separable_conv2d

2-D convolution with separable filters.

tf.compat.v1.nn.separable_conv2d(
    input, depthwise_filter, pointwise_filter, strides, padding, rate=None,
    name=None, data_format=None, dilations=None
)

Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions [1, 2] and 3, not spatial separability between dimensions 1 and 2.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q, r}
    input[b, strides[1] * i + di, strides[2] * j + dj, q] *
    depthwise_filter[di, dj, q, r] *
    pointwise_filter[0, 0, q * channel_multiplier + r, k]

strides controls the strides for the depthwise convolution only, since the pointwise convolution has implicit strides of [1, 1, 1, 1]. Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertical strides, strides = [1, stride, stride, 1]. If any value in rate is greater than 1, we perform atrous depthwise convolution, in which case all values in the strides tensor must be equal to 1.

Args
`input`	4-D `Tensor` with shape according to `data_format`.
`depthwise_filter`	4-D `Tensor` with shape `[filter_height, filter_width, in_channels, channel_multiplier]`. Contains `in_channels` convolutional filters of depth 1.
`pointwise_filter`	4-D `Tensor` with shape `[1, 1, channel_multiplier * in_channels, out_channels]`. Pointwise filter to mix channels after `depthwise_filter` has convolved spatially.
`strides`	1-D of size 4. The strides for the depthwise convolution for each dimension of `input`.
`padding`	Controls how to pad the image before applying the depthwise convolution. Can be the string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a Python list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`.
`rate`	1-D of size 2. The dilation rate in which we sample input values across the `height` and `width` dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1.
`name`	A name for this operation (optional).
`data_format`	The data format for input. Either "NHWC" (default) or "NCHW".
`dilations`	Alias of rate.

Returns
A 4-D `Tensor` with shape according to 'data_format'. For example, with data_format="NHWC", shape is [batch, out_height, out_width, out_channels].

tf.compat.v1.nn.separable_conv2d

Args

Returns