tf.nn.depthwise_conv2d( input, filter, strides, padding, rate=None, name=None, data_format=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Convolution
Depthwise 2-D convolution.
Given a 4D input tensor ('NHWC' or 'NCHW' data formats) and a filter tensor of shape [filter_height, filter_width, in_channels, channel_multiplier]
containing in_channels
convolutional filters of depth 1, depthwise_conv2d
applies a different filter to each input channel (expanding from 1 channel to channel_multiplier
channels for each), then concatenates the results together. The output has in_channels * channel_multiplier
channels.
In detail,
output[b, i, j, k * channel_multiplier + q] = sum_{di, dj} filter[di, dj, k, q] * input[b, strides[1] * i + rate[0] * di, strides[2] * j + rate[1] * dj, k]
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.
input
: 4-D with shape according to data_format
.filter
: 4-D with shape [filter_height, filter_width, in_channels, channel_multiplier]
.strides
: 1-D of size 4. The stride of the sliding window for each dimension of input
.padding
: A string, either 'VALID'
or 'SAME'
. The padding algorithm. See the comment here
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".A 4-D Tensor
with shape according to data_format
. E.g., for "NHWC" format, shape is [batch, out_height, out_width, in_channels * channel_multiplier].
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d