tf.nn.depthwise_conv2d_native(input, filter, strides, padding, name=None)

tf.nn.depthwise_conv2d_native(input, filter, strides, padding, name=None)

See the guide: Neural Network > Convolution

Computes a 2-D depthwise convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel 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. Thus, the output has in_channels * channel_multiplier channels.

for k in 0..in_channels-1 for q in 0..channel_multiplier-1 output[b, i, j, k * channel_multiplier + q] = sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * filter[di, dj, k, q]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].

Args:

  • input: A Tensor. Must be one of the following types: float32, float64.
  • filter: A Tensor. Must have the same type as input.
  • strides: A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input.
  • padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.

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