tf.nn.conv3d(input, filter, strides, padding, name=None)
See the guide: Neural Network > Convolution
Computes a 3-D convolution given 5-D
In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product.
Our Conv3D implements a form of cross-correlation.
Tensor. Must be one of the following types:
[batch, in_depth, in_height, in_width, in_channels].
Tensor. Must have the same type as
[filter_depth, filter_height, filter_width, in_channels, out_channels].
in_channelsmust match between
strides: A list of
intsthat has length
>= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of
input. Must have
strides = strides = 1.
"SAME", "VALID". The type of padding algorithm to use.
name: A name for the operation (optional).
Tensor. Has the same type as