tf.nn.conv3d( input, filter, strides, padding, data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None )
Defined in generated file:
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.
data_format: An optional
"NDHWC", "NCDHW". Defaults to
"NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
dilations: An optional list of
ints. Defaults to
[1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of
input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of
data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
name: A name for the operation (optional).
Tensor. Has the same type as