TensorFlow 1 version | View source on GitHub |
Computes a 3-D convolution given 5-D input
and filters
tensors.
tf.nn.conv3d(
input, filters, strides, padding, data_format='NDHWC', dilations=None,
name=None
)
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.
Args | |
---|---|
input
|
A Tensor . Must be one of the following types: half , bfloat16 , float32 , float64 .
Shape [batch, in_depth, in_height, in_width, in_channels] .
|
filters
|
A Tensor . Must have the same type as input .
Shape [filter_depth, filter_height, filter_width, in_channels,
out_channels] . in_channels must match between input and filters .
|
strides
|
A list of ints that has length >= 5 .
1-D tensor of length 5. The stride of the sliding window for each
dimension of input . Must have strides[0] = strides[4] = 1 .
|
padding
|
A string from: "SAME", "VALID" .
The type of padding algorithm to use.
|
data_format
|
An optional string from: "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). |
Returns | |
---|---|
A Tensor . Has the same type as input .
|