tf.nn.conv3d(input, filter, strides, padding, data_format=None, name=None)

Type: function

Docstring:

Computes a 3-D convolution given 5-D input and filter tensors.

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: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Shape [batch, in_depth, in_height, in_width, in_channels].
filter: 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 filter.
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].
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.