tf.nn.conv3d

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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.