Computes a 3-D convolution given 5-D `input`

and `filter`

tensors.

tf.compat.v1.nn.conv3d( input, filter=None, strides=None, padding=None, data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None, filters=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]` . |

`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]. |

`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` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/nn/conv3d