tf.nn.conv3d(input, filter, strides, padding, name=None)
See the guide: Neural Network > Convolution
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.
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.name
: A name for the operation (optional).A Tensor
. Has the same type as input
.
Defined in tensorflow/python/ops/gen_nn_ops.py
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/conv3d