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TensorFlow 1 version
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View source on GitHub
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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 | |
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input
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A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
Shape [batch, in_depth, in_height, in_width, in_channels].
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filters
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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.
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strides
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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.
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padding
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A string from: "SAME", "VALID".
The type of padding algorithm to use.
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data_format
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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].
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dilations
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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.
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name
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A name for the operation (optional). |
Returns | |
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A Tensor. Has the same type as input.
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