tf.nn.conv3d(input, filter, strides, padding, name=None) 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 = strides = 1. padding: A string from: "SAME", "VALID". The type of padding algorithm to use. name: A name for the operation (optional). Returns: A Tensor. Has the same type as input.
filter的shape也多个 filter_depth.在conv2d中, filter_height, filter_height构成感受眼的大小.在conv3d中,由filter_depth,filter_height,filter_width构成了感受眼的大小
out_channels（卷积核的个数， 一个卷积核的shape是 [depth, height, width]）