如何在Keras中使用Conv2D在5D张量的最后三个维度上应用卷积? [英] How to apply convolution on the last three dimensions of a 5D tensor using the Conv2D in Keras?

问题描述

通常，Keras中Conv2D的输入张量是尺寸为batch_size * n * n * channel_size的4D张量.现在我有一个尺寸为batch_size * N * n * n * channel_size的5D张量，我想为N中每个i的最后三个尺寸应用2D卷积层.例如，如果内核大小为1，则我希望输出的尺寸为batch_size * N * n * n * 1.

Usually the input tensor of the Conv2D in Keras is a 4D tensor with the dimension batch_size * n * n * channel_size. Now I have a 5D tensor with the dimension batch_size * N * n * n * channel_size and I want to apply the 2D convolutional layer for the last three dimensions for each i in N. For example, if the kernel size is 1, then I expect that the output will have the dimension batch_size * N * n * n * 1.

任何人都知道一些使用Keras实施它的简单方法吗?

Anyone knows some easy ways to implement it with Keras?

例如，对于完全连接的层，Keras可以自动完成.如果输入的形状为batch_size * N * n，则Keras中的Dense层将为N中的每个i设置一个FC层.因此，如果设置Dense(m)，我们将使用batch_size * N * m获得输出.

For example, for the fully-connected layer Keras can do it automatically. If the input has the shape batch_size * N * n, then the Dense layer in Keras will set a FC layer for each i in N. Hence we will get the output with batch_size * N * m, if we set Dense(m).

推荐答案

您可以使用 图层包装器，即可在5D张量中的所有图像上应用相同的卷积图层.例如:

You can use the TimeDistributed layer wrapper to apply the same convolution layer on all the images in the 5D tensor. For example:

model = Sequential()
model.add(TimeDistributed(Conv2D(5, (3,3), padding='same'), input_shape=(10, 100, 100, 3)))

model.summary()

模型摘要:

Layer (type)                 Output Shape              Param #   
=================================================================
time_distributed_2 (TimeDist (None, 10, 100, 100, 5)   140       
=================================================================
Total params: 140
Trainable params: 140
Non-trainable params: 0
_________________________________________________________________

这篇关于如何在Keras中使用Conv2D在5D张量的最后三个维度上应用卷积?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！