convolution in tensorflow (tf.nn.conv)

本文详细解析了TensorFlow中卷积操作的概念及其应用。主要包括conv2d、depthwise_conv2d和separable_conv2d等不同类型的卷积运算,并讨论了它们之间的区别。此外,还介绍了tf.nn.conv系列函数在不同维度数据上的使用场景。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Understanding convolution in tensorflow

Points should be noticed

The convolution ops sweep a 2-D filter over a batch of images, applying the filter to each window of each image of the appropriate size. The different ops trade off between generic vs. specific filters:
conv2d: Arbitrary filters that can mix channels together.
depthwise_conv2d: Filters that operate on each channel independently.
separable_conv2d: A depthwise spatial filter followed by a pointwise
filter.

from:
Neural Network | TensorFlow
https://www.tensorflow.org/api_guides/python/nn#Convolution

Explanation of the equation in API of tf.nn.conv2d

这里写图片描述

b in yellow shadow is for travering through the batch with images.
the variables in green shadow is for locating a patch.
In the equation, di and dj loop variable to traverse through the height and width of the patch in one image.
~~I’m not very sure about this.
q is for traversing through the channels of the image, with the step of strike[3]. ~~

ref:
tf.nn.conv2d | TensorFlow
https://www.tensorflow.org/api_docs/python/tf/nn/conv2d

tf.nn.conv[1d, 2d, 3d]

There are tf.nn.conv1d, tf.nn.conv2d and tf.nn.conv3d. Problems are there, which to be choosed and what’s the difference.
I have not studied this deep. For I am using tensorflow to processing images, it seems tf.nn.conv2d is for me. From my perspective, the difference of them are as following.

  • tf.nn.conv1d is for some linear structure, text, sound or some others.
  • tf.nn.conv2d is for structure appeared to be 2-D, such as image.
  • tf.nn.conv3d is for something not familiar to me. It seems to be applied in signal processing.
tf.nn.conv2d is a function in TensorFlow that performs a 2D convolution operation on a given input tensor and a set of filters. It is typically used in deep learning models for image processing and computer vision tasks. The function takes several arguments, including the input tensor, the filter tensor, the strides for the convolution operation, and the padding scheme. The output of the convolution operation is a new tensor that represents the result of applying the filters to the input tensor. Here is an example usage of tf.nn.conv2d: ``` import tensorflow as tf # Define input and filter tensors input_tensor = tf.placeholder(tf.float32, shape=[None, 28, 28, 3]) filter_tensor = tf.Variable(tf.random_normal([5, 5, 3, 32])) # Perform a 2D convolution operation with strides of 1 and padding of 'SAME' conv = tf.nn.conv2d(input_tensor, filter_tensor, strides=[1, 1, 1, 1], padding='SAME') # Run the convolution operation within a TensorFlow session with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # Define a sample input tensor input_data = np.random.rand(1, 28, 28, 3) # Run the convolution operation on the input tensor conv_result = sess.run(conv, feed_dict={input_tensor: input_data}) ``` In this example, we define an input tensor with a shape of (None, 28, 28, 3), which represents a batch of 28x28 RGB images. We also define a filter tensor with a shape of (5, 5, 3, 32), which represents 32 5x5 filters that will be applied to the input tensor. We then call tf.nn.conv2d with the input and filter tensors, specifying a stride of 1 and a padding scheme of 'SAME'. This means that the output tensor will have the same spatial dimensions as the input tensor, and that the edges of the input tensor will be zero-padded to ensure that the filters can be applied to all pixels. Finally, we run the convolution operation within a TensorFlow session, providing a sample input tensor to test the operation. The resulting conv_result tensor will have a shape of (1, 28, 28, 32), representing a batch of 28x28 feature maps for each of the 32 filters applied to the input tensor.
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值