Note - Convolution Network: convoluted shape tensor calculation

Front:

• The shape of the factors affecting shape:
  • 1. convolution kernel size
  • 2.stride step
  • 3.padding mode

official:

• K - the number of convolution kernels
• F - convolution kernel size
• S - step
• P - filled peripheryThe number of layers

use

• Apparently under the valid mode, direct volume, not discarded

我推导的valid模式下的计算方式(以 W 举例)：
W2 = (W1 - F)/S + 1

• SAME mode
  • How to determine P
    • Equation 1 using the calculated theoretical output shape, and then using the formula P 2 backstepping

      Official 1
      
      Official 2
      

5×5的图像
3×3的卷积核
步长 2
padding SAME

import tensorflow as tf
import numpy as np
pic = tf.constant(value=np.ones((5, 5), dtype=np.float32), shape=(1,5,5,1))
W = tf.constant(value=np.ones((3, 3), dtype=np.float32), shape=(3,3,1,1))
output = tf.nn.conv2d(pic, W, strides=[1, 2, 2, 1], padding='SAME')

with tf.Session() as sess:

    print(output.eval().shape)

"""
运行结果：
(1, 3, 3, 1)
"""

to sum up

• Analyzing output tensor shape shape
  

reference

Depth learning the basics - convolution calculation formula and pooling
appreciated CNN layer and the convolution calculation cell layer