简介
我们把神经网络比作眼睛，我们看看卷积神经网络（CNN）能够观察到什么：
[站外图片上传中...(image-d0c56d-1609856640469)]
基础条件：-
- 读者知道如何构建CNN模型。
- 读者了解可训练的参数计算以及各个中间层的输入和输出的大小。
注意：在这里，我们只关心构建CNN模型并观察其特征图（feature map），我们不关心模型的准确性。
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img import os import numpy as np import matplotlib.pyplot as plt
现在，在不浪费时间的情况下，让我们建立一个CNN模型：
model=tf.keras.models.Sequential([ tf.keras.layers.Conv2D(8,(3,3),activation ='relu', input_shape=(150,150,3)), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Conv2D(16,(3,3),activation ='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Conv2D(32,(3,3),activation ='relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(1024,activation='relu'), tf.keras.layers.Dense(512,activation='relu'), tf.keras.layers.Dense(3,activation='softmax') ])
该模型的summary是：
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 148, 148, 8) 224 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 74, 74, 8) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 72, 72, 16) 1168 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 36, 36, 16) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 34, 34, 32) 4640 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 17, 17, 32) 0 _________________________________________________________________ flatten (Flatten) (None, 9248) 0 _________________________________________________________________ dense (Dense) (None, 1024) 9470976 _________________________________________________________________ dense_1 (Dense) (None, 512) 524800 _________________________________________________________________ dense_2 (Dense) (None, 3) 1539 ================================================================= Total params: 10,003,347 Trainable params: 10,003,347 Non-trainable params: 0 _________________________________________________________________
正如我们在上面看到的，我们具有三个卷积层，其后是MaxPooling层，两个全连接层和一个输出全连接层。
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