基于深度学习的卷积神经网络的四种动物识别案例分享（含数据集）.zip

#coding=UTF8 # 昵 称:XieXu # 时 间: 2022/11/21/0021 14:54 # 【设置GPU】 import tensorflow as tf gpus = tf.config.list_physical_devices("GPU") if gpus: tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用 tf.config.set_visible_devices([gpus[0]],"GPU") # 打印显卡信息，确认GPU可用 print(gpus) # 【导入数据集，查看数据】 import matplotlib.pyplot as plt # 支持中文 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 import os,PIL # 设置随机种子尽可能使结果可以重现 import numpy as np np.random.seed(1) # 设置随机种子尽可能使结果可以重现 import tensorflow as tf tf.random.set_seed(1) import pathlib # data_dir = "./data" data_dir = "./data" data_dir = pathlib.Path(data_dir) image_count = len(list(data_dir.glob('*/*'))) print("图片总数为：",image_count) batch_size = 4 img_height = 299 img_width = 299 """ 关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789 """ # 划分 训练集 和 测试集 train_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="training", seed=12, image_size=(img_height, img_width), batch_size=batch_size) # 上述 输出： # Found 4000 files belonging to 4 classes. # Using 3200 files for training. val_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="validation", seed=12, image_size=(img_height, img_width), batch_size=batch_size) # 上述输出 # Found 4000 files belonging to 4 classes. # Using 800 files for validation. class_names = train_ds.class_names print('class_names:',class_names) # class_names: ['cat', 'chook', 'dog', 'horse'] for image_batch, labels_batch in train_ds: print('image_batch.shape:',image_batch.shape) # image_batch.shape: (4, 299, 299, 3) print('labels_batch.shape:',labels_batch.shape) # labels_batch.shape: (4,) break AUTOTUNE = tf.data.AUTOTUNE train_ds = ( train_ds.cache() .shuffle(1000) # .map(train_preprocessing) # 这里可以设置预处理函数 # .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size .prefetch(buffer_size=AUTOTUNE) ) val_ds = ( val_ds.cache() .shuffle(1000) # .map(val_preprocessing) # 这里可以设置预处理函数 # .batch(batch_size) # 在image_dataset_from_directory处已经设置了batch_size .prefetch(buffer_size=AUTOTUNE) ) # ====================================# # Xception的网络部分 # ====================================# # from tensorflow.keras.preprocessing import image from keras.preprocessing import image from tensorflow.python.keras.models import Model from tensorflow.python.keras import layers # from tensorflow.python.keras.layers import Dense, Input, BatchNormalization, Activation, Conv2D, SeparableConv2D, MaxPooling2D from tensorflow.python.keras.layers import Dense, Input, Activation, Conv2D, SeparableConv2D, MaxPooling2D from keras.layers import BatchNormalization from tensorflow.python.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D from tensorflow.python.keras import backend as K from keras.applications.imagenet_utils import decode_predictions # from tensorflow.python import keras # AttributeError: module 'tensorflow.python.keras' has no attribute 'model' from keras.models import load_model def Xception(input_shape=[299, 299, 3], classes=1000): img_input = Input(shape=input_shape) # =================# # Entry flow # =================# # block1 # 299,299,3 -> 149,149,64 x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) # block2 # 149,149,64 -> 75,75,128 residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) # block3 # 75,75,128 -> 38,38,256 residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) # block4 # 38,38,256 -> 19,19,728 residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) x = layers.add([x, residual]) # =================# # Middle flow # =================# # block5--block12 # 19,19,728 -> 19,19,728 for i in range(8): residual = x prefix = 'block' + str(i + 5) x = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) # =================# # Exit flow # =================# # block13 # 19,19,728 -> 10,10,1024 residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableCon