企业官网建设流程全解析

简介:

1.基于VGG16模型进行特征提取, 结合mlp实现猫狗二分类

2.训练数据--"dog_cat_class\training_set"

3.模型训练流程

1.对图像数据进行导入和预处理

2.搭建模型, 导入VGG16模型, 去除mlp层, 将经过VGG16训练后的数据作为输入, 输入到自建的mlp层中进行训练,

要求:

hidden layers=1, units=10, activation=relu

out layer:units=1, activation=sigmoid

3.对模型进行评估和预测

4.随机下载百度的12张猫/狗的图片, 对模型进行实战测试

4.代码实现(推荐直接看第二个, 比较规范)

4.1, 小垃圾写的(我写的)

# ============================================================ # 1.数据集导入, 单张图片导入, 可以通过load_image导入 from keras.preprocessing.image import load_img, img_to_array img_path=r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\dogs\dog.1.jpg" img_data=load_img(img_path, target_size=(224,224)) img_data=img_to_array(img_data) # print(img_data.shape) # type(img_data) # 2.模型搭建和模型训练 # 对图像增加一个维度, 然后通过图像预处理, 成为合格的图像输入格式, from keras.applications.vgg16 import preprocess_input import numpy as np x_train=np.expand_dims(img_data, axis=0) # 这里最好换一个名字, 如果接受变量还是img_data的话, 当再一次执行这个代码单元, 会增加数组的维度 x_train=preprocess_input(x_train) # print(x_train.shape) # 将合格的图片数据输入到VGG16模型中 from keras.applications.vgg16 import VGG16 extract_model=VGG16(include_top=False, weights='imagenet') img_features=extract_model.predict(x_train) print(img_features.shape) # 实现对图片的批量读入 import numpy as np from keras.applications.vgg16 import VGG16 vgg=VGG16(include_top=False, weights='imagenet') from keras.preprocessing.image import load_img, img_to_array from keras.applications.vgg16 import preprocess_input def model_prepro(model, img_path): img_data=load_img(img_path, target_size=(224,224)) img_array=img_to_array(img_data) x_train=np.expand_dims(img_array, axis=0) x_train=preprocess_input(x_train) x_vgg=model.predict(x_train) x_vgg=x_vgg.reshape(1, 25088) return x_vgg import os # file_path=r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\"+sub_file file_path1=r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\cat" img_name_list = os.listdir(file_path1) img_list=[] for i in img_name_list: if os.path.splitext(i)[1]=='.jpg': img_list.append(i) img_path_list=[os.path.join(file_path1, i) for i in img_list] img_feature_array1=np.zeros([len(img_path_list), 25088]) for i in range(len(img_path_list)): img_feature=model_prepro(vgg, img_path_list[i]) img_feature_array1[i]=img_feature # 显示正在处理的图片 print("preprecessing is"+img_list[i]) ##################################################### file_path2=r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\dog" img_name_list = os.listdir(file_path2) img_list=[] for i in img_name_list: if os.path.splitext(i)[1]=='.jpg': img_list.append(i) img_path_list=[os.path.join(file_path2, i) for i in img_list] img_feature_array2=np.zeros([len(img_path_list), 25088]) for i in range(len(img_path_list)): img_feature=model_prepro(vgg, img_path_list[i]) img_feature_array2[i]=img_feature # 显示正在处理的图片 print("preprecessing is"+img_list[i]) print(img_feature_array1.shape, img_feature_array2.shape) y1=np.zeros(600) y2=np.ones(602) x_all=np.concatenate((img_feature_array1, img_feature_array2), axis=0) y_all=np.concatenate((y1, y2), axis=0) y_all=y_all.reshape(-1,1) print(x_all.shape, y_all.shape) # 分割数据集 from sklearn.model_selection import train_test_split x_train, x_test,y_train, y_test = train_test_split(x_all, y_all, test_size=0.3, random_state=10) print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) # MLP模型搭建和训练 from keras.models import Sequential vgg_model=Sequential() from keras.layers import Dense vgg_model.add(Dense(units=10, input_dim=25088, activation='relu')) vgg_model.add(Dense(units=1, activation='sigmoid')) vgg_model.compile(optimizer='adam', metrics=['accuracy'], loss='binary_crossentropy') vgg_model.fit(x_train, y_train, epochs=50) vgg_model.summary() # ========================================================================== # 训练集预测 y_train_predict=vgg_model.predict(x_train) y_train_predict=np.argmax(y_train_predict, axis=1) print(y_train_predict.shape) # 计算train准确率 from sklearn.metrics import accuracy_score accuracy_score=accuracy_score(y_train, y_train_predict) print("accuracy is ", accuracy_score) # 测试集预测 y_test_predict=vgg_model.predict(x_test) y_test_predict=np.argmax(y_test_predict, axis=1) print(y_test_predict.shape) # 计算test准确率 from sklearn.metrics import accuracy_score accuracy_score=accuracy_score(y_test, y_test_predict) print("accuracy is ", accuracy_score) # ==================================================================== # 在网上下载图片, 进行随机测试 from keras.preprocessing.image import load_img, img_to_array pic_animal=r"C:\Users\鹰\Desktop\Dog+Cat\11.jpg" pic_animal=load_img(pic_animal, target_size=(224,224)) pic_animal=img_to_array(pic_animal) x_train=np.expand_dims(pic_animal, axis=0) x_train=preprocess_input(x_train) # 特征提取 features=vgg.predict(x_train) x=features.reshape(1, -1) print(x.shape) print(features.shape) y_predict=vgg_model.predict(x) import numpy as np y_predict=np.argmax(y_predict, axis=1) print("result is :", y_predict) # 结果为0--猫, 结果为1--狗

结果是...

4.2: 千问大模型修改后的

import numpy as np from keras.preprocessing.image import load_img, img_to_array from keras.applications.vgg16 import preprocess_input, VGG16 from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from sklearn.metrics import accuracy_score import os # 1. 数据集导入, 单张图片导入, 可以通过load_image导入 img_path = r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\dogs\dog.1.jpg" img_data = load_img(img_path, target_size=(224, 224)) img_data = img_to_array(img_data) print("Single image shape:", img_data.shape) # 2. 模型搭建和模型训练 def model_prepro(model, img_path): img_data = load_img(img_path, target_size=(224, 224)) img_array = img_to_array(img_data) x_train = np.expand_dims(img_array, axis=0) x_train = preprocess_input(x_train) x_vgg = model.predict(x_train) x_vgg = x_vgg.reshape(1, -1) return x_vgg # 加载 VGG16 模型 vgg = VGG16(include_top=False, weights='imagenet') # 处理 cat 文件夹 file_path1 = r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\cat" img_name_list = os.listdir(file_path1) img_list = [i for i in img_name_list if os.path.splitext(i)[1].lower() == '.jpg'] img_path_list = [os.path.join(file_path1, i) for i in img_list] img_feature_array1 = np.zeros([len(img_path_list), 25088]) for i in range(len(img_path_list)): img_feature = model_prepro(vgg, img_path_list[i]) img_feature_array1[i] = img_feature print(f"Processing: {img_list[i]} (Cat)") # 处理 dog 文件夹 file_path2 = r"C:\Users\鹰\Desktop\ML_Set\dog_cat_class\training_set\dog" img_name_list = os.listdir(file_path2) img_list = [i for i in img_name_list if os.path.splitext(i)[1].lower() == '.jpg'] img_path_list = [os.path.join(file_path2, i) for i in img_list] img_feature_array2 = np.zeros([len(img_path_list), 25088]) for i in range(len(img_path_list)): img_feature = model_prepro(vgg, img_path_list[i]) img_feature_array2[i] = img_feature print(f"Processing: {img_list[i]} (Dog)") print("Feature array shapes:", img_feature_array1.shape, img_feature_array2.shape) # 创建标签 y1 = np.zeros(len(img_feature_array1)) y2 = np.ones(len(img_feature_array2)) # 合并特征和标签 x_all = np.concatenate((img_feature_array1, img_feature_array2), axis=0) y_all = np.concatenate((y1, y2), axis=0) y_all = y_all.reshape(-1, 1) print("Combined data shapes:", x_all.shape, y_all.shape) # 分割数据集 x_train, x_test, y_train, y_test = train_test_split(x_all, y_all, test_size=0.3, random_state=10) print("Data split shapes:", x_train.shape, x_test.shape, y_train.shape, y_test.shape) # MLP模型搭建和训练 vgg_model = Sequential() vgg_model.add(Dense(units=128, input_dim=25088, activation='relu')) vgg_model.add(Dense(units=64, activation='relu')) vgg_model.add(Dense(units=1, activation='sigmoid')) vgg_model.compile(optimizer='adam', metrics=['accuracy'], loss='binary_crossentropy') vgg_model.fit(x_train, y_train, epochs=100, batch_size=32, validation_data=(x_test, y_test)) vgg_model.summary() # 训练集预测 y_train_predict = vgg_model.predict(x_train) y_train_predict = (y_train_predict > 0.5).astype(int) # 使用阈值 0.5 进行二分类 print("Train prediction shape:", y_train_predict.shape) # 计算train准确率 train_accuracy = accuracy_score(y_train, y_train_predict) print("Train accuracy is:", train_accuracy) # 测试集预测 y_test_predict = vgg_model.predict(x_test) y_test_predict = (y_test_predict > 0.5).astype(int) # 使用阈值 0.5 进行二分类 print("Test prediction shape:", y_test_predict.shape) # 计算test准确率 test_accuracy = accuracy_score(y_test, y_test_predict) print("Test accuracy is:", test_accuracy) # 在网上下载图片, 进行随机测试 pic_animal = r"C:\Users\鹰\Desktop\Dog+Cat\11.jpg" pic_animal = load_img(pic_animal, target_size=(224, 224)) pic_animal = img_to_array(pic_animal) x_train = np.expand_dims(pic_animal, axis=0) x_train = preprocess_input(x_train) # 特征提取 features = vgg.predict(x_train) x = features.reshape(1, -1) print("Feature shape:", x.shape) print("Feature shape before reshape:", features.shape) # 预测 y_predict = vgg_model.predict(x) y_predict = (y_predict > 0.5).astype(int) # 使用阈值 0.5 进行二分类 print("Prediction result is:", "猫" if y_predict[0][0] == 0 else "狗")

结果是...

这对我的心灵的伤害是百分百的暴击, 我的是反面教材........

想要看正版规范代码, 就看第二个, 当然,

如果觉得50%的成功率还行的话, 那我的勉强也能看

兄弟们不嫌弃的话, 也可以看看, 吸取一下经验教训, 看个乐子

5.扩展

扩展1:

keras.models 模块中的主要组成部分:

1.Sequential 模型是一种线性堆叠的层结构，适用于大多数简单的神经网络

2.Functional API 是一种更灵活的模型构建方式，允许创建复杂的非线性拓扑结构

扩展2:

keras.applications 导入 VGG16 时，你可以得到以下主要部分：

VGG16 Model: 这是整个 VGG16 网络模型，可以直接用来进行预测或者作为迁移学习的基础。

Preprocess Input: 一个函数，用于对输入图像数据进行预处理，以便与 VGG16 模型兼容。实现:from keras.applications.vgg16 import preprocess_input。

Decode Predictions: 一个函数，用于将 VGG16 模型的输出转换为人类可读的标签。实现:from keras.applications.vgg16 import decode_predictions。

Weights: 预训练的权重文件。这些权重是在 ImageNet 数据集上训练得到的，可以帮助你在自己的任务上快速获得较好的性能。

6.数据集链接:

官网:

Cat and Dog | KaggleCats and Dogs dataset to train a DL modelhttps://www.kaggle.com/datasets/tongpython/cat-and-dog?resource=download

百度网盘分享:

链接：https://pan.baidu.com/s/1T1mymwIqOOF3MKfWxRtnpQ
提取码：6axn