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写在前面:
实验目的:通过建立Alexnet神经网络建立模型并根据训练数据来训练模型 以达到可以将一张花的类别进行分类
Python版本:Python3
IDE:VSCode
系统:MacOS
数据集以及代码的资源放在文章末尾了 有需要请自取~
目录
训练模型代码 (附有注释)
训练结果 Accuracy展示
前言
本文仅作为学习训练 不涉及任何商业用途 如有错误或不足之处还请指出
数据集
数据集一共有五种花的类别 但本次实验模型仅用了rose和sunflower两种类别进行分类测试
五种花的类别:
Rose:
Sunflower:
训练模型代码 (附有注释)
import os , glob from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import matplotlib.pyplot as plt # 变量 resize = 224 # 图片尺寸参数 epochs = 8 # 迭代次数 batch_size = 5 # 每次训练多少张 #—————————————————————————————————————————————————————————————————————————————————— # 训练集路径 train_data_path = '/Users/liqun/Desktop/KS/MyPython/DataSet/flowers/Training' # 玫瑰花文件夹路径 rose_path = os.path.join(train_data_path,'rose') # 太阳花文件夹路径 sunflower_path = os.path.join(train_data_path,'sunflower') # 将文件夹内的图片读取出来 fpath_rose = [os.path.abspath(fp) for fp in glob.glob(os.path.join(rose_path,'*.jpg'))] fpath_sunflower = [os.path.abspath(fp) for fp in glob.glob(os.path.join(sunflower_path,'*.jpg'))] #文件数量 num_rose = len(fpath_rose) num_sunflower = len(fpath_sunflower) # 设置标签 label_rose = [0] * num_rose label_sunflower = [1] * num_sunflower # 展示 print('rose: ', num_rose) print('sunflower: ', num_sunflower) # 划分为多少验证集 RATIO_TEST = 0.1 num_rose_test = int(num_rose * RATIO_TEST) num_sunflower_test = int(num_sunflower * RATIO_TEST) # train fpath_train = fpath_rose[num_rose_test:] + fpath_sunflower[num_sunflower_test:] label_train = label_rose[num_rose_test:] + label_sunflower[num_sunflower_test:] # validation fpath_vali = fpath_rose[:num_rose_test] + fpath_sunflower[:num_sunflower_test] label_vali = label_rose[:num_rose_test] + label_sunflower[:num_sunflower_test] num_train = len(fpath_train) num_vali = len(fpath_vali) # 展示 print('num_train: ', num_train) print('num_label: ', num_vali) # 预处理函数 def preproc(fpath, label): image_byte = tf.io.read_file(fpath) # 读取文件 image = tf.io.decode_image(image_byte) # 检测图像是否为BMP,GIF,JPEG或PNG,并执行相应的操作将输入字节string转换为类型uint8的Tensor image_resize = tf.image.resize_with_pad(image, 224, 224) #缩放到224*224 image_norm = tf.cast(image_resize, tf.float32) / 255. #归一化 label_onehot = tf.one_hot(label, 2) return image_norm, label_onehot dataset_train = tf.data.Dataset.from_tensor_slices((fpath_train, label_train)) #将数据进行预处理 dataset_train = dataset_train.shuffle(num_train).repeat() #打乱顺序 dataset_train = dataset_train.map(preproc, num_parallel_calls=tf.data.experimental.AUTOTUNE) dataset_train = dataset_train.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE) #一批次处理多少份 dataset_vali = tf.data.Dataset.from_tensor_slices((fpath_vali, label_vali)) dataset_vali = dataset_vali.shuffle(num_vali).repeat() dataset_vali = dataset_vali.map(preproc, num_parallel_calls=tf.data.experimental.AUTOTUNE) dataset_vali = dataset_vali.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE) #—————————————————————————————————————————————————————————————————————————————————— # 建立模型 卷积神经网络 model = tf.keras.Sequential(name='Alexnet') # 第一层 model.add(layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), padding='valid', input_shape=(resize,resize,3), activation='relu')) model.add(layers.BatchNormalization()) # 第一层池化层:最大池化层 model.add(layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid')) #第二层 model.add(layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), padding='same', activation='relu')) model.add(layers.BatchNormalization()) #第二层池化层 model.add(layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid')) #第三层 model.add(layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')) #第四层 model.add(layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')) #第五层 model.add(layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')) #池化层 model.add(layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid')) #第6,7,8层 model.add(layers.Flatten()) model.add(layers.Dense(4096, activation='relu')) model.add(layers.Dropout(0.5)) model.add(layers.Dense(4096, activation='relu')) model.add(layers.Dropout(0.5)) model.add(layers.Dense(1000, activation='relu')) model.add(layers.Dropout(0.5)) # Output Layer model.add(layers.Dense(2, activation='softmax')) # Training 优化器 随机梯度下降算法 model.compile(loss='categorical_crossentropy', optimizer='sgd', #梯度下降法 metrics=['accuracy']) history = model.fit(dataset_train, steps_per_epoch = num_train//batch_size, epochs = epochs, #迭代次数 validation_data = dataset_vali, validation_steps = num_vali//batch_size, verbose = 1) # 评分标准 scores_train = model.evaluate(dataset_train, steps=num_train//batch_size, verbose=1) print(scores_train) scores_vali = model.evaluate(dataset_vali, steps=num_vali//batch_size, verbose=1) print(scores_vali) #保存模型 model.save('/Users/liqun/Desktop/KS/MyPython/project/flowerModel.h5') ''' history对象的history内容(history.history)是字典类型, 键的内容受metrics的设置影响,值的长度与epochs值一致。 ''' history_dict = history.history train_loss = history_dict['loss'] train_accuracy = history_dict['accuracy'] val_loss = history_dict['val_loss'] val_accuracy = history_dict['val_accuracy'] # Draw loss plt.figure() plt.plot(range(epochs), train_loss, label='train_loss') plt.plot(range(epochs), val_loss, label='val_loss') plt.legend() plt.xlabel('epochs') plt.ylabel('loss') # Draw accuracy plt.figure() plt.plot(range(epochs), train_accuracy, label='train_accuracy') plt.plot(range(epochs), val_accuracy, label='val_accuracy') plt.legend() plt.xlabel('epochs') plt.ylabel('accuracy') # Display plt.show() print('Train has finished')
训练集数据量展示
训练迭代过程展示
训练结果 Accuracy展示
训练结果 Loss展示
测试集
预测结果代码
import cv2 from tensorflow.keras.models import load_model resize = 224 label = ('rose', 'sunflower') image = cv2.resize(cv2.imread('/Users/liqun/Desktop/KS/MyPython/DataSet/flowers/Training/sunflower/23286304156_3635f7de05.jpg'),(resize,resize)) image = image.astype("float") / 255.0 # 归一化 image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) # 加载模型 model = load_model('/Users/liqun/Desktop/KS/MyPython/project/flowerModel.h5') predict = model.predict(image) i = predict.argmax(axis=1)[0] # 展示结果 print('——————————————————————') print('Predict result') print(label[i],':',max(predict[0])*100,'%')
预测结果展示
结语
模型到这里就训练并检测完毕了 如有需要的小伙伴可以下载下方的数据集测试集及源代码
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