图像分类模型训练与预测 - 基于VGG16迁移学习

import os
import random
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, BatchNormalization, GlobalAveragePooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import VGG16

train_dir = 'C:/Users/chaofan/Desktop/class/class/archive/seg_train/seg_train' # 训练集目录
val_dir = 'C:/Users/chaofan/Desktop/class/class/archive/seg_test/seg_test'    #测试集目录
classes = os.listdir(train_dir)  # 获取类别列表
batch_size = 64  # 批大小
IMG_HEIGHT = 150  # 图像高度
IMG_WIDTH = 150  # 图像宽度
epochs = 10  # 训练轮数

train_image_generator = ImageDataGenerator(
    rescale=1./255,  # 归一化
    horizontal_flip=True  # 水平翻转
)

val_image_generator = ImageDataGenerator(
    rescale=1./255  # 归一化
)

train_data_gen = train_image_generator.flow_from_directory(
    batch_size=batch_size,  # 批大小
    directory=train_dir,  # 训练集目录
    shuffle=True,  # 是否打乱数据
    target_size=(IMG_HEIGHT, IMG_WIDTH),  # 图像大小
    class_mode='categorical'  # 分类方式
)

val_data_gen = val_image_generator.flow_from_directory(
    batch_size=batch_size,  # 批大小
    directory=val_dir,  # 验证集目录
    shuffle=True,  # 是否打乱数据
    target_size=(IMG_HEIGHT, IMG_WIDTH),  # 图像大小
    class_mode='categorical'  # 分类方式
)

total_train = len(train_data_gen)  # 训练集样本数
total_val = len(val_data_gen)  # 验证集样本数

print('总训练数据批次数：', total_train)
print('总验证数据批次数： ', total_val)

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3))

for layer in base_model.layers:
    layer.trainable = False

model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(len(classes), activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

history = model.fit(
    train_data_gen,  # 训练集数据生成器
    steps_per_epoch=total_train,  # 每轮迭代的步数
    epochs=epochs,  # 训练轮数
    validation_data=val_data_gen,  # 验证集数据生成器
    validation_steps=total_val  # 验证集每轮迭代的步数
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()), 1])
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0, 1.0])
plt.title('Training and Validation Loss')
plt.xlabel('epoch')

plt.show()

# 从测试集随机抽取5张图片
test_images = []
test_labels = []
for i in range(5):
    random_class = random.choice(classes)
    random_image = random.choice(os.listdir(os.path.join(val_dir, random_class)))
    img_path = os.path.join(val_dir, random_class, random_image)
    img = image.load_img(img_path, target_size=(IMG_HEIGHT, IMG_WIDTH))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = img / 255.0  # 归一化图像
    test_images.append(img)
    test_labels.append(random_class)

test_images = np.vstack(test_images)
test_labels = np.array(test_labels)

# 对选定的图像进行预测
predictions = model.predict(test_images)
predicted_labels = [classes[np.argmax(pred)] for pred in predictions]

# 显示图像及其预测标签
plt.figure(figsize=(15, 10))
for i in range(5):
    plt.subplot(1, 5, i+1)
    plt.imshow(test_images[i])
    plt.title(f'Predicted: {predicted_labels[i]}
True: {test_labels[i]}')
    plt.axis('off')

plt.show()

这段代码从测试集中随机抽取5张图像，使用训练好的模型对它们进行预测，并显示图像及其预测标签。