嗨害大家好鸭！我是爱摸鱼的芝士❤
宠物真的看着好治愈
谁不想有一只属于自己的乖乖宠物捏~
这篇文章中我放弃了以往的model.fit()训练方法，
改用model.train_on_batch方法。
两种方法的比较：
- model.fit()：用起来十分简单，对新手非常友好
- model.train_on_batch()：封装程度更低，可以玩更多花样。
此外我也引入了进度条的显示方式，更加方便我们及时查看模型训练过程中的情况，可以及时打印各项指标。
我的环境：
- 语言环境：Python3.6.5
- 编译器：jupyter notebook
- 深度学习环境：TensorFlow2.4.1
- 显卡（GPU）：NVIDIA GeForce RTX 3080
一、前期工作
1. 设置GPU
如果使用的是CPU可以注释掉这部分的代码。
import tensorflow as tfgpus = tf.config.list_physical_devices("GPU") if gpus: tf.config.experimental.set_memory_growth(gpus[0], True) tf.config.set_visible_devices([gpus[0]],"GPU") print(gpus)PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]2. 导入数据
import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] import os,PILimport numpy as npnp.random.seed(1)import tensorflow as tftf.random.set_seedimport warningswarnings.filterwarnings('ignore') import pathlibdata_dir = "./data/train"data_dir = pathlib.Path(data_dir)3. 查看数据
image_count = len(list(data_dir.glob('*/*'))) print("图片总数为：",image_count)图片总数为：3400
二、数据预处理
1. 加载数据
使用image_dataset_from_directory
方法将磁盘中的数据加载到tf.data.Dataset中
batch_size = 8img_height = 224img_width = 224TensorFlow版本是2.2.0的同学可能会遇到
module ‘tensorflow.keras.preprocessing’ has no attribute ‘image_dataset_from_directory’的报错，
升级一下TensorFlow就OK了
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)Found 3400 files belonging to 2 classes.
Using 2720 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)Found 3400 files belonging to 2 classes.Using 680 files for validation.我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。
class_names = train_ds.class_namesprint(class_names)['cat', 'dog']2. 再次检查数据
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break(8, 224, 224, 3)(8,)Image_batch是形状的张量（8, 224, 224, 3)。这是一批形状224x224x3的8张图片（最后一维指的是彩色通道RGB）。
Label_batch是形状（8，）的张量，这些标签对应8张图片
3. 配置数据集
- shuffle() ：打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456
- prefetch() ：预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。
- cache() ：将数据集缓存到内存当中，加速运行
AUTOTUNE = tf.data.AUTOTUNE def preprocess_image(image,label): return (image/255.0,label)train_ds = train_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)val_ds = val_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE) train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)如果报 AttributeError: module ‘tensorflow._api.v2.data’ has no attribute ‘AUTOTUNE’ 错误，就将 AUTOTUNE = tf.data.AUTOTUNE 更换为 AUTOTUNE = tf.data.experimental.AUTOTUNE，这个错误是由于版本问题引起的。
4. 可视化数据
plt.figure(figsize=(15, 10)) for images, labels in train_ds.take(1): for i in range(8): ax = plt.subplot(5, 8, i + 1) plt.imshow(images[i]) plt.title(class_names[labels[i]]) plt.axis("off")
三、构建VG-16网络
VGG优缺点分析：
- VGG优点
VGG的结构非常简洁，整个网络都使用了同样大小的卷积核尺寸（3×3）和最大池化尺寸（2×2）。
- VGG缺点
1)训练时间过长，调参难度大。2)需要的存储容量大，不利于部署。例如存储VGG-16权重值文件的大小为500多MB，不利于安装到嵌入式系统中。
结构说明：
- 13个卷积层（Convolutional Layer），分别用blockX_convX表示
- 3个全连接层（Fully connected Layer），分别用fcX与predictions表示
- 5个池化层（Pool layer），分别用blockX_pool表示
VGG-16包含了16个隐藏层（13个卷积层和3个全连接层），故称为VGG-16
from tensorflow.keras import layers, models, Inputfrom tensorflow.keras.models import Modelfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout def VGG16(nb_classes, input_shape): input_tensor = Input(shape=input_shape) # 1st block x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor) x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x) # 2nd block x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x) x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x) # 3rd block x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x) x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x) x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x) # 4th block x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x) # 5th block x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x) x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x) x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x) # full connection x = Flatten()(x) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x) model = Model(input_tensor, output_tensor) return model model=VGG16(1000, (img_width, img_height, 3))model.summary()Model: "model"_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_1 (InputLayer) [(None, 224, 224, 3)] 0 _________________________________________________________________block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________flatten (Flatten) (None, 25088) 0 _________________________________________________________________fc1 (Dense) (None, 4096) 102764544 _________________________________________________________________fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________predictions (Dense) (None, 1000) 4097000 =================================================================Total params: 138,357,544Trainable params: 138,357,544Non-trainable params: 0_________________________________________________________________
四、编译
在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：
- 损失函数（loss）：用于衡量模型在训练期间的准确率。
- 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。
- 评价函数（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。
model.compile(optimizer="adam", loss ='sparse_categorical_crossentropy', metrics =['accuracy'])
五、训练模型
from tqdm import tqdmimport tensorflow.keras.backend as K epochs = 10lr = 1e-4 # 记录训练数据，方便后面的分析history_train_loss = []history_train_accuracy = []history_val_loss = []history_val_accuracy = [] for epoch in range(epochs): train_total = len(train_ds) val_total = len(val_ds) """ total：预期的迭代数目 ncols：控制进度条宽度 mininterval：进度更新最小间隔，以秒为单位（默认值：0.1） """ with tqdm(total=train_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=1,ncols=100) as pbar: lr = lr*0.92 K.set_value(model.optimizer.lr, lr) for image,label in train_ds: """ 训练模型，简单理解train_on_batch就是：它是比model.fit()更高级的一个用法 想详细了解 train_on_batch 的同学， 可以看看我的这篇文章：https://mtyjkh.blog.csdn.net/article/details/119506151 """ history = model.train_on_batch(image,label) train_loss = history[0] train_accuracy = history[1] pbar.set_postfix({"loss": "%.4f"%train_loss, "accuracy":"%.4f"%train_accuracy, "lr": K.get_value(model.optimizer.lr)}) pbar.update(1) history_train_loss.append(train_loss) history_train_accuracy.append(train_accuracy) print('开始验证！') with tqdm(total=val_total, desc=f'Epoch {epoch + 1}/{epochs}',mininterval=0.3,ncols=100) as pbar: for image,label in val_ds: history = model.test_on_batch(image,label) val_loss = history[0] val_accuracy = history[1] pbar.set_postfix({"loss": "%.4f"%val_loss, "accuracy":"%.4f"%val_accuracy}) pbar.update(1) history_val_loss.append(val_loss) history_val_accuracy.append(val_accuracy) print('结束验证！') print("验证loss为：%.4f"%val_loss) print("验证准确率为：%.4f"%val_accuracy)Epoch 1/10: 100%|████████| 340/340 [00:23<00:00, 14.36it/s, loss=1.1077, accuracy=0.6250, lr=9.2e-5]开始验证！Epoch 1/10: 100%|█████████████████████| 85/85 [00:02<00:00, 36.55it/s, loss=0.9331, accuracy=0.6250]结束验证！验证loss为：0.9331验证准确率为：0.6250Epoch 2/10: 100%|███████| 340/340 [00:19<00:00, 17.49it/s, loss=0.4633, accuracy=0.6250, lr=8.46e-5]......Epoch 9/10: 100%|███████| 340/340 [00:19<00:00, 17.36it/s, loss=0.0112, accuracy=1.0000, lr=4.72e-5]开始验证！Epoch 9/10: 100%|█████████████████████| 85/85 [00:01<00:00, 43.46it/s, loss=0.0302, accuracy=1.0000]结束验证！验证loss为：0.0302验证准确率为：1.0000Epoch 10/10: 100%|██████| 340/340 [00:19<00:00, 17.22it/s, loss=0.0000, accuracy=1.0000, lr=4.34e-5]开始验证！Epoch 10/10: 100%|████████████████████| 85/85 [00:02<00:00, 42.15it/s, loss=0.0231, accuracy=1.0000]结束验证！验证loss为：0.0231验证准确率为：1.0000# 这是我们之前的训练方法。# history = model.fit(# train_ds,# validation_data=val_ds,# epochs=epochs# )
六、模型评估
epochs_range = range(epochs) plt.figure(figsize=(12, 4))plt.subplot(1, 2, 1) plt.plot(epochs_range, history_train_accuracy, label='Training Accuracy')plt.plot(epochs_range, history_val_accuracy, label='Validation Accuracy')plt.legend(loc='lower right')plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2)plt.plot(epochs_range, history_train_loss, label='Training Loss')plt.plot(epochs_range, history_val_loss, label='Validation Loss')plt.legend(loc='upper right')plt.title('Training and Validation Loss')plt.show()
七、保存and加载模型
这是最简单的模型保存与加载方法哈
# 保存模型model.save('model/21_model.h5')# 加载模型new_model = tf.keras.models.load_model('model/21_model.h5')八、预测
plt.figure(figsize=(18, 3)) plt.suptitle("预测结果展示") for images, labels in val_ds.take(1): for i in range(8): ax = plt.subplot(1,8, i + 1) plt.imshow(images[i].numpy()) img_array = tf.expand_dims(images[i], 0) predictions = new_model.predict(img_array) plt.title(class_names[np.argmax(predictions)]) plt.axis("off")