一、前期工作
1. 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)
2. 导入数据
import numpy as np 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,pathlib #隐藏警告 import warnings warnings.filterwarnings('ignore') data_dir = "./365-9-data" data_dir = pathlib.Path(data_dir) 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
batch_size = 64 img_height = 224 img_width = 224
""" 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 """ 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.
class_names = train_ds.class_names print(class_names)
['cat', 'dog']
for image_batch, labels_batch in train_ds: print(image_batch.shape) print(labels_batch.shape) break
(64, 224, 224, 3)
(64,)
2. 配置数据集
AUTOTUNE = tf.data.AUTOTUNE def preprocess_image(image,label): return (image/255.0,label) # 归一化处理 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)
3. 可视化数据
plt.figure(figsize=(15, 10)) # 图形的宽为15高为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（3x3）和最大池化尺寸（2x2）。
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, Input from tensorflow.keras.models import Model from 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: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 _________________________________________________________________
四、编译
model.compile(optimizer="adam", loss ='sparse_categorical_crossentropy', metrics =['accuracy'])
五、训练模型
tqdm相关用法：【Python】 tqdm 库 - 知乎 (zhihu.com)
from tqdm import tqdm import tensorflow.keras.backend as K epochs = 10 lr = 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) train_loss = [] train_accuracy = [] 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://www.yuque.com/mingtian-fkmxf/hv4lcq/ztt4gy """ # 这里生成的是每一个batch的acc与loss history = model.train_on_batch(image,label) train_loss.append(history[0]) train_accuracy.append(history[1]) pbar.set_postfix({"train_loss": "%.4f"%history[0], "train_acc":"%.4f"%history[1], "lr": K.get_value(model.optimizer.lr)}) pbar.update(1) history_train_loss.append(np.mean(train_loss)) history_train_accuracy.append(np.mean(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: val_loss = [] val_accuracy = [] for image,label in val_ds: # 这里生成的是每一个batch的acc与loss history = model.test_on_batch(image,label) val_loss.append(history[0]) val_accuracy.append(history[1]) pbar.set_postfix({"val_loss": "%.4f"%history[0], "val_acc":"%.4f"%history[1]}) pbar.update(1) history_val_loss.append(np.mean(val_loss)) history_val_accuracy.append(np.mean(val_accuracy)) print('结束验证！') print("验证loss为：%.4f"%np.mean(val_loss)) print("验证准确率为：%.4f"%np.mean(val_accuracy))
结束验证！
验证loss为：0.0762
验证准确率为：0.9793
BUG(写法如下)：
from tqdm import tqdm import tensorflow.keras.backend as K epochs = 10 lr = 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://www.yuque.com/mingtian-fkmxf/hv4lcq/ztt4gy """ 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)
此时每个epoch返回的accuracy和loss是模型对每个训练或测试集最后一个batch的训练或测试的结果。
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、预测
import numpy as np # 采用加载的模型（new_model）来看预测结果 plt.figure(figsize=(18, 3)) # 图形的宽为18高为5 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) # 显示图片 plt.imshow(images[i].numpy()) # 需要给图片增加一个维度 img_array = tf.expand_dims(images[i], 0) # 使用模型预测图片中的人物 predictions = model.predict(img_array) plt.title(class_names[np.argmax(predictions)]) plt.axis("off")
