:ice_cream: 本文为 :link:365天深度学习训练营 中的学习记录博客
:icecream: 参考文章:365天深度学习训练营 | 第8周:猫狗识别(训练营内部成员可阅读)
:meat_on_bone: 原作者: K同学啊|接辅导、项目定制
:house_with_garden: 我的环境:
语言环境:Python3.6.5
编译器:jupyter lab
深度学习环境:TensorFlow2.4.1
数据集:参加训练营可获取
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() model.train_on_batch()
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。
:house_with_garden: 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
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 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)
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
2. 导入数据
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 = "./365-7-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
使用 image_dataset_from_directory
方法将磁盘中的数据加载到 tf.data.Dataset
中
batch_size = 8 img_height = 224 img_width = 224
TensorFlow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。
""" 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.
""" 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 """ 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_names print(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 Label_batch
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: 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) # 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)
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)) # 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(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, 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
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 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)
Epoch 1/10: 100%|████████| 340/340 [00:25<00:00, 13.08it/s, loss=0.7219, accuracy=0.3750, lr=9.2e-5] 开始验证! Epoch 1/10: 100%|█████████████████████| 85/85 [00:02<00:00, 32.83it/s, loss=0.6900, accuracy=0.5000] 结束验证! 验证loss为:0.6900 验证准确率为:0.5000 ....... Epoch 9/10: 100%|█████████████████████| 85/85 [00:02<00:00, 36.35it/s, loss=0.0008, accuracy=1.0000] 结束验证! 验证loss为:0.0008 验证准确率为:1.0000 Epoch 10/10: 100%|██████| 340/340 [00:21<00:00, 15.64it/s, loss=0.0066, accuracy=1.0000, lr=4.34e-5] 开始验证! Epoch 10/10: 100%|████████████████████| 85/85 [00:02<00:00, 35.65it/s, loss=0.0002, accuracy=1.0000] 结束验证! 验证loss为:0.0002 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、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")
1/1 [==============================] - 0s 129ms/step 1/1 [==============================] - 0s 19ms/step 1/1 [==============================] - 0s 18ms/step 1/1 [==============================] - 0s 18ms/step 1/1 [==============================] - 0s 17ms/step 1/1 [==============================] - 0s 18ms/step 1/1 [==============================] - 0s 17ms/step 1/1 [==============================] - 0s 17ms/step
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