深度学习训练营第8周

• 本文为365天深度学习训练营 中的学习记录博客
• 参考文章：365天深度学习训练营-第8周：猫狗识别（训练营内部成员可读）
• 原作者：K同学啊|接辅导、项目定制
  ● 难度：夯实基础⭐⭐
  ● 语言：Python3、TensorFlow2
  ● 时间：9月12-9月16日

要求：

1. 了解model.train_on_batch()并运用
2. 了解tqdm，并使用tqdm实现可视化进度条

拔高（可选）：
3. 本文代码中存在一个严重的BUG，请找出它并配以文字说明
VGG16模型的最后一层输出分类结果应该只有2种
探索（难度有点大）
4. 修改代码，处理BUG

一、前期准备

import tensorflow as tf
import matplotlib.pyplot as plt

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 PIL,pathlib

dir_path = r'/home/mw/input/88559128/dataset/dataset/365-7-data'
dir_path = pathlib.Path(dir_path)

image_cnt = len(list(dir_path.glob('*/*.jpg')))
image_cnt

3400

二、数据预处理

1. 加载数据

batch_size = 8
img_height = 224
img_width = 224

train_ds = tf.keras.preprocessing.image_dataset_from_directory(dir_path,
                                                                batch_size=batch_size,
                                                                image_size=(img_height,img_width),
                                                                seed=12,
                                                                shuffle=True,
                                                                validation_split=0.2,
                                                                subset="training")
val_ds = tf.keras.preprocessing.image_dataset_from_directory(dir_path,
                                                                batch_size=batch_size,
                                                                image_size=(img_height,img_width),
                                                                seed=12,
                                                                shuffle=True,
                                                                validation_split=0.2,
                                                                subset="validation")
class_names = train_ds.class_names
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,)

3. 配置数据集

from tensorflow.keras import models,layers
import numpy as np

AUTOTUNE = tf.data.experimental.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)

4、查看图片

from tensorflow.keras import models,layers
import numpy as np

AUTOTUNE = tf.data.experimental.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)

三、设置网络模型

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(1024, activation='relu',  name='fc1')(x)
    x = Dense(128, 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(len(class_names), (img_width, img_height, 3))# 1000改为2
model.summary()

model.compile(optimizer='adam',loss=tf.keras.losses.sparse_categorical_crossentropy,
             metrics = ['accuracy'])

四、模型训练

新知识点

tqdm参数

class tqdm(object):
“”"
Decorate an iterable object, returning an iterator which acts exactly
like the original iterable, but prints a dynamically updating
progressbar every time a value is requested.
“”"

def init(self, iterable=None, desc=None, total=None, leave=False,
file=sys.stderr, ncols=None, mininterval=0.1,
maxinterval=10.0, miniters=None, ascii=None,
disable=False, unit=‘it’, unit_scale=False,
dynamic_ncols=False, smoothing=0.3, nested=False,
bar_format=None, initial=0, gui=False):
iterable: 可迭代的对象, 在手动更新时不需要进行设置
desc: 字符串, 左边进度条描述文字
total: 总的项目数
leave: bool值, 迭代完成后是否保留进度条
file: 输出指向位置, 默认是终端, 一般不需要设置
ncols: 调整进度条宽度, 默认是根据环境自动调节长度, 如果设置为0, 就没有进度条, 只有输出的信息
unit: 描述处理项目的文字, 默认是’it’, 例如: 100 it/s, 处理照片的话设置为’img’ ,则为 100 img/s
unit_scale: 自动根据国际标准进行项目处理速度单位的换算, 例如 100000 it/s >> 100k it/s
colour: 进度条颜色，例如：‘green’, ‘#00ff00’。支持的颜色：“BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE”

train_on_batch函数原型

y_pred = Model.train_on_batch(
x,
y=None,
sample_weight=None,
class_weight=None,
reset_metrics=True,
return_dict=False,
)
参数详解：

• x：模型输入，单输入就是一个 numpy 数组， 多输入就是 numpy 数组的列表
• y：标签，单输出模型就是一个 numpy 数组， 多输出模型就是 numpy 数组列表
• sample_weight：mini-batch 中每个样本对应的权重，形状为 (batch_size)
• class_weight：类别权重，作用于损失函数，为各个类别的损失添加权重，主要用于类别不平衡的情况， 形状为 (num_classes)
• reset_metrics：默认True，返回的metrics只针对这个mini-batch, 如果False，metrics 会跨批次累积
• return_dict：默认 False, y_pred 为一个列表，如果 True 则 y_pred 是一个字典
• 优点
  ● 更精细自定义训练过程，更精准的收集 loss 和 metrics
  ● 分步训练模型-GAN的实现
  ● 多GPU训练保存模型更加方便
  ● 更多样的数据加载方式

tf.keras.backend

keras后端API
定义python操作时使用的上下文管理器
…backend.set_value函数
从Numpy数组设置变量的值。
参数：
x：要设置为新值的Tensor。
value：将张量设置为Numpy数组（具有相同形状）的值。
tf.keras.backend.get_value（x）返回一个变量的值

from tqdm import tqdm
import tensorflow.keras.backend as K

epochs = 12
lr = 1e-4
#记录训练数据，方便后续分析
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*0.9 #随epoch减小学习率  
        K.set_value(model.optimizer.lr,lr) ##通过K.set_value对原模型的学习率进行更新
        
        for image,label in train_ds:
            history = model.train_on_batch(image,label) #输入数据与对应标签
            train_loss = history[0]   #这里我们是既有loss也由metrics的单输出模型
            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=0.5,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 1/12: 100%|██████████| 340/340 [00:46<00:00, 7.35it/s, loss=0.6920, accuracy=0.8750, lr=9e-5]
Epoch 1/12: 0%| | 0/85 [00:00 开始验证！
Epoch 1/12: 100%|█████████████████████| 85/85 [00:04<00:00, 21.21it/s, loss=0.6910, accuracy=0.6250]
结束验证！
验证loss为：0.6910
验证准确率为：0.6250
…
Epoch 12/12: 100%|██████| 340/340 [00:45<00:00, 7.42it/s, loss=0.0017, accuracy=1.0000, lr=2.82e-5]
开始验证！
Epoch 12/12: 100%|████████████████████| 85/85 [00:03<00:00, 21.90it/s, loss=0.0010, accuracy=1.0000]
结束验证！
验证loss为：0.0010
验证准确率为：1.0000

五、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()

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")