T10：数据增强

T10周：数据增强

- - **一、前期工作**
  - - 1.设置GPU,导入库
    - 2.加载数据
  - **二、数据增强**
  - **三、增强方式**
  - - 方法一：将其嵌入model中
    - 方法二：在Dataset数据集中进行数据增强
  - **四、训练模型**
  - **五、自定义增强函数**
  - **六、总结**

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

🍺 要求：

学会在代码中使用数据增强手段来提高acc
请探索更多的数据增强手段并记录

在本教程中，你将学会如何进行数据增强，并通过数据增强用少量数据达到非常非常棒的识别准确率。我将展示两种数据增强方式，以及如何自定义数据增强方式并将其放到我们代码当中，两种数据增强方式如下：

将数据增强模块嵌入model中
在Dataset数据集中进行数据增强

⌛ 我的环境：

语言环境：Python3.6.5
编译器：Google Colab
深度学习环境：TensorFlow2.17.0

一、前期工作

1.设置GPU,导入库

import matplotlib.pyplot as plt
import numpy as np
#隐藏警告
import warnings
warnings.filterwarnings('ignore')

from tensorflow.keras import layers
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.加载数据

from google.colab import drive
drive.mount("/content/drive/")
%cd "/content/drive/Othercomputers/My laptop/jupyter notebook/data/"

Mounted at /content/drive/
/content/drive/Othercomputers/My laptop/jupyter notebook/data

data_dir   = "./10/"
img_height = 224
img_width  = 224
batch_size = 32

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.3,
    subset="training",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)

Found 600 files belonging to 2 classes.
Using 420 files for training.

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.3,
    subset="validation",
    seed=12,
    image_size=(img_height, img_width),
    batch_size=batch_size)

Found 600 files belonging to 2 classes.
Using 180 files for validation.

由于原始数据集不包含测试集，因此需要创建一个。使用 tf.data.experimental.cardinality 确定验证集中有多少批次的数据，然后将其中的 20% 移至测试集。

val_batches = tf.data.experimental.cardinality(val_ds)
test_ds     = val_ds.take(val_batches // 5)
val_ds      = val_ds.skip(val_batches // 5)

print('Number of validation batches: %d' % tf.data.experimental.cardinality(val_ds))
print('Number of test batches: %d' % tf.data.experimental.cardinality(test_ds))

Number of validation batches: 5
Number of test batches: 1

class_names = train_ds.class_names
print(class_names)

['cat', 'dog']

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)
test_ds  = test_ds.map(preprocess_image, num_parallel_calls=AUTOTUNE)

train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds   = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

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(2, 4, i + 1)
        plt.imshow(images[i])
        plt.title(class_names[labels[i]])

        plt.axis("off")

在这里插入图片描述

二、数据增强

我们可以使用 tf.keras.layers.experimental.preprocessing.RandomFlip 与 tf.keras.layers.experimental.preprocessing.RandomRotation 进行数据增强

tf.keras.layers.experimental.preprocessing.RandomFlip：水平和垂直随机翻转每个图像。
tf.keras.layers.experimental.preprocessing.RandomRotation：随机旋转每个图像

data_augmentation = tf.keras.Sequential([
  tf.keras.layers.RandomFlip("horizontal_and_vertical"), #进行随机水平和垂直翻转
  tf.keras.layers.RandomRotation(0.2), #按照0.2的弧度值进行随机旋转
  tf.keras.layers.RandomBrightness(factor=0.5, value_range=(0.0, 1.0), seed=123), #按照0.1比例调整亮度
  tf.keras.layers.RandomContrast(factor=0.1)
])

# 第一个层表示进行随机的水平和垂直翻转，而第二个层表示按照 0.2 的弧度值进行随机旋转。
#其余增强方式：https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation
# Add the image to a batch.
image = tf.expand_dims(images[i], 0)

plt.figure(figsize=(8, 8))
for i in range(9):
    augmented_image = data_augmentation(image)
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(augmented_image[0])
    plt.axis("off")

在这里插入图片描述

三、增强方式

方法一：将其嵌入model中

model_base = tf.keras.Sequential([
  data_augmentation,
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
])

这样做的好处是：

数据增强这块的工作可以得到GPU的加速（如果你使用了GPU训练的话）

注意：只有在模型训练时（Model.fit）才会进行增强，在模型评估(Model.evaluate)以及预测(Model.predict)时并不会进行增强操作。

方法二：在Dataset数据集中进行数据增强

batch_size = 32
AUTOTUNE = tf.data.AUTOTUNE
#定义处理函数，只有training数据集会处理
def prepare(ds):
    ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)
    return ds

train_ds = prepare(train_ds)

四、训练模型

model = tf.keras.Sequential([
  model_base,
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(len(class_names))
])

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=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=['accuracy'])

epochs=20
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

Epoch 1/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 116ms/step - accuracy: 0.5252 - loss: 0.6915 - val_accuracy: 0.4257 - val_loss: 0.7044
Epoch 2/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 30ms/step - accuracy: 0.5461 - loss: 0.6897 - val_accuracy: 0.5068 - val_loss: 0.6871
Epoch 3/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 35ms/step - accuracy: 0.6517 - loss: 0.6707 - val_accuracy: 0.7500 - val_loss: 0.6056
Epoch 4/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 45ms/step - accuracy: 0.6466 - loss: 0.6242 - val_accuracy: 0.7568 - val_loss: 0.5237
Epoch 5/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 41ms/step - accuracy: 0.6958 - loss: 0.5998 - val_accuracy: 0.7365 - val_loss: 0.6128
Epoch 6/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 37ms/step - accuracy: 0.7187 - loss: 0.6327 - val_accuracy: 0.7230 - val_loss: 0.5495
Epoch 7/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 37ms/step - accuracy: 0.7182 - loss: 0.5746 - val_accuracy: 0.7770 - val_loss: 0.5527
Epoch 8/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 34ms/step - accuracy: 0.7288 - loss: 0.5921 - val_accuracy: 0.8176 - val_loss: 0.4373
Epoch 9/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 31ms/step - accuracy: 0.7544 - loss: 0.4827 - val_accuracy: 0.8243 - val_loss: 0.3807
Epoch 10/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 29ms/step - accuracy: 0.7535 - loss: 0.5227 - val_accuracy: 0.8446 - val_loss: 0.4185
Epoch 11/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.7958 - loss: 0.4514 - val_accuracy: 0.8649 - val_loss: 0.3466
Epoch 12/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8288 - loss: 0.3707 - val_accuracy: 0.8581 - val_loss: 0.3243
Epoch 13/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8466 - loss: 0.3749 - val_accuracy: 0.7973 - val_loss: 0.4575
Epoch 14/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 29ms/step - accuracy: 0.8277 - loss: 0.4484 - val_accuracy: 0.8784 - val_loss: 0.3096
Epoch 15/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8629 - loss: 0.2800 - val_accuracy: 0.8716 - val_loss: 0.2454
Epoch 16/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8496 - loss: 0.3098 - val_accuracy: 0.9054 - val_loss: 0.1984
Epoch 17/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 29ms/step - accuracy: 0.8630 - loss: 0.2889 - val_accuracy: 0.9257 - val_loss: 0.2030
Epoch 18/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8820 - loss: 0.2658 - val_accuracy: 0.9257 - val_loss: 0.2008
Epoch 19/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 29ms/step - accuracy: 0.8940 - loss: 0.2895 - val_accuracy: 0.9324 - val_loss: 0.1662
Epoch 20/20
[1m14/14[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 31ms/step - accuracy: 0.8923 - loss: 0.2681 - val_accuracy: 0.8919 - val_loss: 0.2506

loss, acc = model.evaluate(test_ds)
print("Accuracy", acc)

[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 409ms/step - accuracy: 0.8438 - loss: 0.2309
Accuracy 0.84375

五、自定义增强函数

import random
# 这是大家可以自由发挥的一个地方
def aug_img(image):
    seed = (random.randint(0,9), 0)
    # 随机改变图像对比度
    stateless_random_brightness = tf.image.stateless_random_contrast(image, lower=0.1, upper=1.0, seed=seed)
    return stateless_random_brightness

image = tf.expand_dims(images[3]*255, 0)
print("Min and max pixel values:", image.numpy().min(), image.numpy().max())

Min and max pixel values: 14.000048 253.28577

plt.figure(figsize=(8, 8))
for i in range(9):
    augmented_image = aug_img(image)
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(augmented_image[0].numpy().astype("uint8"))

    plt.axis("off")