深度学习训练营
学习内容
在深度学习当中,由于准备数据集本身是一件十分复杂的过程,很难保障每一张图片的学习能力都很高,所以对于同一种图片采用数据增强就显得十分重要了
原文链接
- ???? 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P10周:实现数据增强
- ???? 原作者：K同学啊|接辅导、项目定制
环境介绍
- 语言环境：Python3.9.13
- 编译器：jupyter notebook
- 深度学习环境：TensorFlow2
- 数据链接:猫和狗数据
前置工作
设置GPU
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)
加载数据
将对应的数据按照不同种类放入到不同文件夹当中,再将数据整合为animal_data
data_dir = "animal_data"
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 3400 files belonging to 2 classes.
Using 2380 files for training.
val_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 3400 files belonging to 2 classes.
Using 2380 files for training.
创建测试集
因为数据本身没有设置测试集,这里需要进行手动创建
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))
运行结构如下
Number of validation batches: 60
Number of test batches: 15
预测的batches和测试batches分别为60和15
数据类型查看以及数据归一化
class_names = train_ds.class_names
print(class_names)
['cat', 'dog']
进行数据归一化操作
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)
查看数据集
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")
数据增强操作
data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])
#进行随机的水平翻转和垂直翻转
# 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")
使用嵌入model的方法进行数据增强
model = tf.keras.Sequential([
data_augmentation,
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
])
- 这样的操作可以得到
GPU的加速
模型训练
模型开始训练之前都需要进行这个模型的调整
model = tf.keras.Sequential([
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))
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
开始进行正式的训练
epochs=20
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
75/75 [==============================] - 35s 462ms/step - loss: 2.0268e-05 - accuracy: 1.0000 - val_loss: 1.8425e-05 - val_accuracy: 1.0000
Epoch 2/20
75/75 [==============================] - 34s 461ms/step - loss: 1.7937e-05 - accuracy: 1.0000 - val_loss: 1.6272e-05 - val_accuracy: 1.0000
Epoch 3/20
75/75 [==============================] - 35s 461ms/step - loss: 1.5871e-05 - accuracy: 1.0000 - val_loss: 1.4373e-05 - val_accuracy: 1.0000
Epoch 4/20
75/75 [==============================] - 34s 450ms/step - loss: 1.4039e-05 - accuracy: 1.0000 - val_loss: 1.2682e-05 - val_accuracy: 1.0000
Epoch 5/20
75/75 [==============================] - 34s 450ms/step - loss: 1.2429e-05 - accuracy: 1.0000 - val_loss: 1.1195e-05 - val_accuracy: 1.0000
Epoch 6/20
75/75 [==============================] - 35s 462ms/step - loss: 1.1014e-05 - accuracy: 1.0000 - val_loss: 9.8961e-06 - val_accuracy: 1.0000
Epoch 7/20
75/75 [==============================] - 34s 450ms/step - loss: 9.7220e-06 - accuracy: 1.0000 - val_loss: 8.6961e-06 - val_accuracy: 1.0000
Epoch 8/20
75/75 [==============================] - 34s 455ms/step - loss: 8.5416e-06 - accuracy: 1.0000 - val_loss: 7.6252e-06 - val_accuracy: 1.0000
Epoch 9/20
75/75 [==============================] - 34s 459ms/step - loss: 7.5130e-06 - accuracy: 1.0000 - val_loss: 6.7169e-06 - val_accuracy: 1.0000
Epoch 10/20
75/75 [==============================] - 34s 460ms/step - loss: 6.6338e-06 - accuracy: 1.0000 - val_loss: 5.9490e-06 - val_accuracy: 1.0000
Epoch 11/20
75/75 [==============================] - 34s 457ms/step - loss: 5.8835e-06 - accuracy: 1.0000 - val_loss: 5.2946e-06 - val_accuracy: 1.0000
Epoch 12/20
75/75 [==============================] - 34s 456ms/step - loss: 5.2507e-06 - accuracy: 1.0000 - val_loss: 4.7294e-06 - val_accuracy: 1.0000
Epoch 13/20
...
Epoch 19/20
75/75 [==============================] - 34s 449ms/step - loss: 2.5978e-06 - accuracy: 1.0000 - val_loss: 2.3737e-06 - val_accuracy: 1.0000
Epoch 20/20
75/75 [==============================] - 34s 449ms/step - loss: 2.3849e-06 - accuracy: 1.0000 - val_loss: 2.1841e-06 - val_accuracy: 1.0000
这里比较奇怪的是训练的结果准确性很高,loss的值都是很小很小的,和原本博主的相应的内容是不一样的,我觉得很大的可能应该是首先这个数据的内容很大,原本只有几百张图片,但是这里一共有3400张图片,再加上模型训练的增强方式比较简单,导致在结果上面训练看起来很好
loss, acc = model.evaluate(test_ds)
print("Accuracy", acc)
15/15 [==============================] - 1s 83ms/step - loss: 1.9960e-06 - accuracy: 1.0000
Accuracy 1.0
结果可视化
自定义数据增强
这里主要是可以更改随机数种子的大小
import random
# 这是大家可以*发挥的一个地方
def aug_img(image):
seed = (random.randint(5,10), 0)
#设立随机数种植,randint是指在0到9之间进行一个数据的增强
# 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
查看数据增强后的图片
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: 2.4591687 241.47968
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")
