T6:使用tensorflow实现好莱坞明星识别

• 🍨 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
  我的环境
  语言环境：python 3.7.12
  编译器：VS code
  深度学习环境：tensorflow 2.7.0
  数据：本地数据集46-data

一、任务描述

这期的任务是使用对数损失函数完成本次多分类任务，并学习不同损失函数的使用场景。
可选任务：使用vgg16模型。

二、实现过程

1.数据导入与查看

from tensorflow       import keras
from tensorflow.keras import layers,models
import os, PIL, pathlib
import matplotlib.pyplot as plt
import tensorflow        as tf
import numpy             as np

data_dir = "./48-data/"
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print("图片总数为：",image_count)
roses = list(data_dir.glob('Jennifer Lawrence/*.jpg'))
PIL.Image.open(str(roses[0]))

2.数据预处理

2.1加载数据

batch_size = 32
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.youkuaiyun.com/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.1,
    subset="training",
    label_mode = "categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

"""
关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.youkuaiyun.com/article/details/117018789
"""
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.1,
    subset="validation",
    label_mode = "categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)

[‘Angelina Jolie’, ‘Brad Pitt’, ‘Denzel Washington’, ‘Hugh Jackman’, ‘Jennifer Lawrence’, ‘Johnny Depp’, ‘Kate Winslet’, ‘Leonardo DiCaprio’, ‘Megan Fox’, ‘Natalie Portman’, ‘Nicole Kidman’, ‘Robert Downey Jr’, ‘Sandra Bullock’, ‘Scarlett Johansson’, ‘Tom Cruise’, ‘Tom Hanks’, ‘Will Smith’]

2.2可视化数据

plt.figure(figsize=(20, 10))

for images, labels in train_ds.take(1):
    for i in range(20):
        ax = plt.subplot(5, 10, i + 1)

        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[np.argmax(labels[i])])
        
        plt.axis("off")

2.3再次检查数据

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break