天气识别

import tensorflow as tf

import matplotlib.pyplot as plt
import os,PIL

# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)

# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)

from tensorflow import keras
from tensorflow.keras import layers,models

import pathlib

data_dir='/content/drive/MyDrive/DL/DL 100例/天气5/第5天/weather_photos/'
data_dir=pathlib.Path(data_dir)

# 查看数据
roses=list(data_dir.glob('sunrise/*.jpg'))
roses

PIL.Image.open(roses[0])

# 加载数据
batch_size = 32
img_height = 180
img_width = 180

train_ds=tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset='training',
    seed=123,
    image_size=(img_height,img_width),
    batch_size=batch_size
)

Found 1125 files belonging to 4 classes.
Using 900 files for training.

val_ds=tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset='validation',
    seed=123,
    image_size=(img_height,img_width),
    batch_size=batch_size
)

Found 1125 files belonging to 4 classes.
Using 225 files for validation.

class_name=train_ds.class_names
class_name

['cloudy', 'rain', 'shine', 'sunrise']

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_name[labels[i]])
        
        plt.axis("off")

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

(32, 180, 180, 3)
(32,)

AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)