卷积神经网络（CNN）天气识别

前期工作

1. 设置GPU（如果使用的是CPU可以忽略这步）

我的环境：

• 语言环境：Python3.6.5
• 编译器：jupyter notebook
• 深度学习环境：TensorFlow2.4.1

import tensorflow as tfgpus = tf.config.list_physical_devices("GPU")if gpus:gpu0 = gpus[0]                                        #如果有多个GPU，仅使用第0个GPUtf.config.experimental.set_memory_growth(gpu0, True)  #设置GPU显存用量按需使用tf.config.set_visible_devices([gpu0],"GPU")

2. 导入数据

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,modelsimport pathlib

data_dir = "weather_photos/"
data_dir = pathlib.Path(data_dir)

3. 查看数据

数据集一共分为cloudy、rain、shine、sunrise四类，分别存放于weather_photos文件夹中以各自名字命名的子文件夹中。

image_count = len(list(data_dir.glob('*/*.jpg')))print("图片总数为：",image_count)

roses = list(data_dir.glob('sunrise/*.jpg'))
PIL.Image.open(str(roses[0]))

二、数据预处理

1. 加载数据

使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中

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.

我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。

class_names = train_ds.class_names
print(class_names)

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

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

3. 再次检查数据

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

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

• Image_batch是形状的张量（32,180,180,3）。这是一批形状180x180x3的32张图片（最后一维指的是彩色通道RGB）。
• Label_batch是形状（32，）的张量，这些标签对应32张图片

4. 配置数据集

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

三、构建CNN网络

卷积神经网络（CNN）的输入是张量 (Tensor) 形式的 (image_height, image_width, color_channels)，包含了图像高度、宽度及颜色信息。不需要输入batch size。color_channels 为 (R,G,B) 分别对应 RGB 的三个颜色通道（color channel）。在此示例中，我们的 CNN 输入，fashion_mnist 数据集中的图片，形状是 (28, 28, 1)即灰度图像。我们需要在声明第一层时将形状赋值给参数input_shape。

num_classes = 4model = models.Sequential([layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),layers.Conv2D(16, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)), # 卷积层1，卷积核3*3  layers.AveragePooling2D((2, 2)),               # 池化层1，2*2采样layers.Conv2D(32, (3, 3), activation='relu'),  # 卷积层2，卷积核3*3layers.AveragePooling2D((2, 2)),               # 池化层2，2*2采样layers.Conv2D(64, (3, 3), activation='relu'),  # 卷积层3，卷积核3*3layers.Dropout(0.3),  layers.Flatten(),                       # Flatten层，连接卷积层与全连接层layers.Dense(128, activation='relu'),   # 全连接层，特征进一步提取layers.Dense(num_classes)               # 输出层，输出预期结果
])model.summary()  # 打印网络结构

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
rescaling (Rescaling)        (None, 180, 180, 3)       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 178, 178, 16)      448       
_________________________________________________________________
average_pooling2d (AveragePo (None, 89, 89, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 87, 87, 32)        4640      
_________________________________________________________________
average_pooling2d_1 (Average (None, 43, 43, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 41, 41, 64)        18496     
_________________________________________________________________
dropout (Dropout)            (None, 41, 41, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 107584)            0         
_________________________________________________________________
dense (Dense)                (None, 128)               13770880  
_________________________________________________________________
dense_1 (Dense)              (None, 5)                 645       
=================================================================
Total params: 13,795,109
Trainable params: 13,795,109
Non-trainable params: 0
_________________________________________________________________

四、编译

• 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。
  • 指标（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。

# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=opt,loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])

五、训练模型

epochs = 10
history = model.fit(train_ds,validation_data=val_ds,epochs=epochs
)

Epoch 1/10
29/29 [==============================] - 6s 58ms/step - loss: 1.5865 - accuracy: 0.4463 - val_loss: 0.5837 - val_accuracy: 0.7689
Epoch 2/10
29/29 [==============================] - 0s 12ms/step - loss: 0.5289 - accuracy: 0.8295 - val_loss: 0.5405 - val_accuracy: 0.8133
Epoch 3/10
29/29 [==============================] - 0s 12ms/step - loss: 0.2930 - accuracy: 0.8967 - val_loss: 0.5364 - val_accuracy: 0.8000
Epoch 4/10
29/29 [==============================] - 0s 12ms/step - loss: 0.2742 - accuracy: 0.9074 - val_loss: 0.4034 - val_accuracy: 0.8267
Epoch 5/10
29/29 [==============================] - 0s 11ms/step - loss: 0.1952 - accuracy: 0.9383 - val_loss: 0.3874 - val_accuracy: 0.8844
Epoch 6/10
29/29 [==============================] - 0s 11ms/step - loss: 0.1592 - accuracy: 0.9468 - val_loss: 0.3680 - val_accuracy: 0.8756
Epoch 7/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0836 - accuracy: 0.9755 - val_loss: 0.3429 - val_accuracy: 0.8756
Epoch 8/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0943 - accuracy: 0.9692 - val_loss: 0.3836 - val_accuracy: 0.9067
Epoch 9/10
29/29 [==============================] - 0s 12ms/step - loss: 0.0344 - accuracy: 0.9909 - val_loss: 0.3578 - val_accuracy: 0.9067
Epoch 10/10
29/29 [==============================] - 0s 11ms/step - loss: 0.0950 - accuracy: 0.9708 - val_loss: 0.4710 - val_accuracy: 0.8356

六、模型评估

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']loss = history.history['loss']
val_loss = history.history['val_loss']epochs_range = range(epochs)plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()