CNN卷积神经网络天气识别代码：height, width = 224, 224 # Data Processing Stage with resizing and rescaling operations data_preprocess = tf.keras.Sequential( name="data_preprocess", layers=[ tf.keras.layers.Resizing(height, width), # Shape Preprocessing tf.keras.layers.Rescaling(1.0/255), # Value Preprocessing ] ) # Perform Data Processing on the train, val, test dataset train_ds = train_data.map(lambda x, y: (data_preprocess(x), y)) # test_ds = test_data.map(lambda x, y: (data_preprocess(x), y))

以下是一个基于TensorFlow的CNN卷积神经网络天气识别代码，包括了数据预处理的部分：

import tensorflow as tf
 tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 设置图像大小和图像预处理操作
height, width = 224, 224
data_preprocess = tf.keras.Sequential(
    name="data_preprocess",
    layers=[
        tf.keras.layers.Resizing(height, width), # 调整图像大小
        tf.keras.layers.Rescaling(1.0/255), # 归一化
    ]
)

# 加载训练和验证数据集
train_data = ImageDataGenerator().flow_from_directory(
    directory='train/',
    target_size=(height, width),
    batch_size=32,
    class_mode='categorical',
    shuffle=True,
    seed=42
)

val_data = ImageDataGenerator().flow_from_directory(
    directory='val/',
    target_size=(height, width),
    batch_size=32,
    class_mode='categorical',
    shuffle=True,
    seed=42
)

# 对训练和验证数据集进行预处理
train_ds = train_data.map(lambda x, y: (data_preprocess(x), y))
val_ds = val_data.map(lambda x, y: (data_preprocess(x), y))

# 创建卷积神经网络模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(height, width, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(3, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
history = model.fit(train_ds, epochs=10, validation_data=val_ds)

# 评估模型
test_data = ImageDataGenerator().flow_from_directory(
    directory='test/',
    target_size=(height, width),
    batch_size=32,
    class_mode='categorical',
    shuffle=False
)

test_ds = test_data.map(lambda x, y: (data_preprocess(x), y))
test_loss, test_acc = model.evaluate(test_ds)
print('Test accuracy:', test_acc)

在这个例子中，我们使用了TensorFlow的Keras API和ImageDataGenerator来创建CNN卷积神经网络模型，用于识别天气图像。数据预处理包括调整图像大小和归一化操作，通过Lambda函数将其应用到训练、验证和测试数据集上。模型采用了卷积、最大池化、全连接、Dropout等操作，并使用Adam优化器进行模型训练。最后，我们对测试数据集进行评估，计算模型的准确率。