keras实现用resnet18非预训练模型对四种天气分类cpu版本，并绘制图像

首先需要导入相关的库： ``` import tensorflow as tf from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.optimizers import Adam ``` 然后创建一个模型： ``` def create_model(): model = Sequential() # Convolution Block 1 model.add(Conv2D(64, (3,3), activation='relu', padding='same', input_shape=(224,224,3))) model.add(Conv2D(64, (3,3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2,2))) # Convolution Block 2 model.add(Conv2D(128, (3,3), activation='relu', padding='same')) model.add(Conv2D(128, (3,3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2,2))) # Convolution Block 3 model.add(Conv2D(256, (3,3), activation='relu', padding='same')) model.add(Conv2D(256, (3,3), activation='relu', padding='same')) model.add(Conv2D(256, (3,3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2,2))) # Convolution Block 4 model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2,2))) # Convolution Block 5 model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(Conv2D(512, (3,3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2,2))) # Flatten model.add(Flatten()) model.add(Dense(4096, activation='relu')) model.add(Dense(4096, activation='relu')) model.add(Dense(4, activation='softmax')) return model ``` 接着使用ImageDataGenerator来加载数据： ``` train_data_generator = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True ) test_data_generator = ImageDataGenerator(rescale=1./255) train_data = train_data_generator.flow_from_directory( 'train', target_size=(224, 224), batch_size=32, class_mode='categorical' ) test_data = test_data_generator.flow_from_directory( 'test', target_size=(224, 224), batch_size=32, class_mode='categorical' ) ``` 然后创建模型并编译： ``` model = create_model() model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy']) ``` 最后训练模型并绘制图像： ``` history = model.fit( train_data, epochs=10, validation_data=test_data ) # 绘制训练集和验证集的准确率和损失值曲线 import matplotlib.pyplot as plt plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Model Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend(['Train', 'Val'], loc='upper left') plt.show() plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend(['Train', 'Val'], loc='upper left') plt.show() ``` 这样就完成了用resnet18非预训练模型对四种天气分类的任务，并绘制了相应的图像。