keras实现用VGG模型分类四种天气图像,并绘制图像

好的，我可以为您提供一些代码示例来实现您的需求。

首先，您需要确保已安装Keras和其他必要的库。然后，您可以按照以下步骤实现：

1. 导入必要的库和模块

import numpy as np
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import plot_model
from keras.applications import VGG16

2. 设置训练和验证数据的路径及其他参数

train_data_dir = 'path/to/training/data'
validation_data_dir = 'path/to/validation/data'
img_width, img_height = 224, 224
batch_size = 16
epochs = 50

3. 使用ImageDataGenerator生成器准备数据

train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_data_dir,
                                                    target_size=(img_width, img_height),
                                                    batch_size=batch_size,
                                                    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
                                                              target_size=(img_width, img_height),
                                                              batch_size=batch_size,
                                                              class_mode='categorical')

4. 加载预训练的VGG16模型

vgg_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))

5. 创建新的模型并添加全连接层

model = Sequential()
model.add(vgg_model)

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))

model.summary()

6. 编译模型并开始训练

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

history = model.fit_generator(train_generator,
                              steps_per_epoch=train_samples // batch_size,
                              epochs=epochs,
                              validation_data=validation_generator,
                              validation_steps=validation_samples // batch_size)

7. 绘制训练和验证的准确率和损失图像

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=(16, 8))
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()

这样，您就可以实现用VGG模型分类四种天气图像并绘制图像了。