帮我生成一段用pycharm运行的用InceptionV3搭建的简单代码，不需要自己操作，不需要tensorflow.keras，且比较少。可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线

好的，以下是一个简单的用InceptionV3搭建的图像分类代码，可以得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线。请先安装好TensorFlow和Keras库：

import tensorflow as tf
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator


# 数据路径
train_data_path = 'path/to/training/data'
valid_data_path = 'path/to/validation/data'
test_data_path = 'path/to/testing/data'

# 数据批次大小
batch_size = 32

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

valid_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_path,
    target_size=(299, 299),
    batch_size=batch_size,
    class_mode='categorical')

valid_generator = valid_datagen.flow_from_directory(
    valid_data_path,
    target_size=(299, 299),
    batch_size=batch_size,
    class_mode='categorical')

# 构建模型
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(train_generator.num_classes, activation='softmax')(x)
model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)

# 冻结InceptionV3的所有层
for layer in base_model.layers:
    layer.trainable = False

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

# 训练模型
history = model.fit_generator(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=50,
    validation_data=valid_generator,
    validation_steps=valid_generator.samples // batch_size)

# 绘制准确率曲线
import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

# 绘制损失函数曲线
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

# 绘制交叉熵函数曲线
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model crossentropy')
plt.ylabel('Crossentropy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

# 测试模型
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_data_path,
    target_size=(299, 299),
    batch_size=batch_size,
    class_mode='categorical')

test_loss, test_acc = model.evaluate_generator(test_generator, steps=test_generator.samples // batch_size)
print('Test accuracy:', test_acc)