pycharm用CNN进行图像分类的整体代码，分为模型，训练和测试三部分，图像来自文件夹，使用要有引用路径

PyCharm是一个强大的Python集成开发环境，用于深度学习项目，如使用卷积神经网络（CNN）进行图像分类。以下是一个简化的例子，展示如何使用Keras库在PyCharm中实现这个过程：

# 导入必要的库
import os
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator

# 定义模型
def create_cnn_model():
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(units=128, activation='relu'))
    model.add(Dense(units=num_classes, activation='softmax'))  # num_classes是你有多少类别
    return model

# 定义数据集路径和类别数
image_dir = 'path/to/your/image/folder'
num_classes = len(os.listdir(image_dir))  # 获取文件夹下类别数量

# 加载和预处理数据
data_gen = ImageDataGenerator(rescale=1./255)  # 数据增强
train_data = data_gen.flow_from_directory(image_dir, target_size=(64, 64), batch_size=32, class_mode='categorical')

# 创建模型并编译
model = create_cnn_model()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
history = model.fit(train_data, epochs=10, validation_split=0.2)

# 测试模型
test_generator = data_gen.flow_from_directory('path/to/test/folder', target_size=(64, 64), batch_size=32, class_mode='categorical', shuffle=False)
loss, accuracy = model.evaluate(test_generator)
print("Test Accuracy:", accuracy)