pythoncnn模型鲜花识别
时间: 2023-08-16 21:06:26 浏览: 26
鲜花识别是一个常见的图像分类问题,可以使用CNN模型进行解决。以下是一个使用Python和Keras库实现鲜花识别的CNN模型:
1. 导入所需的库:
```
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
```
2. 定义模型:
```
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu', input_shape=(128,128,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(units=512, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(units=5, activation='softmax'))
```
3. 编译模型:
```
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
```
4. 数据预处理:
```
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory('flower_photos/train', target_size=(128,128), batch_size=32, class_mode='categorical')
test_set = test_datagen.flow_from_directory('flower_photos/test', target_size=(128,128), batch_size=32, class_mode='categorical')
```
5. 训练模型:
```
model.fit_generator(training_set, steps_per_epoch=2000, epochs=10, validation_data=test_set, validation_steps=800)
```
在训练完成后,可以使用模型进行鲜花识别。
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