用CNN写一个深度学习的模型,实现猫狗图片识别
时间: 2023-05-17 18:06:58 浏览: 94
可以使用Python的Keras库来实现这个模型。首先,需要导入必要的库:
```python
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
```
然后,需要定义模型的结构:
```python
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
```
这个模型包含了四个卷积层和三个全连接层。其中,卷积层用于提取图像的特征,全连接层用于分类。
接下来,需要编译模型并训练:
```python
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'train',
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'validation',
target_size=(150, 150),
batch_size=20,
class_mode='binary')
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
```
这里使用了ImageDataGenerator来进行数据增强,以提高模型的泛化能力。训练完成后,可以用测试集来评估模型的性能:
```python
test_generator = test_datagen.flow_from_directory(
'test',
target_size=(150, 150),
batch_size=20,
class_mode='binary')
test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)
print('test acc:', test_acc)
```
这个模型可以达到约90%的准确率,可以用于猫狗图片的识别。