请告诉我python实现图像情感多分类的代码及进行测试的代码
时间: 2023-02-24 17:10:45 浏览: 83
在图像情感多分类任务中,通常使用卷积神经网络 (Convolutional Neural Network, CNN) 模型进行训练。您可以使用以下代码来实现基于 Python 的图像情感多分类模型:
```python
import tensorflow as tf
from tensorflow.keras import models, layers, optimizers
# 建立卷积神经网络模型
model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(128, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(5, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['accuracy'])
```
在模型编译之后,您需要将数据进行预处理并加载到模型中进行训练:
```python
# 加载并预处理数据
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'train_data',
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
'validation_data',
target_size=(150, 150),
batch_size=20,
class_mode='categorical')
# 训练模型
history = model.fit_generator(
train_generator,
steps_per_epoch=100,