写一个VGG16处理fer2013数据集的代码
时间: 2023-12-22 17:05:23 浏览: 36
好的,以下是VGG16处理fer2013数据集的代码示例:
```
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
# 加载fer2013数据集
data = np.load("fer2013.npz")
X_train = data['X_train']
y_train = data['y_train']
X_test = data['X_test']
y_test = data['y_test']
# 数据预处理
X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)
X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
num_classes = 7
# 搭建VGG16模型
model = Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(48, 48, 1)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# 模型编译
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, batch_size=128, epochs=50, verbose=1, validation_data=(X_test, y_test))
# 评估模型
scores = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
```
这个代码使用深度学习库Keras来搭建VGG16模型,然后利用fer2013数据集进行训练和测试。最后输出测试集上的loss和准确率。