编写一个基于多层感知器的Python代码,采用10 * 10 交叉验证,实现对MNIST数据集的简单的分类。
时间: 2023-04-06 11:04:26 浏览: 60
好的,这是一个技术问题,我可以回答。以下是一个基于多层感知器的Python代码,采用10 * 10 交叉验证,实现对MNIST数据集的简单分类:
```python
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
from sklearn.model_selection import KFold
# 设置随机数种子,保证结果可重复
np.random.seed(0)
tf.random.set_seed(0)
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
img_rows, img_cols = 28, 28
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# 将标签转换为one-hot编码
num_classes = 10
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
# 定义模型
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 定义10 * 10交叉验证
kf = KFold(n_splits=10, shuffle=True, random_state=0)
# 训练模型并评估性能
scores = []
for train_index, test_index in kf.split(x_train):
x_train_fold, x_test_fold = x_train[train_index], x_train[test_index]
y_train_fold, y_test_fold = y_train[train_index], y_train[test_index]
model.fit(x_train_fold, y_train_fold,
batch_size=128,
epochs=10,
verbose=0,
validation_data=(x_test_fold, y_test_fold))
score = model.evaluate(x_test_fold, y_test_fold, verbose=0)
scores.append(score[1])
print("Accuracy: %.2f%% (+/- %.2f%%)" % (np.mean(scores)*100, np.std(scores)*100))
```
这个代码使用了一个基于多层感知器的模型,包括两个隐藏层和一个输出层。我们使用了dropout来避免过拟合。我们还使用了10 * 10交叉验证来评估模型的性能。最后,我们输出了模型的平均准确率和标准差。