Keras实现线性判别分析（LDA）

最新推荐文章于 2022-10-03 17:02:50 发布

what do you mean

最新推荐文章于 2022-10-03 17:02:50 发布

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文章标签： python 人工智能 keras 分类机器学习

本文链接：https://blog.youkuaiyun.com/weixin_44466257/article/details/123674884

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导入所需模块并限制GPU效率

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import adam_v2
import matplotlib.pyplot as plt
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)  # 设置显存按需申请
tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.
                                                        VirtualDeviceConfiguration(memory_limit=1024)])

生成两类数据

# 生成训练数据集，每类100个数据
def get_train_data(data_size=100):
    data_label = np.zeros((2 * data_size, 1))
    # class 1
    x1 = np.reshape(np.random.normal(1, 0.6, data_size), (data_size, 1))
    y1 = np.reshape(np.random.normal(1, 0.8, data_size), (data_size, 1))
    data_train = np.concatenate((x1, y1), axis=1)
    data_label[0:data_size,