自定义数据集使用tensorflow框架实现逻辑回归并保存模型，然后保存模型后再加载模型进行预测

qwe352633

于 2025-01-27 20:09:33 发布

阅读量134

点赞数 3

文章标签： tensorflow 逻辑回归人工智能

本文链接：https://blog.youkuaiyun.com/qwe352/article/details/145382177

版权

代码

import tensorflow as tf
import numpy as np
from tensorflow.keras import Model
from tensorflow.keras import models
 
data = np.array([[-0.5, 7.7],
                 [1.8, 98.5],
                 [0.9, 57.8],
                 [0.4, 39.2],
                 [-1.4, -15.7],
                 [-1.4, -37.3],
                 [-1.8, -49.1],
                 [1.5, 75.6],
                 [0.4, 34.0],
                 [0.8, 62.3]])
 
x_data = data[:, 0]
y_data = data[:, 1]
 
x_train = tf.constant(x_data, dtype=tf.float32)
y_train = tf.constant(y_data, dtype=tf.float32)
 
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.shuffle(buffer_size=10)
dataset = dataset.batch(2)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
 
def linear():
    input = tf.keras.layers.Input(shape=(1,), dtype=tf.float32)
    y = tf.keras.layers.Dense(1)(inpu