[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fe9H7tpTWJzDs1TXZvlKWSg-i-GiTRDmlZ2dBifJaHQY":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":8,"question":15,"related":16,"source":22,"type":23},[],"2024-06-09 19:56:29",149895294,[],{"count":9,"courseId":10,"courseImg":11,"courseName":12,"workId":13,"workName":14},25,"351d272eeff170c899ccc7f82b487bb2","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5ba4e6a545b58dabb922f06d9a1383c0.jpg","机器学习基础","work_35326808","期末复习主观题","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F24281a5e624e54efd05b4f1b6a90864b.png\"> 补充以下代码,实现如上图所示的单隐层神经网络模型的构建,实现一个二分类问题.(网络中隐含层神经元使用sigmoid激活函数). W1 = tf.Variable( ,mean=0.0, stddev=1.0, dtype=tf.float32)) B1 = tf.Variable( ,dtype = tf.float32) W2 = tf.Variable( ,mean=0.0, stddev=1.0, dtype=tf.float32)) B2 = W = [W1, W2] B = [B1, B2] def model(x, w, b): x = x = x = pred = return pred",[17,24,29,34,39,44,49,54,59,62],{"answer":18,"createTime":5,"id":19,"options":20,"question":21,"source":22,"type":23},[],149895286,[],"什么是机器学习","v1",4,{"answer":25,"createTime":5,"id":26,"options":27,"question":28,"source":22,"type":23},[],149895287,[],"机器学习与人工智能、深度学习有什么关系",{"answer":30,"createTime":5,"id":31,"options":32,"question":33,"source":22,"type":23},[],149895288,[],"人工智能的三要素是什么,请详细说明",{"answer":35,"createTime":5,"id":36,"options":37,"question":38,"source":22,"type":23},[],149895289,[],"你有哪一些深度学习调优的经验,主要哪几个参数,有什么效果",{"answer":40,"createTime":5,"id":41,"options":42,"question":43,"source":22,"type":23},[],149895290,[],"如果想在tensorflow2.0环境中执行或者开发tensorflow1.x代码,该怎么处理",{"answer":45,"createTime":5,"id":46,"options":47,"question":48,"source":22,"type":23},[],149895291,[],"简述监督学习的训练优化过程?如何观察模型训练是否收敛",{"answer":50,"createTime":5,"id":51,"options":52,"question":53,"source":22,"type":23},[],149895292,[],"如何使用二分类模型(例如:逻辑回归)解决多分类问题?联系实际问题,简单谈谈你的理解",{"answer":55,"createTime":5,"id":56,"options":57,"question":58,"source":22,"type":23},[],149895293,[],"为什么分类问题的标签数据要进行独热编码?举例说明",{"answer":60,"createTime":5,"id":6,"options":61,"question":15,"source":22,"type":23},[],[],{"answer":63,"createTime":5,"id":64,"options":65,"question":66,"source":22,"type":23},[],149895295,[],"\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fb41855d30c84f09a200ceb82309cd0a2.png\"> 补充以下代码,实现如上图所示两层全连接神经网络模型的构建,实现一个二分类问题.(网络中隐含层神经元使用relu激活函数). W1 = tf.Variable( ,mean=0.0, stddev=1.0, dtype=tf.float32)) B1 = tf.Variable( ,dtype = tf.float32) W2 = tf.Variable( ,mean=0.0, stddev=1.0, dtype=tf.float32)) B2 = W3 = tf.Variable( ,mean=0.0, stddev=1.0, dtype=tf.float32)) B3 = W = [W1, W2,W3] B = [B1, B2,B3] def model(x, w, b): x = x = x = x = x = pred = return pred"]