m0_62004321的博客

第1关：实现神经网络模型的前向传播import numpy from layers import Convolution, Relu, FullyConnected, MaxPool, SoftmaxWithLossclass TinyNet: def __init__(self, W_conv1, b_conv1, W_conv2, b_conv2, W_fc, b_fc): ########## Begin ########## self.

第1关：QueueStreamimport java.text.SimpleDateFormat import java.util.Date import org.apache.spark.{HashPartitioner, SparkConf} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Seconds, StreamingContext} import scala.collection.m

第1关：Java 操作 MongoDB 数据库（一）package step1;import java.util.function.Consumer;import java.util.logging.Level;import java.util.logging.Logger;import java.util.ArrayList;import java.util.List;import org.bson.Document;import com.mongodb.MongoClient;im

第1关：SparkSQL初识package com.educoder.bigData.sparksql;import org.apache.spark.sql.AnalysisException;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;import org.apache.spark.sql.SparkSession;public class Test1 { public static

第1关：了解并创建一个简单索引mongouse testdb.student.insert([{_id:1,name:"王小明",age:15,score:90},{_id:2,name:"周晓晓",age:18,score:86},{_id:3,name:"王敏",age:20,score:96},{_id:4,name:"李晓亮",age:15,score:74},{_id:5,name:"张青青",age:21,score:88}])db.student.createIndex({s

第1关：实现全连接层的反向传播import numpy as npclass FullyConnected: def __init__(self, W, b): r''' 全连接层的初始化。 Parameter: - W: numpy.array, (D_in, D_out) - b: numpy.array, (D_out) ''' self.W = W s

第1关：实现常见损失函数的前向传播import numpy as npdef softmax(x): x = x - np.max(x, axis=1, keepdims=True) return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)class SoftmaxWithLoss: def __init__(self): self.loss = None def forward