안녕하세요. 항상 친절한 답변 감사합니다. 이번 강의를 보다가 궁금증이 몇 개 생겨 질문드립니다.   1. Use Case가 MVC 패턴의(?) service와 비슷하다는 느낌을 받았는데 비슷한 개념인지 궁금합니다.   2. 기능에 따라 (강의에서는 5개(업데이트, 추가, 삭제 등)) class 별로 각자 구현하였는데 이 방식이 call 함수를 쓰기 위함인지 궁금합니다. MVC에서는 Service 계층에서 하나의 클래스 안에 필요한 메서드(Use Case)를 생성했었는데 기능 하나마다 클래스를 만드니 메모리면에서(큰 프로젝트에서는) 낭비가 되지 않나 싶습니다.   3. 모델이 여러개인 경우 use_case 안에 여러개의 모델 디렉토리를 만들어 그 모델 디렉토리 안에서 dart파일을 보통 관리하나요?   4. note_repository에서 추상클래스를 만들고, 그를 구현하는 NoteRepositoryImpl를 만들고, 구현한 그를 사용하는 db_helper를 또 만들었습니다. Impl과 helper를 합칠만하다고 생각하는데 따로 관리하는 이유가 궁금합니다.

- 학습 관련 질문을 남겨주세요. 상세히 작성하면 더 좋아요! - 먼저 유사한 질문이 있었는지 검색해보세요. - 서로 예의를 지키며 존중하는 문화를 만들어가요. - 잠깐! 인프런 서비스 운영 관련 문의는 1:1 문의하기를 이용해주세요. 맥북이용중입니다.   command prompt가 안나오는데 이 부분은 어떻게 해결 할 수 있을까요?

Epoch 1/100 --------------------------------------------------------------------------- UnimplementedError Traceback (most recent call last) <ipython-input-31-4fdf12fa2786> in <module> 2 validation_data=(validation_set), 3 epochs=epochs, ----> 4 callbacks = [checkpoint] ) 1 frames /usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs) 65 except Exception as e: # pylint: disable=broad-except 66 filtered_tb = _process_traceback_frames(e.__traceback__) ---> 67 raise e.with_traceback(filtered_tb) from None 68 finally: 69 del filtered_tb /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 53 ctx.ensure_initialized() 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None:

스케일은 거의 사용하지 않나요? 여백 비율보다는 여백을 유지하는게 더 중요하지 않을까 싶어서요.. 그리고 이건 다른 질문인데, XD인 경우 도형을 만들고 이미지를 드래그하여 도형에 넣으면 도형 사이즈대로 이미지가 들어가지는데 피그마는 그런 기능이 없을까여?

강의를 듣고 직접 RNN을 구현하는 것을 시도했습니다.   하나의 물체를 다른 위치에서 촬영하고 각 사진마다 물체 좌표값(x,y)을 추출했습니다. 얻은 좌표값을 이용해서 실제 3D 좌표(x,y,z)로 예측하는 것을 만들려고 합니다.   제가 생각한 모델구성은 아래와 같습니다. 하지만, 모델을 구현해보니 좌표값이 1개만 출력되고 출력된 값이 무엇을 예측하고 나왔는지 모르겠습니다.   어디를 수정해야 x,y,z값이  출력되나요?   아래는 사용된 소스 및 데이터 일부입니다. import numpy as np import pandas as pd import torch from sklearn.model_selection import train_test_split import torch.nn as nn import torch.optim as optim import matplotlib.pyplot as plt #데이터 불러오기 path = r"redball_Data.csv" df = pd.read_csv(path) df_suffled = df.sample(frac=1).reset_index() #텐서 데이터 만들기 x = df_suffled[['x1','y1','x2','y2','x3','y3','x4','y4','x5','y5','x6','y6','x7','y7','x8','y8']].values y = df_suffled[['x','y','z']].values print(x.shape) x = x.reshape(len(x),8,2) #2d to 3d print(x.shape) print(y.shape) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") xt = torch.FloatTensor(x).to(device) yt = torch.FloatTensor(y).to(device) x_train, x_test, y_train, y_test = train_test_split(xt,yt,test_size=0.2) print(f"x_train {x_train.shape} | x_test {x_test.shape} | y_train {y_train.shape} | y_test {y_test.shape}") train = torch.utils.data.TensorDataset(x_train, y_train) test = torch.utils.data.TensorDataset(x_test, y_test) batch_size = 1000 train_loader = torch.utils.data.DataLoader(dataset=train, batch_size=batch_size, shuffle=False) test_loader = torch.utils.data.DataLoader(dataset=test, batch_size=batch_size, shuffle=False) #Hyperparameter setting # RNN print(f"input_size : {xt.size(2)}") input_size = xt.size(2) num_layers = 2 hidden_size = 8 sequence_length = xt.size(1) #Create Model class VanillaRNN(nn.Module): def __init__(self, input_size, hidden_size, sequence_length, num_layers, device): super(VanillaRNN, self).__init__() self.device = device self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Sequential(nn.Linear(hidden_size * sequence_length, 3), nn.ReLU()) #1 => 3으로 수정 def forward(self, x): h0 = torch.zeros(self.num_layers, x.size()[0], self.hidden_size).to(self.device) # 초기 hidden state 설정 out, _ = self.rnn(x, h0) # out: RNN의 마지막 레이어로 부터 나온 output feature 반환, hn: hidden state 반환 out = out.reshape(out.shape[0], -1) # many to many 전략 out = self.fc(out) return out model = VanillaRNN(input_size=input_size, hidden_size=hidden_size, sequence_length=sequence_length, num_layers=num_layers, device=device).to(device) criterion = nn.MSELoss() lr = 1e-3 num_epochs = 800 optimizer = optim.Adam(model.parameters(), lr=lr) #Learn Model loss_graph = [] n = len(train_loader) for epoch in range(num_epochs): running_loss = 0.0 for data in train_loader: seq, target = data # 배치 데이터 out = model(seq) loss = criterion(out, target) optimizer.zero_grad() loss.backward() optimizer.step() running_loss += loss.item() loss_graph.append(running_loss / n) if epoch % 50 == 0: print('[epoch: %d] loss: %.4f' % (epoch, running_loss / n)) plt.figure(figsize=(20,10)) plt.plot(loss_graph) plt.show() def plotting(train_loader, test_loader, actual): with torch.no_grad(): train_pred = [] test_pred = [] for data in train_loader: seq, target = data # 배치 데이터 # print(seq.size()) out = model(seq) train_pred += out.cpu().numpy().tolist() for data in test_loader: seq, target = data # 배치 데이터 # print(seq.size()) out = model(seq) test_pred += out.cpu().numpy().tolist() total = train_pred + test_pred plt.figure(figsize=(20, 10)) plt.plot(np.ones(100) * len(train_pred), np.linspace(0, 1, 100), '--', linewidth=0.6) plt.plot(actual, '--') plt.plot(total, 'b', linewidth=0.6) plt.legend(['train boundary', 'actual', 'prediction']) plt.show() plotting(train_loader, test_loader, y[:500,0])       x1,y1,x2,y2,x3,y3,x4,y4,x5,y5,x6,y6,x7,y7,x8,y8,x,z,y 0,0,1554.5,639.5,1022,561.5,0,0,0,0,0,0,516,580,291.5,778,2,1.5,-8.5 0,0,1478.5,630.5,975,567.5,0,0,0,0,0,0,465.5,591,303.5,811.5,3,1.5,-8.5 0,0,1407,621.5,926,573,0,0,0,0,0,0,410.5,603,317.5,849,4,1.5,-8.5 0,0,1338.5,614,874.5,579,0,0,0,0,0,0,351,615.5,333.5,893,5,1.5,-8.5 0,0,1274,606,821.5,585,0,0,0,0,1865.5,679.5,286.5,629.5,352.5,945,6,1.5,-8.5 0,0,1212,599,765,592,0,0,1516.5,1073.5,1780.5,661,216,644.5,375,1006.5,7,1.5,-8.5 0,0,1154,592,707,599,0,0,1544,1006.5,1703,644.5,138.5,661,403,1073.5,8,1.5,-8.5 0,0,1097.5,585,645,606,0,0,1566.5,945,1632.5,629.5,53.5,679.5,0,0,9,1.5,-8.5 0,0,1044.5,579,580.5,614,0,0,1585.5,893,1568,615.5,0,0,0,0,10,1.5,-8.5 0,0,993,573,512,621.5,0,0,1601.5,849,1508.5,603,0,0,0,0,11,1.5,-8.5 0,0,944,567.5,440.5,630.5,0,0,1615.5,811.5,1453.5,591,0,0,0,0,12,1.5,-8.5 0,0,897,561.5,364.5,639.5,0,0,1627,778,1403,580,0,0,0,0,13,1.5,-8.5 0,0,882,566.5,368.5,647.5,0,0,1572.5,768.5,1384,583.5,0,0,0,0,13,1.5,-8 0,0,929.5,572.5,446,638,0,0,1557,800,1434.5,595,0,0,0,0,12,1.5,-8 0,0,979.5,578,519,629.5,0,0,1539,836,1489,607,0,0,0,0,11,1.5,-8 0,0,1031,584.5,588.5,620.5,0,0,1519,878,1548,619.5,0,0,0,0,10,1.5,-8 0,0,1085,591,654.5,613,0,0,1495,927,1612.5,634.5,71.5,686.5,0,0,9,1.5,-8 0,0,1142,598,717,605,0,0,1466,985,1683.5,650,157.5,667,487.5,1055.5,8,1.5,-8 0,0,1202,605,777,598,0,0,1431.5,1055.5,1761.5,667,235.5,650,453,985,7,1.5,-8 0,0,1264.5,613,833.5,591,0,0,0,0,1847.5,686.5,306.5,634.5,424,927,6,1.5,-8 0,0,1330.5,620.5,888,584.5,0,0,0,0,0,0,371,619.5,400,878,5,1.5,-8 0,0,1400,629.5,939.5,578,0,0,0,0,0,0,430,607,380,836,4,1.5,-8 0,0,1473,638,989.5,572.5,0,0,0,0,0,0,484.5,595,362,800,3,1.5,-8 0,0,1550.5,647.5,1037,566.5,0,0,0,0,0,0,535,583.5,346.5,768.5,2,1.5,-8 0,0,1546.5,656,1052.5,571.5,0,0,0,0,0,0,555,587,399,759,2,1.5,-7.5 0,0,1467.5,646,1004.5,577.5,0,0,0,0,0,0,504.5,598.5,418,789,3,1.5,-7.5 0,0,1392.5,637,954.5,584,0,0,0,0,0,0,450,611,439.5,824,4,1.5,-7.5 0,0,1322,628.5,901.5,590,0,0,0,0,1916.5,714,391,624.5,463.5,863.5,5,1.5,-7.5 0,0,1254.5,619.5,846.5,597,29.5,1072.5,0,0,1828.5,692.5,326.5,639,492.5,910.5,6,1.5,-7.5 0,0,1191,611.5,789,604,0,0,1352,1031,1742,673,255,655,526.5,965,7,1.5,-7.5 0,0,1130,604,728,611.5,0,0,1392.5,965,1664,655,177,673,567,1031,8,1.5,-7.5 1889.5,1072.5,1072,597,664.5,619.5,0,0,1426.5,910.5,1592.5,639,90.5,692.5,0,0,9,1.5,-7.5 0,0,1017.5,590,597,628.5,0,0,1455.5,863.5,1528,624.5,2.5,714,0,0,10,1.5,-7.5 0,0,964.5,584,526.5,637,0,0,1479.5,824,1469,611,0,0,0,0,11,1.5,-7.5 0,0,914.5,577.5,451.5,646,0,0,1501,789,1414.5,598.5,0,0,0,0,12,1.5,-7.5 0,0,866.5,571.5,372.5,656,0,0,1520,759,1364,587,0,0,0,0,13,1.5,-7.5 0,0,850,576.5,377,665,0,0,1469,750,1344,591,0,0,0,0,13,1.5,-7 0,0,899,583,457.5,655,0,0,1447.5,779,1394,602.5,0,0,0,0,12,1.5,-7 0,0,950,589,534,645,0,0,1423,812.5,1448.5,615.5,0,0,0,0,11,1.5,-7 1882,997,1003,596,606,635.5,0,0,1395,850,1507.5,629,14,722,0,0,10,1.5,-7 1802.5,1038.5,1059,603,674.5,626.5,0,0,1362,894,1572.5,644,110.5,699.5,698.5,1074.5,9,1.5,-7 1711,1075.5,1117.5,610.5,739.5,618.5,0,0,1323.5,946,1643.5,661,197,679.5,642,1008.5,8,1.5,-7 0,0,1179.5,618.5,801.5,610.5,208,1075.5,1277,1008.5,1722,679.5,275.5,661,595.5,946,7,1.5,-7 0,0,1244.5,626.5,860,603,116.5,1038.5,1221.5,1074.5,1808.5,699.5,346.5,644,557,894,6,1.5,-7 0,0,1313,635.5,916,596,37,997,0,0,1905,722,411.5,629,524,850,5,1.5,-7 0,0,1385,645,969,589,0,0,0,0,0,0,470.5,615.5,496,812.5,4,1.5,-7 0,0,1461.5,655,1020,583,0,0,0,0,0,0,525,602.5,471.5,779,3,1.5,-7 0,0,1542,665,1069,576.5,0,0,0,0,0,0,575,591,450,750.5,2,1.5,-7 0,0,1538,674.5,1085.5,582,0,0,0,0,0,0,596,594,498.5,741.5,2,1.5,-6.5 0,0,1455,664,1036.5,588,0,0,0,0,0,0,546,606.5,523,769.5,3,1.5,-6.5 0,0,1377.5,653,985,595,43.5,931,0,0,0,0,492,619.5,550.5,800.5,4,1.5,-6.5 0,0,1303,643.5,931,602,115.5,964.5,0,0,1885,730,432.5,633.5,581.5,837,5,1.5,-6.5 0,0,1233.5,634.5,874,609.5,195,1002.5,1146,1057.5,1788,706.5,368,649.5,618.5,879,6,1.5,-6.5 1530,1079,1167.5,625.5,814.5,617.5,284.5,1044.5,1207.5,987,1701,685.5,296.5,666.5,661,928.5,7,1.5,-6.5 1634.5,1044.5,1104.5,617.5,751.5,625.5,389,1079,1258,928.5,1622,666.5,218,685.5,711.5,987,8,1.5,-6.5 1724,1002.5,1045,609.5,685.5,634.5,0,0,1300.5,879,1551,649.5,131,706.5,773,1057.5,9,1.5,-6.5 1803.5,964.5,988,602,616,643.5,0,0,1337.5,837,1486.5,633.5,34,730,0,0,10,1.5,-6.5 1875.5,931,934,595,541.5,653,0,0,1368.5,800.5,1427,619.5,0,0,0,0,11,1.5,-6.5 0,0,882.5,588,464,664,0,0,1396,769.5,1373,606.5,0,0,0,0,12,1.5,-6.5 0,0,833.5,582,381,674.5,0,0,1420.5,741.5,1323,594,0,0,0,0,13,1.5,-6.5 1917,849.5,816,587.5,385.5,684.5,0,0,1373.5,733,1302,598,0,0,0,0,13,1.5,-6 1870.5,876,866,594,470,673,0,0,1347,760,1351.5,611,0,0,0,0,12,1.5,-6 1805,904.5,918,601,550.5,662,0,0,1316.5,790,1405.5,624.5,0,0,0,0,11,1.5,-6 1733,935,973,609,625.5,652,0,0,1282,825,1464.5,638.5,54,738.5,0,0,10,1.5,-6 1653,969.5,1030.5,616.5,697,642.5,0,0,1242.5,864.5,1529,655,152,714,844,1033,9,1.5,-6 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739.5,618.5,0,0,1711,1075.5,1117.5,610.5,197,679.5,642,1008.5,1323.5,946,1643.5,661,7,1.5,7 674.5,626.5,0,0,1802.5,1038.5,1059,603,110.5,699.5,698.5,1074.5,1362,894,1572.5,644,6,1.5,7 606,635.5,0,0,1882,997,1003,596,14,722,0,0,1395,850,1507.5,629,5,1.5,7 534,645,0,0,0,0,950,589,0,0,0,0,1423,812.5,1448.5,615.5,4,1.5,7 457.5,655,0,0,0,0,899,583,0,0,0,0,1447.5,779,1394,602.5,3,1.5,7 377,665,0,0,0,0,850,576.5,0,0,0,0,1469,750,1344,591,2,1.5,7 372.5,656,0,0,0,0,866.5,571.5,0,0,0,0,1520,759,1364,587,2,1.5,7.5 451.5,646,0,0,0,0,914.5,577.5,0,0,0,0,1501,789,1414.5,598.5,3,1.5,7.5 526.5,637,0,0,0,0,964.5,584,0,0,0,0,1479.5,824,1469,611,4,1.5,7.5 597,628.5,0,0,0,0,1017.5,590,2.5,714,0,0,1455.5,863.5,1528,624.5,5,1.5,7.5 664.5,619.5,0,0,1889.5,1072.5,1072,597,90.5,692.5,0,0,1426.5,910.5,1592.5,639,6,1.5,7.5 728,611.5,0,0,0,0,1130,604,177,673,567,1031,1392.5,965,1664,655,7,1.5,7.5 789,604,0,0,0,0,1191,611.5,255,655,526.5,965,1352,1031,1742,673,8,1.5,7.5 846.5,597,29.5,1072.5,0,0,1254.5,619.5,326.5,639,492.5,910.5,0,0,1828.5,692.5,9,1.5,7.5 901.5,590,0,0,0,0,1322,628.5,391,624.5,463.5,863.5,0,0,1916.5,714,10,1.5,7.5 954.5,584,0,0,0,0,1392.5,637,450,611,439.5,824,0,0,0,0,11,1.5,7.5 1004.5,577.5,0,0,0,0,1467.5,646,504.5,598.5,418,789,0,0,0,0,12,1.5,7.5 1052.5,571.5,0,0,0,0,1546.5,656,555,587,399,759,0,0,0,0,13,1.5,7.5 1037,566.5,0,0,0,0,1550.5,647.5,535,583.5,346.5,768.5,0,0,0,0,13,1.5,8 989.5,572.5,0,0,0,0,1473,638,484.5,595,362,800,0,0,0,0,12,1.5,8 939.5,578,0,0,0,0,1400,629.5,430,607,380,836,0,0,0,0,11,1.5,8  

- 학습 관련 질문을 남겨주세요. 상세히 작성하면 더 좋아요! - 먼저 유사한 질문이 있었는지 검색해보세요. - 서로 예의를 지키며 존중하는 문화를 만들어가요. - 잠깐! 인프런 서비스 운영 관련 문의는 1:1 문의하기를 이용해주세요. 안녕하세요 캡틴판교님, 강의 잘 듣고있습니다. 실습에서 구현한 클래스를 보면 생성자에서 비동기처리를 수행하는 함수가 실행되고(fetchData())있는데요,  다음과같이 만들고 메소드를 실행했을 때 비동기처리때문인지 결과 값이 빈 배열이 나오는 것을 볼 수 있었습니다...   그래서 질문은 해당 실습코드처럼 클래스의 생성자에서 비동기처리를 하는 함수를 실행하는 방식을 많이 이용하나요? 뭔가 제 생각으로는 생성자에서 비동기로 데이터를 받아와 멤버변수에 값을 넣는 경우 멤버함수(메소드)를 외부에서 이용할 경우 위처럼 문제가 생길 것 같아서요. 실습에서 구현한 클래스를 어떻게 잘 사용할 수 있을까요? 제가 이해한 실습코드의 클래스 로직은 이렇습니다.  클래스가 만들어지고(new AddressBook()) 클래스 생성자에서 fetchData() - fetchContracts()실행 -> 비동기로 처리됨 1이 처리되어 멤버변수 contracts에 받아온 데이터가 할당되기 전 외부의 코드 실행됨(위 스샷) 1이 처리되기 전에 실행되었기 때문에 빈배열 출력 --클래스의 메소드를 사용하고 싶은 경우 생성자가 실행된 이후 메소드들을 실행할 수 있도록 async-await나 프로미스를 이용해 처리를 해줘야 할 것으로 생각되는데, 어떻게 구현할 수 있을지 감이 오지 않습니다... 도와주실 수 있으실까요?      

이렇게 오류가 뜨는데 실행은 됩니다 이건 어떠한 경우인가요?

코드를 동일하게 한 것 같은데, 드래그 기능 작동이 안됩니다. 이유가 궁금합니다. import React from "react"; import { DragDropContext, Draggable, Droppable } from "react-beautiful-dnd"; export default function List({ todoData, setTodoData }) { const handleClick = (id) => { let newTodoData = todoData.filter((data) => data.id !== id); console.log("newTodoData", newTodoData); //this.setState({ todoData: newTodoData }); setTodoData(newTodoData); }; const handleCompleteChange = (id) => { let newTodoData = todoData.map((data) => { if (data.id === id) { data.complited = !data.complited; } return data; }); //this.setState({ todoData: newTodoData }); setTodoData(newTodoData); }; return ( <div> <DragDropContext> <Droppable droppableId="todo"> {(provided) => ( <div {...provided.droppableProps} ref={provided.innerRef}> {todoData.map((data, index) => ( <Draggable key={data.id} draggableId={data.id.toString()} index={index} > {(provided, snapshot) => ( <div key={data.id} {...provided.draggableProps} ref={provided.innerRef} {...provided.dragHandleProps} className={`${ snapshot.isDragging ? "bg-gray-400" : "bg-gray-100" } flex items-center justify-between w-full px-4 py-1 my-2 text-gray-600 border rounded`} > <div className="items-center"> <input type="checkbox" defaultChecked={data.complited} onChange={() => handleCompleteChange(data.id)} />{" "} <span className={ data.complited ? "line-through" : undefined } > {data.title} </span> </div> <div className="items-center"> <button className="px-4 py-2 float-right" onClick={() => handleClick(data.id)} > x </button> </div> </div> )} </Draggable> ))} {provided.placeholder} </div> )} </Droppable> </DragDropContext> </div> ); }

강사님 안녕하세요.   지난 번 TDD 오류가 났고 문제 해결했는데   그 이후로 계속해서 오류가 나지 않고 ok만 뜨고 있네요.   이럴 땐 어떻게 문제를 해결하면 좋을까요? 깃허브에 커밋해주신 코드를 살펴보면서 수정하면 좋을까요..?   접근 방법을 잘 몰라서 문의 드립니다.

학습하는 분들께 도움이 되고, 더 좋은 답변을 드릴 수 있도록 질문전에 다음을 꼭 확인해주세요.1. 강의 내용과 관련된 질문을 남겨주세요.2. 인프런의 질문 게시판과 자주 하는 질문(링크)을 먼저 확인해주세요.(자주 하는 질문 링크: https://bit.ly/3fX6ygx)3. 질문 잘하기 메뉴얼(링크)을 먼저 읽어주세요.(질문 잘하기 메뉴얼 링크: https://bit.ly/2UfeqCG)질문 시에는 위 내용은 삭제하고 다음 내용을 남겨주세요.=========================================[질문 템플릿]1. 강의 내용과 관련된 질문인가요? (예/아니오) y2. 인프런의 질문 게시판과 자주 하는 질문에 없는 내용인가요? (예/아니오) n3. 질문 잘하기 메뉴얼을 읽어보셨나요? (예/아니오) y[질문 내용]여기에 질문 내용을 남겨주세요. 안녕하세요 강의를 보고 memberlist와 다른 파일들을 알맞은 package밑에 잘 넣은 것 같은데 localhost:8080/members에서 계속 연결 거부가 뜹니다.. 이런 식으로 뜨는데 구글링해서 찾은 방법으로 해결이 안되어서 질문 남겨봅니다 감사합니다 h2콘솔창은 이러합니다 터미널에서 포트를 열어보니 8080은 검색이 안나오는걸로 확인됩니다

스프링부트스케줄러를 이용해서 mysql에 있는 데이터를 자동삭제 하고 싶습니다.  블로그들을 찾아봐도 딱히 쓸만한 정보를 얻지 못해서 이 글을 작성하게 됩니다. 따롴 클래스를 만들던지 해서 controller에서 처리하고 싶습니다. db에 데이터가 삽입된 기준으로 하던지 현재 시간을 기준으로 최근 1달 정도 데이터를 유지하던지 그런 방식으로 진행하고 싶습니다. 알려주십쇼 ..

실습 자료를 다운받아 그대로 실행 시켰는데 오류가 뜹니다. 어떤게 문제인지 모르겠습니다. history = model.fit(train_padded, train_labels,                      validation_data=(validation_padded, validation_labels),                     callbacks=[checkpoint],                     epochs=epochs)   오류 내용은  UnknownError Traceback (most recent call last) <ipython-input-38-db48157b102c> in <module> 2 validation_data=(validation_padded, validation_labels), 3 callbacks=[checkpoint], ----> 4 epochs=epochs) 1 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 53 ctx.ensure_initialized() 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None: UnknownError: Graph execution error: Fail to find the dnn implementation. [[{{node CudnnRNN}}]] [[sequential/bidirectional/forward_lstm/PartitionedCall]] [Op:__inference_train_function_9113]

안녕하세요 학습중에 질문드립니다. 다름이 아니라 궁금증이 있습니다. localstorage에 있는key값을 받아와 List에 뿌려주는 방식인데 TodoList.vue내에서 키값 중복에 대한 유효성검사를 따로 하지 않았는데 중복값이 들어가지 않습니다. localStorage내부에서 자체적으로 중복값 유효성검사를 하는건가요? 알고싶습니다.

df = pd.read.csv('/kaggle/input/london-bike-sharing-dataset/london_merged.csv',arse_dates * ['timestamp'])df.head()   이거 맞나요?

Category 컴포넌트가 아래 그림과  같이 아래로 생성됩니다. 의미적으로는 div 태그가 추가되는 것이어서 아래 출력이 맞는 것 같긴 한대요. 강사님 수업에서는 옆으로 Category가 붙더라구요. (아래 코드는 Github 강사님 수업 소스에서 API로 읽어와 상태변경하는 부분만 변경하였음)      

앞서 @Component나 @Autowired를 배우기 이전에는 코드를 아래처럼 작성했었습니다. public class MemberServiceImpl implements MemberService{ private final MemberRepository memberRepository; public MemberServiceImpl(MemberRepository memberRepository) { this.memberRepository = memberRepository; } @Component, @Autowired와 같은 Annotation을 사용하지는 않았지만 AppConfig를 통해 직접 스프링빈으로 등록해두었다고 이해하고 있는데,   그렇다면 저 생성자 주입이 이루어지는 부분은 스프링에 의한 '의존관계 자동주입'이 맞을까요?   @Autowired는 생성자가 하나일 때 생략이 가능하다고 했으니, 위의 예제는 스프링빈을 직접 등록한 뒤 @Autowired가 생략된 형태로 생성자 주입을 했다고 볼 수 있는건지요?   추가로, @Autowired를 사용하기 위해서는 DI의 대상이 되는 memberService, memberRepository 모두 스프링빈이어야 하는지, 주입되는 memberRepository만 스프링빈이어도 가능한 것인지 궁금합니다.

안녕하세요 김영한 강사님,   OSIV를 false로 하면 트랜잭션 라이프사이클이랑 영속성 라이프 사이클이이 같이 끝맺음을 가지는거로 이해했습니다.   그렇다면 트랜잭션 안에서 엔티티를 save메소드로 저장 한다면 트랜잭션이 끝나고난뒤에 DB에 저장하기위해 인서트문이 날라가는게 아닌가요?   트랜잭션 메소드를 끝낸뒤 로그로 엔티티는 찍히는데 디비에는 저장이안되어있어서   findBy등으로 찾아오지못해 제가 정확히 이해를 못하는것인지, 아니면 잘못 사용하고있는것인지 궁금합니다.   트랜잭션안에서 save를 한뒤 정상적으로 끝난뒤에도 디비에는 존재하지 않는 이유는 무엇인가요?  

step1의 batchstatus가 왜 complete가 아닌 abandoned인지 잘 모르겠습니다. 

안녕하세요 🙋‍♂️한 입 크기로 잘라먹는 리액트 강사 이정환 winterlood입니다!물론 질문은 수강생 여러분들 편하게 해 주시면 되지만!아래의 몇 가지 규칙만 지켜주시면 더 빠르고 정확한 답변을 드릴 수 있습니다!- 개인적인 고민 상담도 받아드립니다 :)- 강의의 몇 분 몇 초 관련 질문인지 알려주세요!- 서로 예의를 지키며 존중하는 문화를 만들어가요. - 인프런 서비스 운영 관련 문의는 1:1 문의하기를 이용해주세요.   안녕하세요! 최종 프로젝트 edit 페이지 구현하다가 감정 이미지가 제대로 나타나지 않아서 질문드립니다! 강사님처럼 DiaryEditor 컴포넌트를 만들어서 new 페이지랑 edit 페이지에 공통으로 사용하였는데, new 페이지에서는 잘 나오는 이미지가 edit 페이지에서는 제대로 안 나오더라구요. 그리고 diary 컴포넌트에서도 이미지가 나오지 않습니다.. emotionList의 img 경로에 문제가 있는 걸까요? 저와 비슷한 질문을 남기신 분께 process.env 를 수정하셔야 할 거 같다고 강사님이 답변을 남기셨던던데, 제가 그것만 보고는 어떻게 수정을 해야할지 잘 감이 오지 않아 코드 샌드박스로 코드 남깁니다!   codesandbox-url   감사합니다!

강의대로 똑같이 따라했는데, 테이블이 사진처럼 item, orderitem 두가지 밖에 생기지 않습니다. 혹시 제가 어디 잘못 작성한 것이 있는지 알 수 있을까요?