공대생 도전 일지

2023.11.30 - [인공지능/공부] - 센서신호, FFT, STFT data를 통해 하중 예측하기 센서신호, FFT, STFT data를 통해 하중 예측하기 import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout, Conv2D, MaxPooling2D, GlobalAveragePooling2D,Conv1 yoonschallenge.tistory.com 아직 RNN에 대해 정확히 공부하지 않아 ..

import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout, Conv2D, MaxPooling2D, GlobalAveragePooling2D,Conv1D, MaxPooling1D, GlobalAveragePooling1D,LSTM from keras.utils import plot_model, to_categorical from keras.regularizers import l2 from sklearn..

자동차 대회를 그렇게 나가고도 상을 하나도 못 받더니 인공지능으로 하나 받았네요. 컴퓨터 사이언스 사이에서 공대 혼자 있습니다 앞으로도 열심히 해보쟈구여

처음보곤 뭔 말이지 싶었다. 또 정답이 있는지도 모르고 인코딩 디코딩으로 풀려고 했었다..... 그러나 ytrain이 있는 것을 한참 뒤에 알았고, 그 이후부터 학습을 시작했다. 데이터 읽기 부터가 문제다.. import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout, Conv2D, MaxPooling2D, GlobalAveragePooling2D,Conv1D, MaxPooling1D, GlobalAv..

숫자맞추기는 이미 했던 거니까! import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout, Conv2D, MaxPooling2D, GlobalAveragePooling2D from keras.utils import plot_model, to_categorical from keras.regularizers import l2 from sklearn.model_selection import train_te..

import torch import torch.nn as nn import torch.nn.functional as f import torch.optim as optim from torch.utils.data import TensorDataset from torch.utils.data import DataLoader import numpy as np import matplotlib.pyplot as plt import pandas as pd from google.colab import drive drive.mount('/content/drive') data = pd.read_csv("/content/drive/MyDrive/Colab Notebooks/health.csv") h = torch.FloatT..

import tensorflow as tf import numpy as np import pandas as pd from keras.datasets.mnist import load_data from keras.models import Sequential, Model from keras.layers import Dense, Input ,Flatten, Dropout from keras.utils import plot_model, to_categorical from keras.regularizers import l2 from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from google.colab impor..

import numpy as np import matplotlib.pyplot as plt import scipy.io # Setup the parameters you will use for this part of the exercise input_layer_size = 400 num_labels = 10 from google.colab import drive drive.mount('/content/drive') # Given code def displayData(X, example_width=None): if example_width is None: example_width = int(round(np.sqrt(X.shape[1]))) plt.set_cmap("gray") m, n = X.shape ex..

import numpy as np import matplotlib.pyplot as plt # ==================== Load Data ==================== # The first two columns contains the exam scores and the third column # contains the label. from google.colab import drive drive.mount('/content/drive') data = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ex2data2.txt', delimiter=',') data = np.array(data) X = data[:,0:2] y = data[:,2:3..

import numpy as np import matplotlib.pyplot as plt from google.colab import drive drive.mount('/content/drive') # ==================== Load Data ==================== # The first two columns contains the exam scores and the third column # contains the label. data = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ex2data1.txt', delimiter=',') data = np.array(data) X = data[:,0:2] y = data[:,2:3..