Be Brave, Be Humble

https://github.com/FulISun/Computer_Vision/blob/main/CNN/03_Fashion_MNIST_callbacks.ipynb Fashion MNIST Example 정확도가 잘 안나오면 하이퍼 파라미터 먼저 수정하기~ learning rate -> epoch -> architecture 순으로 수정할 것 accuracy는 그대로인데 loss만 감소하는 경우는 맞추는 값만 맞추고 틀린 값은 계속 틀려서 => decision making 필요함. 데이터 확인해서 코딩 오류 수정하거나 해당 값 삭제 해야함 ex) 정답 = [ 1, 2, 3, 4 ] 예측1 = [ 1, 2, 3, 3 ] 예측2 = [ 1, 2, 3, 10 ] 인 경우 둘 다 정확도는 0.75지만 loss가 예..

https://github.com/FulISun/Computer_Vision/blob/main/CNN/02_NN_MNIST_sparse_dropout_batchnormalization.ipynb Sparse ver [1] MNIST Data 생성 및 확인 In [ ]: import tensorflow as tf import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten, Dropout, BatchNormalization from tensorflow.keras.datasets import mnist from tensorflow.keras.utils impor..

https://github.com/FulISun/Computer_Vision/blob/main/CNN/01_NN_MNIST_example_onehot.ipynb MNIST Example [1] MNIST Data 생성 및 확인 In [ ]: import tensorflow as tf import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.datasets import mnist from tensorflow.keras.utils import to_categorical In [ ]: (X_train, t_train), ..

[1] 데이터 로드 및 기본 정보 확인 In [ ]: import matplotlib import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('./kaggle_diabetes.csv') In [ ]: df.head() Out[ ]: PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome01234 6 148 72 35 0 33.6 0.627 50 1 1 85 66 29 0 26.6 0.351 31 0 8 183 64 0 0 23.3 0.672 32 1 1 89 66 23 94 28.1 0.167 21 0 0 137 40 35 168 43..

binary classification의 정답 종류 2개(0 or 1) => sigmoid는 0.5 기준으로 0/1 나누므로 출력층 노드 수는 1개면 됨. multiple classification의 정답 종류 n개(0, 1, 2, ..., n) => 출력노드는 정답의 수인 n One-Hot In [ ]: import tensorflow as tf from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Flatten, Input, Dense from tensorflow.keras.optimizers import SGD, Adam In [ ]: import numpy as np try: loaded_d..

Logistic Regression Example In [ ]: import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Dense from tensorflow.keras.optimizers import SGD, Adam [1] 데이터 생성 In [ ]: import numpy as np try: loaded_data = np.loadtxt('./diabetes.csv', delimiter = ',') x_data = loaded_data[ :, 0:-1] t_data = loaded_data[ :, [-1]] print(x_data.shape) print..

Functional API In [ ]: import tensorflow as tf from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Flatten, Dense, Input from tensorflow.keras.optimizers import SGD import numpy as np Data Preparation In [ ]: x_data = np.array([ 1, 2, 3, 4, 5, 6]) t_data = np.array([ 3, 4, 5, 6, 7, 8]) Modeling: init W & b randomly In [ ]: input_ = Input(shape=(1,)) # x= Fla..