목록분류 전체보기 (318)
printf("ho_tari\n");
Queue3 (array 클래스와 has-a 관계)
#include #include "queue.h" int main() { //Queue q1; // Queue q1(100); //Queue q2(10); // Queue q2 = 10; //Queue q3 = q2; //q1 = q2; Queue s1(10), s2(100); s1.push(100); s1.push(200); std::cout
Queue2 (생성자 초기화 리스트, static member, explicit)
#include #include "queue.h" int main() { //Queue q1; // Queue q1(100); //Queue q2(10); // Queue q2 = 10; //Queue q3 = q2; //q1 = q2; Queue s1(10), s2(100); s1.push(100); s1.push(200); std::cout
HW2
Breast Cancer Wisconsin Dataset Classification problem ◦ 10 input variables ◦ 1 binary output variable (benign or malignant) 569 data samples ◦ Use the first 100 samples as test set ◦ Use the next 100 samples as validation set ◦ Use the others as training set ◦ Use Numpy slicing. Do not use the “train_test_split” function. Data Preparation Download breast-cancer-wisconsin.data Remove the row..
Stack2 (extern, explicit)
#include #include "stack.h" int main() { Stack s1(10); Stack s2(100); s1.push(100); //구조체 자료는 인자를 전달할 때 오버헤드를 줄이기 위해 포인터를 쓴다. s1.push(200); s1.push(300); std::cout
PCA #13
from tensorflow.keras import layers, models from tensorflow.keras.datasets import mnist from tensorflow.keras import backend as K import numpy as np import matplotlib.pyplot as plt def Conv2D(filters, kernel_size, padding="same", activation="relu"): return layers.Conv2D(filters, kernel_size, padding=padding, activation=activation) class SCAE(models.Model): def __init__(self, org_shape=(1,28,28))..
PCA #12
from tensorflow.keras import layers, models class AE(models.Model): def __init__(self, x_nodes=784, z_dim=36): x_shape = (x_nodes,) x = layers.Input(shape=x_shape) z = layers.Dense(z_dim, activation='relu')(x) y = layers.Dense(x_nodes, activation='sigmoid')(z) # Essential parts: super().__init__(x, y) self.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Optional Par..