수수께끼 고양이

import tensorflow as tf mnist=tf.keras.datasets.cifar10 (x_train, y_train),(x_test, y_test)=mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 # 모든 픽셀의 숫자를 255.0으로 나누어 각 픽셀을 실수로 바구어 인공신경망에 입력하게 됨 model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(32,32,3)), tf.keras.layers.Conv2D(24,(3,3),(1,1),activation='relu'), # 커널5*5 사이즈 -> stride 2,2 tf.keras.layers.Conv..

참고 링크 End-to-End Deep Learning for Self-Driving Cars | NVIDIA Technical Blog End-to-End Deep Learning for Self-Driving Cars | NVIDIA Technical Blog We have used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. developer.nvidia.com 엔디비아 디벨롭 예제 1 import tensorflow as tf model = tf.keras.Sequential([ tf.keras.laye..

필터의 갯수를 줄이고 cifar10 데이터 셋 학습하기(cifar10으로 데이터 셋 바꾸기) import tensorflow as tf mnist=tf.keras.datasets.cifar10 (x_train, y_train),(x_test, y_test)=mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(28,28,1)), tf.keras.layers.Conv2D(32,(3,3),activation='relu', padding='same'), tf.keras.layers.MaxPool2D((2,2)), tf...

Conv2D-Conv2D-MaxPooling2D import tensorflow as tf mnist=tf.keras.datasets.fashion_mnist (x_train, y_train),(x_test, y_test)=mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train = x_train.reshape((60000, 28, 28, 1)) x_test = x_test.reshape((10000, 28, 28, 1)) model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(28,28,1)), tf.keras.layers.Conv2D(32,(3,3),ac..

6x6x2 입력 import numpy as np np.random.seed(1) image=np.random.randint(5, size=(4,4,2)) print('image_0=\n', image[:,:,0]) print('image_1=\n', image[:,:,1]) filter=np.random.randint(5, size=(3,3,2)) print('filter_0=\n', filter[:,:,0]) print('filter_1=\n', filter[:,:,1]) image_pad=np.pad(image,((1,1),(1,1),(0,0))) print('image_pad_0=\n', image_pad[:,:,0]) print('image_pad_1=\n', image_pad[:,:,1]) c..

3x3x2 입력 이미지 import numpy as np np.random.seed(1) image=np.random.randint(5, size=(3,3,2)) print('image_0=\n', image[:,:,0]) print('image_1=\n', image[:,:,1]) filter=np.random.randint(5, size=(3,3,2)) print('filter_0=\n', filter[:,:,0]) print('filter_1=\n', filter[:,:,1]) image_x_filter=image*filter print('image_x_filter_0=\n', image_x_filter[:,:,0]) print('image_x_filter_1=\n', image_x_filter[:..

1. 1~9 사이의 정수값으로 구성된 3x3 랜덤 필터를 만들어 적용해보시오 import numpy as np import cv2 import matplotlib.pyplot as plt image_color=cv2.imread('../images/cat.jpg') print('image_color.shape =',image_color.shape) image=cv2.cvtColor(image_color,cv2.COLOR_BGR2GRAY) print('image.shape =',image.shape) np.random.seed(1) filter=np.random.randint(1,10, size=(3,3,3))/9 image_pad=np.pad(image,((1,1),(1,1))) print('image_..

import numpy as np import cv2 import matplotlib.pyplot as plt image_bgr=cv2.imread('../images/cat.jpg') image=cv2.cvtColor(image_bgr,cv2.COLOR_BGR2RGB) print('image.shape =',image.shape) filter=np.array([[ [1,1,1], [1,1,1], [1,1,1] ],[ [1,1,1], [1,1,1], [1,1,1] ],[ [1,1,1], [1,1,1], [1,1,1] ]])/9 image_pad=np.pad(image,((1,1),(1,1),(0,0))) print('image_pad.shape =', image_pad.shape) convolution=..

1. import numpy as np import cv2 import matplotlib.pyplot as plt image_color=cv2.imread('../images/cat.jpg') print('image_color.shape =',image_color.shape) image=cv2.cvtColor(image_color,cv2.COLOR_BGR2GRAY) print('image.shape =',image.shape) filter=np.array([ [1,1,1], [1,1,1], [1,1,1] ])/9 image_pad=np.pad(image,((1,1),(1,1))) print('image_pad.shape =', image_pad.shape) convolution=np.zeros_like..

import numpy as np np.random.seed(1) H,W,C=56,156,48 F=128 image=np.random.randint(5,size=(H,W,C)) print(image.shape) filter=np.random.randint(5,size=(F,3,3,C)) print('filter_0=\n', filter[0]) print('filter_0=\n', filter[1]) image_pad=np.pad(image,((1,1),(1,1),(0,0))) print('image_pad=\n', image_pad) convolution=np.zeros((H,W,F)) for fn in range(F): for row in range(H): for col in range(W): wind..