개발잡부

import tensorflow as tf from tensorflow.keras import datasets, layers, models import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() #32x32x3 #Norma train_images, test_images = train_images/255.0, test_images/255.0 class_names = ['airplane','automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] plt.figure(fig..

!pip install -q -U tensorflow !pip install -q tensorflow_datasets import numpy as np import tensorflow_datasets as tfds import tensorflow as tf tfds.disable_progress_bar() import matplotlib.pyplot as plt def plot_graphs(history, metric): plt.plot(history.history[metric]) plt.plot(history.history['val_'+metric], '') plt.xlabel("Epochs") plt.ylabel(metric) plt.legend([metric, 'val_'+ metric]) data..

import tensorflow as tf from tensorflow.keras import datasets, layers, models import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() #32x32x3 #Norma train_images, test_images = train_images/255.0, test_images/255.0 이미지 확인 class_names = ['airplane','automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] plt.fig..

import numpy as np import matplotlib.pyplot as plt import random param1 = random.randint(1,5) #기울기 param2 = random.randint(20,40) #편향 data = [[3,24], [6,48], [9,40], [12, 52]] x = np.array([i[0] for i in data]) y = np.array([i[1] for i in data]) plt.plot(x,y,"b.") def model(x): y = param1 * x + param2 return y def rmse(predict, true): return np.sqrt(((predict - true) ** 2).mean()) predict = [] f..

local에서 테스트 할려고 하니까 안되네.. google colab 에서 해야함.. import tensorflow as tf from tensorflow import keras import numpy as np import matplotlib.pyplot as plt fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle bo..

https://colab.research.google.com/ Google Colaboratory colab.research.google.com 새노트 파이썬 코드 입력 실행 - 재생버튼 클릭 (command + enter) 런타임 - 런타임 유형변경 하드웨어 가속기를 GPU , TPU 로 변경할 수 있다. 코드블럭 추가 코드실행 - 런타임 초기화 텍스트 블럭 추가