PyTorch Layer 이해하기 Load Packages import torch from torchvision import datasets, transforms import numpy as np import matplotlib.pyplot as plt %matplotlib inline 예제 불러오기 train_loader = torch.utils.data.DataLoader( datasets.MNIST('dataset', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor() ])), batch_size=1) image, label = next(iter(train_loader)) image.shape, label.s..

PyTorch Data Preprocess import torch from torchvision import datasets, transforms Import Error ImportError: cannot import name 'PILLOW_VERSION' from 'PIL' pillow 버전이 7.0.0 이상 일경우 Import 에러 나는 경우가 있습니다. 아래 처럼 pillow 버전을 내려주면 해결이 됩니다. $ pip install pillow==6.2.2 Data Loader 부르기 Pytorch는 DataLoader를 불러 model에 넣습니다. batch_size = 32 train_loader = torch.utils.data.DataLoader( datasets.MNIST('dataset/..

TensorFlow 2.0 import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import datasets Hyperparameter batch_size = 64 learning_rate = 0.001 dropout_rate = 0.7 input_shape = (28, 28, 1) num_classes = 10 Preprocess (train_x, train_y), (test_x, test_y) = datasets.mnist.load_data() train_x = train_x[..., tf.newaxis] test_x = test_x[..., tf.newaxis] train_x = train_x / 255. ..

Load Packages import numpy as np import torch Basic PyTorch 기초 사용법 nums = torch.arange(9) nums.shape nums.numpy() nums.reshape(3, 3) randoms = torch.rand((3, 3)) zeros = torch.zeros((3, 3)) ones = torch.ones((3, 3)) torch.zeros_like(ones) Operations PyTorch로 수학연산 하기 nums * 3 nums = nums.reshape((3, 3)) nums + nums result = torch.add(nums, 10) result.numpy() # Out array([[10, 11, 12], [13, 14, 15..

Load Packages import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import datasets Build Model input_shape = (28, 28, 1) num_classes = 10 learning_rate = 0.001 inputs = layers.Input(input_shape, dtype=tf.float64) net = layers.Conv2D(32, (3, 3), padding='SAME')(inputs) net = layers.Activation('relu')(net) net = layers.Conv2D(32, (3, 3), padding='SAME')(net) net = laye..

TensorFlow 공식 홈페이지에서 설명하는 Expert 버전을 사용해봅니다. Load Packages import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import datasets 학습 과정 돌아보기 Build Model input_shape = (28, 28, 1) num_classes = 10 inputs = layers.Input(input_shape, dtype=tf.float64) net = layers.Conv2D(32, (3, 3), padding='SAME')(inputs) net = layers.Activation('relu')(net) net = layers.Conv2D(32, (3, 3..

Load Packages import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import datasets 학습 과정 돌아보기 Prepare MNIST Datset (train_x, train_y), (test_x, test_y) = datasets.mnist.load_data() Build Model inputs = layers.Input((28, 28, 1)) net = layers.Conv2D(32, (3, 3), padding='SAME')(inputs) net = layers.Activation('relu')(net) net = layers.Conv2D(32, (3, 3), padding='SAME')(..

Load Packages import tensorflow as tf import os import matplotlib.pyplot as plt %matplotlib inline Input Image from tensorflow.keras import datasets (train_x, train_y), (test_x, test_y) = datasets.mnist.load_data() image = train_x[0] # 차원 수 높이기 image = image[tf.newaxis, ..., tf.newaxis] image.shape # Out (1, 28, 28, 1) Feature Extraction Convolution filters: layer에서 나갈 때 몇 개의 filter를 만들 것인지 kern..

Load Packages import numpy as np import matplotlib.pyplot as plt import tensorflow as tf %matplotlib inline 데이터 불러오기 TensorFlow에서 제공해주는 데이터셋(MNIST) 예제 불러오기 입니다. from tensorflow.keras import datasets mnist = datasets.mnist (train_x, train_y), (test_x, test_y) = mnist.load_data() train_x.shape # Out (60000, 28, 28) Image Dataset 들여다보기 불러온 데이터셋에서 이미지 데이터 하나만 뽑아서 시각화합니다. 데이터 하나만 뽑기 image = train_x[0..

Load Packages import numpy as np import tensorflow as tf Tensor 생성 list -> Tensor tf.constant([1, 2, 3]) # Out tuple -> Tensor tf.constant(((1, 2, 3), (1, 2, 3))) # Out Array -> Tensor arr = np.array([1, 2, 3]) tf.constant(arr) # Out Tensor에 담긴 정보 확인 shape 확인 tensor = tf.constant(arr) tensor.shape # Out TensorShape([3]) Data Type 확인 주의: Tensor를 생성 할 때 Data Type을 정해주지 않기 때문에 혼동이 올 수 있습니다. Data Ty..