【TF2.0応用編】TFの例の強いデータセット機能で汎用的なDataAugmentationを並列化しハイスピードで実現した件 - Qiitaimport tensorflow as tf import tensorflow.keras as keras import matplotlib.pyplot as plt import sklearn import numpy as np from tqdm import tqdm (tr_x,tr_y),(te_x,te_y)=keras.datasets.cifar10.load_data() tr_x, te_x = tr_x/255.0, te_x/255.0 tr_y, te_y = tr_y.reshape(-1,1), te_y.reshape(-1,1) model = keras.models.Sequential() model.add(keras.layers.Convolution2D(32,3,padding="same",activation="relu"
