from keras.datasets import mnist from keras.utils import to_categorical from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Dense from keras.layers import Flatten from keras.optimizers import SGD # load train and test da...
from keras.dataset import mnist mnist.load_data(path) path是保存的路径 模型结构 model1.png 这个模型用了两个Convolution2D层,两个MaxPooling2D层,一个Flatten层,两个全连接Dense层,使用的激活函数是relu,优化器是adam 训练代码 fromkeras.model importSequentialfromkeras.layers import Convolution2D,Dense,Flatte...
from keras.layers import Dense from keras.layers import Dropout # Dropout将在训练过程中每次更新参数时按一定概率(rate)随机断开输入神经元,Dropout层用于防止过拟合。 from keras.layers import Flatten # Flatten层用来将输入“压平”,即把多维的输入一维化,常用在从卷积层到全连接层的过渡。Flatten不影响batch...
keras下解决 example 案例中 MNIST 数据集下载不了的问题 keras 源码中下载MNIST的方式是 path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz'),数据源是通过 url = https://s3.amazonaws.com/img-datasets/mnist.npz 进行下载的。访问该 url 地址被墙了,导致 MNIST 相关的...
用from torch.utils.data import DataLoader进行导入, train_load=DataLoader(dataset=train_data,batch_size=100,shuffle=True) test_load=DataLoader(dataset=test_data,batch_size=100,shuffle=True) 随机加载批量大小为l00数据给train_load和test_load,每个变量都由两部分组成,用迭代器将两部分分开 ...
ds = ms.dataset.MnistDataset(data_train) print(data_train) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 以上在华为云上不断报错,而且无法排除错误,于是我参考了网上视频教程,使用keras(基于tensorflow框架)第三方库在本地电脑上运行: ...
Keras之01-用MNIST数据集训练一个DNN 模型code # -*- coding: utf-8 -*-'''Trains a simple deep NN on the MNIST dataset. Gets to 98.40% test accuracy after 20 epochs (there is *a lot* of margin for parameter tuning). 2 seconds per epoch on a K520 GPU. ...
from keras.datasets import mnist import numpy as np import matplotlib.pyplot as plt 2、设置GPU(我下载的tensorflow-gpu 默认使用GPU)只使用GPU if gpus:gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用 tf.config...
importkeras fromkeras.datasetsimportmnist #load mnist dataset (X_train, y_train), (X_test, y_test) = mnist.load_data()#everytime loading data won't be so easy :) 其中,X_train 包含 6 万张 大小为 28x28 的训练图片,y_train 包含这些图片对应的标签。与之类似,X_test 包含了 1 万张大...
from __future__ import print_function from ..utils.data_utils import get_file import numpy as np def load_data(path='mnist.npz'):"""Loads the MNIST dataset.# Arguments path: path where to cache the dataset locally (relative to ~/.keras/datasets).# Returns Tuple of Numpy arrays: `(x...