cifar10_dir = 'C:/Users/1/.keras/datasets/cifar-10-batches-py' (train_images, train_labels), (test_images, test_labels) = load_data(cifar10_dir) 1. 2. 注意:在官网下好cifar10数据集后将其解压成下面形式 load_local_cifar10.py from __future__ import absolute_import from __future__ ...
datasets, Model, layers, Sequential, losses4fromtensorflow.keras.layersimportConv2D, Dense, add, BatchNormalization, GlobalAveragePooling2D5importmatplotlib.pyplot as plt67#load data ---8(x, y), (x_test, y_test) =datasets.cifar10.load_data()9y = tf.squeeze(y, axis=1)10y_test = tf.squ...
代码如下: 1#encoding: utf-82importtensorflow as tf3fromtensorflowimportkeras4fromtensorflow.kerasimportlayers, Sequential, losses, optimizers, datasets5importmatplotlib.pyplot as plt67#load data8(x, y), (x_test, y_test) =datasets.cifar10.load_data()9y = tf.squeeze(y, axis=1)10y_test = tf...
cifar10_dir = 'C:/Users/1/.keras/datasets/cifar-10-batches-py'(train_images, train_labels), (test_images, test_labels) = load_data(cifar10_dir) 注意:在官网下好cifar10数据集后将其解压成下面形式 load_local_cifar10.py from __future__ import absolute_importfrom __future__ import divisi...
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() 1. 具体的下载路径就是这里: C:\Users\tuhoo\.keras\datasets 1. 上面的代码跑完, 我们就可以看到具体的图片了: 如何自己下载数据 keras cifar10.load_data() 自己下载数据 ...
import tensorflow as tf from keras.datasets import cifar10 from keras.utils import np_utils import numpy as np # 加载CIFAR-10数据集 (x_train, y_train), (x_test, y_test) = cifar10.load_data() # 将像素值归一化到0到1的范围内 x_train = x_train.astype('float32') / 255.0 x_test...
加载cifar10数据集 代码语言:javascript 代码运行次数:0 运行 AI代码解释 cifar10_dir = 'C:/Users/1/.keras/datasets/cifar-10-batches-py' (train_images, train_labels), (test_images, test_labels) = load_data(cifar10_dir) 注意:在官网下好cifar10数据集后将其解压成下面形式 load_local_cifar10....
(train_images,train_labels),(test_images,test_labels)=datasets.cifar10.load_data()# 将像素的值标准化至0到1的区间内。train_images,test_images=train_images/255.0,test_images/255.0 2.构建卷积神经网络模型 tf.keras.layers.Conv2D 2D卷积层(例如,图像上的空间卷积)。
keras import backend as K import numpy as np import os import sys from six.moves import cPickle def load_batch(fpath, label_key='labels'): """Internal utility for parsing CIFAR data. # Arguments fpath: path the file to parse. label_key: key for label data in the retrieve dictionary...
import tensorflow.keras as keras from keras.datasets import mnist #加载数据集 (trainX, trainY), (testX, testY) = keras.datasets.cifar10.load_data() 接下来,我们将自己实现一个深度可分离的卷积层。其实,Tensorflow中已经有一个实现,但是我们将在最后一个示例代码中再讨论它。