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百度抓取网站,百度 seo 工具,网站续费查询,开发项目管理软件win10 ， cpu的情况下安装的mindspore。目前因为环境问题，所以，很少程序支持。下面我给出程序，验证过程和结果。参考资料： https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html 程序案例路…

win10 ， cpu的情况下安装的mindspore。目前因为环境问题，所以，很少程序支持。下面我给出程序，验证过程和结果。

参考资料： https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html

程序案例路径： https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/lenet.py

程序我稍微做了点改动。

import os
import urllib.request
from urllib.parse import urlparse
import gzip
import argparse
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
from mindspore.common.initializer import TruncatedNormal
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.transforms.vision import Inter
from mindspore.nn.metrics import Accuracy
from mindspore.common import dtype as mstype
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogitsdef unzipfile(gzip_path):"""unzip dataset fileArgs:gzip_path: dataset file path"""open_file = open(gzip_path.replace('.gz',''), 'wb')gz_file = gzip.GzipFile(gzip_path)open_file.write(gz_file.read())gz_file.close()def download_dataset():"""Download the dataset from http://yann.lecun.com/exdb/mnist/."""print("******Downloading the MNIST dataset******")train_path = "./MNIST_Data/train/"test_path = "./MNIST_Data/test/"train_path_check = os.path.exists(train_path)test_path_check = os.path.exists(test_path)if train_path_check == False and test_path_check ==False:os.makedirs(train_path)os.makedirs(test_path)train_url = {"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"}test_url = {"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"}for url in train_url:url_parse = urlparse(url)# split the file name from urlfile_name = os.path.join(train_path,url_parse.path.split('/')[-1])if not os.path.exists(file_name.replace('.gz','')):file = urllib.request.urlretrieve(url, file_name)unzipfile(file_name)os.remove(file_name)for url in test_url:url_parse = urlparse(url)# split the file name from urlfile_name = os.path.join(test_path,url_parse.path.split('/')[-1])if not os.path.exists(file_name.replace('.gz','')):file = urllib.request.urlretrieve(url, file_name)unzipfile(file_name)os.remove(file_name)def create_dataset(data_path, batch_size=32, repeat_size=1,num_parallel_workers=1):""" create dataset for train or testArgs:data_path: Data pathbatch_size: The number of data records in each grouprepeat_size: The number of replicated data recordsnum_parallel_workers: The number of parallel workers"""# define datasetmnist_ds = ds.MnistDataset(data_path)# define operation parametersresize_height, resize_width = 32, 32rescale = 1.0 / 255.0shift = 0.0rescale_nml = 1 / 0.3081shift_nml = -1 * 0.1307 / 0.3081# define map operationsresize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Resize images to (32, 32)rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize imagesrescale_op = CV.Rescale(rescale, shift) # rescale imageshwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network# apply map operations on imagesmnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)# apply DatasetOpsbuffer_size = 10000mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train scriptmnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)mnist_ds = mnist_ds.repeat(repeat_size)return mnist_dsdef conv(in_channels, out_channels, kernel_size, stride=1, padding=0):"""Conv layer weight initial."""weight = weight_variable()return nn.Conv2d(in_channels, out_channels,kernel_size=kernel_size, stride=stride, padding=padding,weight_init=weight, has_bias=False, pad_mode="valid")def fc_with_initialize(input_channels, out_channels):"""Fc layer weight initial."""weight = weight_variable()bias = weight_variable()return nn.Dense(input_channels, out_channels, weight, bias)def weight_variable():"""Weight initial."""return TruncatedNormal(0.02)class LeNet5(nn.Cell):"""Lenet network structure."""# define the operator requireddef __init__(self):super(LeNet5, self).__init__()self.conv1 = conv(1, 6, 5)self.conv2 = conv(6, 16, 5)self.fc1 = fc_with_initialize(16 * 5 * 5, 120)self.fc2 = fc_with_initialize(120, 84)self.fc3 = fc_with_initialize(84, 10)self.relu = nn.ReLU()self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)self.flatten = nn.Flatten()# use the preceding operators to construct networksdef construct(self, x):x = self.conv1(x)x = self.relu(x)x = self.max_pool2d(x)x = self.conv2(x)x = self.relu(x)x = self.max_pool2d(x)x = self.flatten(x)x = self.fc1(x)x = self.relu(x)x = self.fc2(x)x = self.relu(x)x = self.fc3(x)return xdef train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb):"""Define the training method."""print("============== Starting Training ==============")# load training datasetds_train = create_dataset(os.path.join(mnist_path, "train"), 32, repeat_size)model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)def test_net(args, network, model, mnist_path):"""Define the evaluation method."""print("============== Starting Testing ==============")# load the saved model for evaluationparam_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")# load parameter to the networkload_param_into_net(network, param_dict)# load testing datasetds_eval = create_dataset(os.path.join(mnist_path, "test"))acc = model.eval(ds_eval, dataset_sink_mode=False)print("============== Accuracy:{} ==============".format(acc))if __name__ == "__main__":parser = argparse.ArgumentParser(description='MindSpore LeNet Example')parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],help='device where the code will be implemented (default: CPU)')args = parser.parse_args()context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,enable_mem_reuse=False)# download mnist dataset# download_dataset()# learning rate settinglr = 0.01momentum = 0.9epoch_size = 1mnist_path = "./MNIST_Data"# define the loss functionnet_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')repeat_size = epoch_size# create the networknetwork = LeNet5()# define the optimizernet_opt = nn.Momentum(network.trainable_params(), lr, momentum)config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)# save the network model and parameters for subsequence fine-tuningckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)# group layers into an object with training and evaluation featuresmodel = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)test_net(args, network, model, mnist_path)

大部分都是官方内容。

注意：官方文件的内容，下载数据集部分不能错。配置路径等，剩下所有的设置都不需要，只需要直接运行。