如题所述，官网地址：
https://www.mindspore.cn/tutorial/zh-CN/r1.2/quick_start.html
数据集下载：
mkdir -p ./datasets/MNIST_Data/train ./datasets/MNIST_Data/test wget -NP ./datasets/MNIST_Data/train https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-labels-idx1-ubyte wget -NP ./datasets/MNIST_Data/train https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-images-idx3-ubyte wget -NP ./datasets/MNIST_Data/test https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/t10k-labels-idx1-ubyte wget -NP ./datasets/MNIST_Data/test https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/t10k-images-idx3-ubyte tree ./datasets/MNIST_Data
个人整合后的代码：
#!/usr/bin python # encoding:UTF-8 """" 对输入的超参数进行处理 """ import os import argparse """ 设置运行的背景context """ from mindspore import context """ 对数据集进行预处理 """ import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.vision.c_transforms as CV from mindspore.dataset.vision import Inter from mindspore import dtype as mstype """ 构建神经网络 """ import mindspore.nn as nn from mindspore.common.initializer import Normal """ 训练时对模型参数的保存 """ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig """ 导入模型训练需要的库 """ from mindspore.nn import Accuracy from mindspore.train.callback import LossMonitor from mindspore import Model parser = argparse.ArgumentParser(description='MindSpore LeNet Example') parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU']) args = parser.parse_known_args()[0] # 为mindspore设置运行背景context context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): # 定义数据集 mnist_ds = ds.MnistDataset(data_path) resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081 # 定义所需要操作的map映射 resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32) # 使用map映射函数，将数据操作应用到数据集 mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) # 进行shuffle、batch、repeat操作 buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.repeat(repeat_size) return mnist_ds class LeNet5(nn.Cell): """ Lenet网络结构 """ def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() # 定义所需要的运算 self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() def construct(self, x): # 使用定义好的运算构建前向网络 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 x # 实例化网络 net = LeNet5() # 定义损失函数 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # 定义优化器 net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9) # 设置模型保存参数 # 每125steps保存一次模型参数，最多保留15个文件 config_ck = CheckpointConfig(save_checkpoint_steps=125, keep_checkpoint_max=15) # 应用模型保存参数 ckpoint = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) def train_net(args, model, epoch_size, data_path, repeat_size, ckpoint_cb, sink_mode): """定义训练的方法""" # 加载训练数据集 ds_train = create_dataset(os.path.join(data_path, "train"), 32, repeat_size) model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(125)], dataset_sink_mode=sink_mode) def test_net(network, model, data_path): """定义验证的方法""" ds_eval = create_dataset(os.path.join(data_path, "test")) acc = model.eval(ds_eval, dataset_sink_mode=False) print("{}".format(acc)) mnist_path = "./datasets/MNIST_Data" train_epoch = 1 dataset_size = 1 model = Model(net, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) train_net(args, model, train_epoch, mnist_path, dataset_size, ckpoint, False) test_net(net, model, mnist_path)
训练结果：
epoch: 1 step: 125, loss is 2.2982173 epoch: 1 step: 250, loss is 2.296105 epoch: 1 step: 375, loss is 2.3065567 epoch: 1 step: 500, loss is 2.3062077 epoch: 1 step: 625, loss is 2.3096561 epoch: 1 step: 750, loss is 2.2847052 epoch: 1 step: 875, loss is 2.284628 epoch: 1 step: 1000, loss is 1.8122461 epoch: 1 step: 1125, loss is 0.4140602 epoch: 1 step: 1250, loss is 0.25238502 epoch: 1 step: 1375, loss is 0.17819008 epoch: 1 step: 1500, loss is 0.3202765 epoch: 1 step: 1625, loss is 0.12312577 epoch: 1 step: 1750, loss is 0.11027573 epoch: 1 step: 1875, loss is 0.2680659 {'Accuracy': 0.9598357371794872}
为网络导入模型参数，并进行预测：
本步骤与上面的训练步骤相关，需要前面设置好的数据集，并且需要前面已经训练好的网络参数。
import os import numpy as np """ 构建神经网络 """ import mindspore.nn as nn from mindspore.common.initializer import Normal from mindspore import Tensor # 导入模型参数 from mindspore.train.serialization import load_checkpoint, load_param_into_net """ 对数据集进行预处理 """ import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.dataset.vision.c_transforms as CV from mindspore.dataset.vision import Inter from mindspore import dtype as mstype """ 导入模型训练需要的库 """ from mindspore.nn import Accuracy from mindspore import Model def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): # 定义数据集 mnist_ds = ds.MnistDataset(data_path) resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081 # 定义所需要操作的map映射 resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32) # 使用map映射函数，将数据操作应用到数据集 mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) # 进行shuffle、batch、repeat操作 buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.repeat(repeat_size) return mnist_ds class LeNet5(nn.Cell): """ Lenet网络结构 """ def __init__(self, num_class=10, num_channel=1): super(LeNet5, self).__init__() # 定义所需要的运算 self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.flatten = nn.Flatten() def construct(self, x): # 使用定义好的运算构建前向网络 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 x # 实例化网络 net = LeNet5() # 定义损失函数 net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # 定义优化器 net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9) # 构建模型 model = Model(net, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) # 加载已经保存的用于测试的模型 param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt") # 加载参数到网络中 load_param_into_net(net, param_dict) _batch_size = 8 # 定义测试数据集，batch_size设置为1，则取出一张图片 mnist_path = "./datasets/MNIST_Data" ds_test = create_dataset(os.path.join(mnist_path, "test"), batch_size=_batch_size).create_dict_iterator() data = next(ds_test) # images为测试图片，labels为测试图片的实际分类 images = data["image"].asnumpy() labels = data["label"].asnumpy() # 使用函数model.predict预测image对应分类 output = model.predict(Tensor(data['image'])) predicted = np.argmax(output.asnumpy(), axis=1) # 输出预测分类与实际分类 for i in range(_batch_size): print(f'Predicted: "{predicted[i]}", Actual: "{labels[i]}"')
运行结果：
