mindspore resnet50 cifar-10

您的问题是关于MindSpore框架中ResNet50模型在CIFAR-10数据集上的应用。

ResNet50是一个深度卷积神经网络模型，用于图像分类任务。CIFAR-10是一个经典的图像分类数据集，包含10个类别的60000张32x32的彩色图像。

在MindSpore框架中，您可以使用ResNet50模型对CIFAR-10数据集进行训练和测试。首先，您需要准备好CIFAR-10数据集并将其转换为MindSpore支持的数据格式。然后，您可以使用MindSpore提供的ResNet50模型进行训练和测试。

以下是一个示例代码片段，演示如何在MindSpore中使用ResNet50模型对CIFAR-10数据集进行训练和测试：

import mindspore.nn as nn
import mindspore.ops.operations as P
from mindspore import Model
from mindspore import Tensor
from mindspore import context
from mindspore import dataset as ds
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.nn.metrics import Accuracy

# Define the ResNet50 model
class ResNet50(nn.Cell):
    def __init__(self, num_classes=10):
        super(ResNet50, self).__init__()
        self.resnet50 = nn.ResNet50(num_classes=num_classes)

    def construct(self, x):
        x = self.resnet50(x)
        return x

# Load the CIFAR-10 dataset
data_home = "/path/to/cifar-10/"
train_data = ds.Cifar10Dataset(data_home, num_parallel_workers=8, shuffle=True)
test_data = ds.Cifar10Dataset(data_home, num_parallel_workers=8, shuffle=False)

# Define the hyperparameters
learning_rate = 0.1
momentum = 0.9
epoch_size = 200
batch_size = 32

# Define the optimizer
optimizer = nn.Momentum(filter(lambda x: x.requires_grad, resnet50.get_parameters()), learning_rate, momentum)

# Define the loss function
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')

# Define the model
net = ResNet50()

# Define the model checkpoint
config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=10)
ckpt_cb = ModelCheckpoint(prefix="resnet50", directory="./checkpoints/", config=config_ck)

# Define the training dataset
train_data = train_data.batch(batch_size, drop_remainder=True)

# Define the testing dataset
test_data = test_data.batch(batch_size, drop_remainder=True)

# Define the model and train it
model = Model(net, loss_fn=loss_fn, optimizer=optimizer, metrics={"Accuracy": Accuracy()})
model.train(epoch_size, train_data, callbacks=[ckpt_cb, LossMonitor()], dataset_sink_mode=True)

# Load the trained model and test it
param_dict = load_checkpoint("./checkpoints/resnet50-200_1000.ckpt")
load_param_into_net(net, param_dict)
model = Model(net, loss_fn=loss_fn, metrics={"Accuracy": Accuracy()})
result = model.eval(test_data)
print("Accuracy: ", result["Accuracy"])

注意：在上述示例代码中，我们使用的是ResNet50网络的一个变体，该变体适用于CIFAR-10数据集的图像大小。如果您想使用标准的ResNet50网络，您需要调整输入图像的大小。