把batch_size = 128 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)修改为调用cifar-10
时间: 2023-08-31 08:40:51 浏览: 178
data_batch_5_data_batch5_cifar10_data_batch_0_batch_
### 回答1:
好的,可以使用以下代码调用cifar-10数据集:
```python
import d2l
import torch
batch_size = 128
resize = 32
# 加载 CIFAR-10 数据集
train_iter, test_iter = d2l.load_data_cifar10(batch_size, resize)
```
这里,我们使用了 `d2l.load_data_cifar10` 函数来加载 CIFAR-10 数据集,并将其分成了训练集和测试集。我们还将图像大小调整为 $32\times 32$,并使用了批量大小为 $128$。
### 回答2:
要将代码中的`d2l.load_data_fashion_mnist`修改为加载 CIFAR-10 数据集,我们可以使用相同的 `d2l` 模块中的 `load_data_cifar10` 函数。我们只需要将 `batch_size` 参数设置为 128 并加载数据时指定为图像大小为 224。下面是修改后的代码:
```python
import d2l
batch_size = 128
train_iter, test_iter = d2l.load_data_cifar10(batch_size, resize=224)
```
这样,我们就可以使用 CIFAR-10 数据集进行训练和测试了。
### 回答3:
要将batch_size = 128 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)修改为调用cifar-10数据集,可以按照以下步骤进行修改:
首先,我们需要导入相关的包:
```
import d2l
from mxnet import gluon, autograd, init, nd
from mxnet.gluon import nn, data as gdata, loss as gloss
```
然后,我们可以定义一个函数来加载cifar-10数据集:
```
def load_data_cifar10(batch_size, resize=None):
"""Download the CIFAR-10 dataset and then load it into memory."""
transformer = []
if resize:
transformer += [gdata.vision.transforms.Resize(resize)]
transformer += [gdata.vision.transforms.ToTensor()]
transformer = gdata.vision.transforms.Compose(transformer)
mnist_train = gdata.vision.CIFAR10(train=True).transform_first(transformer)
mnist_test = gdata.vision.CIFAR10(train=False).transform_first(transformer)
return (gdata.DataLoader(mnist_train, batch_size, shuffle=True),
gdata.DataLoader(mnist_test, batch_size, shuffle=False))
```
接下来,我们可以使用该函数来加载cifar-10数据集:
```
batch_size = 128
train_iter, test_iter = load_data_cifar10(batch_size, resize=224)
```
以上就是将batch_size = 128 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)修改为调用cifar-10的方法。这样就可以加载cifar-10数据集并设置合适的batch_size和图片大小。
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