dataset = dataset.map(trans_fn)
时间: 2023-11-06 21:07:38 浏览: 57
As an AI language model, I do not have access to the specific context or information about the dataset and trans_fn mentioned in the code snippet. However, in general, the code is using the map() function to apply a transformation function (trans_fn) to each element in the dataset. The resulting dataset would have the same number of elements as the original dataset, but each element would be transformed according to the logic in the trans_fn function.
相关问题
train_dataset = LegacyPPIDataset(mode='train') n_classes = train_dataset._labels.shape[1] num_feats = train_dataset.features.shape[1]
根据代码,'LegacyPPIDataset'对象应该已经正确创建了,并且访问'features'属性是必须的,因此第一个问题可以排除。
第二个问题可能是代码中出现了其他名称为'train_dataset'的对象并且具有'features'属性,导致代码访问到了错误的对象。建议检查一下代码中是否存在类似以下的情况:
```
train_dataset = ...
...
train_dataset = another_dataset(...)
...
n_classes = train_dataset._labels.shape[1]
num_feats = train_dataset.features.shape[1]
```
如果存在这种情况,可以将第二个'='号左边的变量名修改为其他名称,避免名称冲突。
最后,如果以上两种情况都不是问题所在,需要检查一下'LegacyPPIDataset'类的代码,确认是否对'features'属性进行了修改或删除,导致无法访问。如果是这种情况,可以修改'LegacyPPIDataset'类的代码,确保'features'属性正确存在。
example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0])如何将该段代码修改为使用本地的fashion_mnist数据
集呢?
您可以尝试使用以下代码将 `example_dataset` 进行修改以使用本地的 fashion_mnist 数据集:
```
import tensorflow_federated as tff
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
def preprocess(dataset):
def batch_format_fn(element):
return (tf.reshape(element['pixels'],[-1,784]),tf.reshape(element['label'],[-1,1]))
return dataset.repeat(NUM_EPOCHS).map(batch_format_fn).shuffle(SHUFFLE_BUFFER).batch(BATCH_SIZE)
def make_federated_data(client_data, client_ids):
return [preprocess(client_data.create_tf_dataset_for_client(client_id)) for client_id in client_ids]
emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
fashion_mnist_train, fashion_mnist_test = fashion_mnist.load_data()
fashion_mnist_train_client = tff.simulation.ClientData.from_tensor_slices(
{'pixels': train_images, 'label': train_labels})
fashion_mnist_test_client = tff.simulation.ClientData.from_tensor_slices(
{'pixels': test_images, 'label': test_labels})
federated_train_data_fashion_mnist = make_federated_data(fashion_mnist_train_client,
fashion_mnist_train_client.client_ids)
example_dataset = federated_train_data_fashion_mnist[0]
```
这段代码将使用 `tff.simulation.datasets.emnist.load_data()` 加载 EMNIST 数据集,并使用 `fashion_mnist.load_data()` 加载 fashion_mnist 数据集。然后,它将为 `fashion_mnist` 创建 `ClientData` 对象,然后将 `ClientData` 对象转换为可以在 TFF 训练环境中使用的联合数据集。最后,它将 `example_dataset` 赋值给第一个 "fashion_mnist" 客户端的数据集。