使用fashionmnist数据集时，如何修改emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(cache_dir = '/home/cqx/PycharmProjects/cache/fed_emnist_digitsonly') example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0]) example_element = next(iter(example_dataset)) example_element['label'].numpy()

对于fashionmnist数据集，需要修改load_data函数的参数，代码如下：

fashion_train, fashion_test = tff.simulation.datasets.fashion_mnist.load_data(cache_dir='/home/cqx/PycharmProjects/cache/fed_fashion_mnist')
example_dataset = fashion_train.create_tf_dataset_for_client(fashion_train.client_ids[0])
example_element = next(iter(example_dataset))
example_element['label'].numpy()

这样就可以加载fashionmnist数据集，并且得到一个示例元素的标签(label)。

相关问题

import nest_asyncio nest_asyncio.apply() import collections import numpy as np import tensorflow as tf import tensorflow_federated as tff np.random.seed(0) tff.federated_computation(lambda: 'Hello, World!')() emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(cache_dir = '/home/cqx/PycharmProjects/cache/fed_emnist_digitsonly') example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0]) example_element = next(iter(example_dataset)) example_element['label'].numpy()当把数据集换成本地fashion_mnist时，代码该如何修改

如果要将数据集从emnist_train更改为fashion_mnist，则需要对代码进行以下更改：

1. 导入fashion_mnist数据集

from tensorflow.keras.datasets import fashion_mnist

2. 加载fashion_mnist数据集

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

3. 将数据集转换为TFF格式

def preprocess_fn(images, labels):
  images = tf.expand_dims(images, axis=-1)
  images = tf.cast(images, tf.float32) / 255.0
  labels = tf.one_hot(labels, 10)
  return collections.OrderedDict(x=images, y=labels)

def make_federated_data(client_data, client_ids):
  return [client_data.create_tf_dataset_for_client(client_id)
          .map(preprocess_fn)
          .shuffle(500)
          .repeat(NUM_EPOCHS)
          .batch(BATCH_SIZE)
          .prefetch(PREFETCH_BUFFER)
          for client_id in client_ids]

train_data = make_federated_data(fashion_mnist, client_ids)

在替换原始数据集之后，这些更改将确保新数据集与代码一起工作。

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。