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emnist.py
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emnist.py
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# Copyright 2019, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Libraries for the federated EMNIST dataset for simulation."""
import collections
import tensorflow as tf
from tensorflow_federated.python.simulation.datasets import download
from tensorflow_federated.python.simulation.datasets import from_tensor_slices_client_data
from tensorflow_federated.python.simulation.datasets import sql_client_data
def _add_proto_parsing(dataset: tf.data.Dataset) -> tf.data.Dataset:
"""Add parsing of the tf.Example proto to the dataset pipeline."""
def parse_proto(tensor_proto):
parse_spec = {
'pixels': tf.io.FixedLenFeature(shape=(28, 28), dtype=tf.float32),
'label': tf.io.FixedLenFeature(shape=(), dtype=tf.int64)
}
parsed_features = tf.io.parse_example(tensor_proto, parse_spec)
return collections.OrderedDict(
label=tf.cast(parsed_features['label'], tf.int32),
pixels=parsed_features['pixels'])
return dataset.map(parse_proto, num_parallel_calls=tf.data.AUTOTUNE)
def load_data(only_digits=True, cache_dir=None):
"""Loads the Federated EMNIST dataset.
Downloads and caches the dataset locally. If previously downloaded, tries to
load the dataset from cache.
This dataset is derived from the Leaf repository
(https://github.com/TalwalkarLab/leaf) pre-processing of the Extended MNIST
dataset, grouping examples by writer. Details about Leaf were published in
"LEAF: A Benchmark for Federated Settings" https://arxiv.org/abs/1812.01097.
*Note*: This dataset does not include some additional preprocessing that
MNIST includes, such as size-normalization and centering.
In the Federated EMNIST data, the value of 1.0
corresponds to the background, and 0.0 corresponds to the color of the digits
themselves; this is the *inverse* of some MNIST representations,
e.g. in [tensorflow_datasets]
(https://github.com/tensorflow/datasets/blob/master/docs/datasets.md#mnist),
where 0 corresponds to the background color, and 255 represents the color of
the digit.
Data set sizes:
*only_digits=True*: 3,383 users, 10 label classes
- train: 341,873 examples
- test: 40,832 examples
*only_digits=False*: 3,400 users, 62 label classes
- train: 671,585 examples
- test: 77,483 examples
Rather than holding out specific users, each user's examples are split across
_train_ and _test_ so that all users have at least one example in _train_ and
one example in _test_. Writers that had less than 2 examples are excluded from
the data set.
The `tf.data.Datasets` returned by
`tff.simulation.datasets.ClientData.create_tf_dataset_for_client` will yield
`collections.OrderedDict` objects at each iteration, with the following keys
and values, in lexicographic order by key:
- `'label'`: a `tf.Tensor` with `dtype=tf.int32` and shape [1], the class
label of the corresponding pixels. Labels [0-9] correspond to the digits
classes, labels [10-35] correspond to the uppercase classes (e.g., label
11 is 'B'), and labels [36-61] correspond to the lowercase classes
(e.g., label 37 is 'b').
- `'pixels'`: a `tf.Tensor` with `dtype=tf.float32` and shape [28, 28],
containing the pixels of the handwritten digit, with values in
the range [0.0, 1.0].
Args:
only_digits: (Optional) whether to only include examples that are from the
digits [0-9] classes. If `False`, includes lower and upper case
characters, for a total of 62 class labels.
cache_dir: (Optional) directory to cache the downloaded file. If `None`,
caches in Keras' default cache directory.
Returns:
Tuple of (train, test) where the tuple elements are
`tff.simulation.datasets.ClientData` objects.
"""
database_path = download.get_compressed_file(
origin='https://storage.googleapis.com/tff-datasets-public/emnist_all.sqlite.lzma',
cache_dir=cache_dir)
if only_digits:
train_client_data = sql_client_data.SqlClientData(
database_path, 'digits_only_train').preprocess(_add_proto_parsing)
test_client_data = sql_client_data.SqlClientData(
database_path, 'digits_only_test').preprocess(_add_proto_parsing)
else:
train_client_data = sql_client_data.SqlClientData(
database_path, 'all_train').preprocess(_add_proto_parsing)
test_client_data = sql_client_data.SqlClientData(
database_path, 'all_test').preprocess(_add_proto_parsing)
return train_client_data, test_client_data
def get_synthetic():
"""Returns a small synthetic dataset for testing.
The two clients produced have exactly 10 examples apiece, one example for each
digit label. The images are derived from a fixed set of hard-coded images.
Returns:
A `tff.simulation.datasets.ClientData` object that matches the
characteristics (other than size) of those provided by
`tff.simulation.datasets.emnist.load_data`.
"""
return from_tensor_slices_client_data.TestClientData({
'synthetic1': _get_synthetic_digits_data(),
'synthetic2': _get_synthetic_digits_data()
})
def _get_synthetic_digits_data():
"""Returns a dictionary suitable for `tf.data.Dataset.from_tensor_slices`.
Returns:
A dictionary that matches the structure of the data produced by
`tff.simulation.datasets.emnist.load_data`, with keys (in lexicographic
order) `label` and `pixels`.
"""
data = _SYNTHETIC_DIGITS_DATA
img_list = []
for img_array in data:
img_array = tf.constant(img_array, dtype=tf.float32) / 9.0
img_list.append(img_array)
pixels = tf.stack(img_list, axis=0)
labels = tf.constant(range(10), dtype=tf.int32)
return collections.OrderedDict(label=labels, pixels=pixels)
# pyformat: disable
# pylint: disable=bad-continuation,bad-whitespace
_SYNTHETIC_DIGITS_DATA = [
[[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,7,4,4,4,4,4,7,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,4,0,0,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,2,0,2,4,2,0,2,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,2,0,2,4,2,0,2,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,4,0,0,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,7,4,4,4,4,4,7,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9]],
[[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,7,4,4,4,7,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,2,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,0,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,2,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,2,0,0,0,2,4,4,4,7,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,0,0,0,0,0,0,0,0,4,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,4,4,4,4,4,4,4,4,7,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9]],
[[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,7,4,4,4,4,4,7,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,4,0,0,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,2,0,2,4,2,0,2,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,7,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,7,4,2,0,2,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,7,4,2,0,2,4,7,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,2,0,2,4,4,4,4,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,4,9,4,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,2,4,2,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,4,0,0,0,0,0,0,0,0,0,4,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,7,4,4,4,4,4,4,4,4,4,7,9,9,9,9,9,9,9,9,9],
[9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9],
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