3

吹代码中如何保存模型

如果要运行代码，请访问https://github.com/tensorflow/federated 并下载 federated_learning_for_image_classification.ipynb。

如果您告诉我如何在教程 federated_learning_for_image_classification.ipynb 中保存联邦学习模型，我将不胜感激。



from __future__ import absolute_import, division, print_function
import tensorflow_federated as tff
from matplotlib import pyplot as plt
import tensorflow as tf
import six
import numpy as np
from six.moves import range
import warnings
import collections
import nest_asyncio
import h5py_character
from tensorflow.keras import layers
nest_asyncio.apply()
warnings.simplefilter('ignore')
tf.compat.v1.enable_v2_behavior()
np.random.seed(0)


NUM_CLIENTS = 1
NUM_EPOCHS = 1
BATCH_SIZE = 20
SHUFFLE_BUFFER = 500
num_classes = 3755

if six.PY3:
    tff.framework.set_default_executor(
        tff.framework.create_local_executor(NUM_CLIENTS))  


data_train = h5py_character.load_characters_data()

print(len(data_train.client_ids))

example_dataset = data_train.create_tf_dataset_for_client(
    data_train.client_ids[0])


def preprocess(dataset):
    def element_fn(element):
        # element['data'] = tf.expand_dims(element['data'], axis=-1)
        return collections.OrderedDict([
            # ('x', tf.reshape(element['data'], [-1])),
            ('x', tf.reshape(element['data'], [64, 64, 1])),
            ('y', tf.reshape(element['label'], [1])),
        ])

    return dataset.repeat(NUM_EPOCHS).map(element_fn).shuffle(
        SHUFFLE_BUFFER).batch(BATCH_SIZE)


preprocessed_example_dataset = preprocess(example_dataset)  
print(iter(preprocessed_example_dataset).next())


sample_batch = tf.nest.map_structure(
    lambda x: x.numpy(), iter(preprocessed_example_dataset).next())



def make_federated_data(client_data, client_ids):
    return [preprocess(client_data.create_tf_dataset_for_client(x))
            for x in client_ids]


sample_clients = data_train.client_ids[0:NUM_CLIENTS]

federated_train_data = make_federated_data(data_train, sample_clients)




def create_compiled_keras_model():

    model = tf.keras.Sequential([
        layers.Conv2D(input_shape=(64, 64, 1), filters=64, kernel_size=(3, 3), strides=(1, 1),
                      padding='same', activation='relu'),
        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
        layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
        layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
        layers.MaxPool2D(pool_size=(2, 2), padding='same'),

        layers.Flatten(),
        layers.Dense(1024, activation='relu'),
        layers.Dense(3755, activation='softmax')
    ])

    model.compile(
        optimizer=tf.keras.optimizers.Adam(),
        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
        # metrics=['accuracy'])
        metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])



    return model


def model_fn():
    keras_model = create_compiled_keras_model()
    global model_to_save
    model_to_save = keras_model
    print(keras_model.summary())
    return tff.learning.from_compiled_keras_model(keras_model, sample_batch)


iterative_process = tff.learning.build_federated_averaging_process(model_fn)


state = iterative_process.initialize()

state, metrics = iterative_process.next(state, federated_train_data)

print('round  1, metrics={}'.format(metrics))

for round_num in range(2, 110):
    state, metrics = iterative_process.next(state, federated_train_data)
    print('round {:2d}, metrics={}'.format(round_num, metrics))
4

3 回答 3

1

粗略地说，我们将在这里使用对象及其save_checkpoint/load_checkpoint方法。特别是，您可以实例化 a FileCheckpointManager，并要求它state（几乎）直接保存。

state在您的示例中是tff.python.common_libs.anonymous_tuple.AnonymousTuple(IIRC) 的一个实例，它与 不兼容tf.convert_to_tensor，正如其文档字符串所需要save_checkpoint和声明的那样。TFF 研究代码中经常使用的通用解决方案是引入一个 Pythonattr的类，以便在返回状态后立即从匿名元组转换——参见此处的示例。

假设上述情况，以下草图应该有效：

# state assumed an anonymous tuple, previously created
# N some integer 

ckpt_manager = FileCheckpointManager(...)
ckpt_manager.save_checkpoint(ServerState.from_anon_tuple(state), round_num=N)

要从此检查点恢复，您可以随时调用：

state = iterative_process.initialize()
ckpt_manager = FileCheckpointManager(...)
restored_state = ckpt_manager.load_latest_checkpoint(
    ServerState.from_anon_tuple(state))

需要注意的一点：上面链接的代码指针一般都在tff.python.research...，pip包中不包含；因此，获得它们的首选方法是将代码分叉到您自己的项目中，或者拉下存储库并从源代码构建它。

感谢您对 TFF 的关注！

于 2019-11-10T20:31:20.550 回答
0

FileCheckpointManager您可以使用该类

https://github.com/tensorflow/federated/blob/master/tensorflow_federated/python/simulation/checkpoint_manager.py

但是，TFF 的已发布版本 (v0.18.0) 不支持此类。您应该将此文件复制到您的项目目录，以便您可以导入FileCheckpointManager.


'''
# PASTE YOUR CODE BEFORE HERE

# Required:
iterative_process = tff.learning.build_federated_averaging_process(model_fn)
state = iterative_process.initialize()
'''

from checkpoint_manager import FileCheckpointManager

fcm = FileCheckpointManager('checkpoint/')

# Save model

round_num = 110 # It depends on rounds you have trained
fcm.save_checkpoint(state, round_num)

# Load model

state, round_num = fcm.load_latest_checkpoint(state)
state, metrics = iterative_process.next(state, federated_train_data)
于 2021-03-28T06:34:39.167 回答
0

model.save_weights 不适用于这个问题吗？我知道 FileCheckpointManager 会做一个更完整的工作（每轮捕获权重），但我猜就最终的联合平均模型而言，参数空间应该在 save_weights 中可用。

于 2020-03-03T12:58:35.407 回答