def __init__(self,
input_dataset,
buffer_size,
seed=None,
reshuffle_each_iteration=None):
"""Randomly shuffles the elements of this dataset."""
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=int64, name="buffer_size")
self._seed, self._seed2 = random_seed.get_seed(seed)
if reshuffle_each_iteration is None:
reshuffle_each_iteration = True
self._reshuffle_each_iteration = reshuffle_each_iteration
variant_tensor = gen_dataset_ops.shuffle_dataset(
input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
seed=self._seed,
seed2=self._seed2,
reshuffle_each_iteration=self._reshuffle_each_iteration,
**self._flat_structure)
super().__init__(input_dataset, variant_tensor)
from tensorflow.python.ops.gen_dataset_ops import shuffle_dataset
sys.path.append(os.pardir) # 부모 디렉터리의 파일을 가져올 수 있도록 설정
import numpy as np
import matplotlib.pyplot as plt
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
# 결과를 빠르게 얻기 위해 훈련 데이터를 줄임
x_train = x_train[:500]
t_train = t_train[:500]
# 20%를 검증 데이터로 분할
validation_rate = 0.20
validation_num = x_train.shape[0] * validation_rate
x_train, t_train = shuffle_dataset(x_train, t_train)
x_val = x_train[:validation_num]
t_val = t_train[:validation_num]
x_train = x_train[validation_num:]
t_train = t_train[validation_num:]
def __train(lr, weight_decay, epocs=50):
network = MultiLayerNet(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100, 100],
output_size=10,
weight_decay_lambda=weight_decay)
trainer = Trainer(network,
x_train,
t_train,
x_val,