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Source code for nets.data.iterator

"""
This modules defines how the data should be called from a datasets.
This takes into account ``shuffle`` mode.
"""

import numpy as np
import nets
from .batch import Batch


[docs]class Iterator(object): r""" An ``Iterator`` call the data in batches. These batches can be shuffled and normalized. Usually, you want to feed a model with these batches. """ def __init__(self, dataset, batch_size=32, shuffle=False): self.dataset = dataset self.batch_size = batch_size self.shuffle = shuffle def __iter__(self): starts = np.arange(0, len(self.dataset), self.batch_size) if self.shuffle: np.random.shuffle(list(self.dataset)) for start in starts: end = start + self.batch_size batch_size = min(end, len(self.dataset)) - start yield Batch(self.dataset[start:end], batch_size) def __len__(self): return len(self.dataset) // self.batch_size

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