tf.keras.preprocessing.sequence.TimeseriesGenerator

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Utility class for generating batches of temporal data.

Inherits From: Sequence

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tf.compat.v1.keras.preprocessing.sequence.TimeseriesGenerator

tf.keras.preprocessing.sequence.TimeseriesGenerator(
    data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None,
    shuffle=False, reverse=False, batch_size=128
)

This class takes in a sequence of data-points gathered at equal intervals, along with time series parameters such as stride, length of history, etc., to produce batches for training/validation. Arguments: data: Indexable generator (such as list or Numpy array) containing consecutive data points (timesteps). The data should be at 2D, and axis 0 is expected to be the time dimension. targets: Targets corresponding to timesteps in data. It should have same length as data. length: Length of the output sequences (in number of timesteps). sampling_rate: Period between successive individual timesteps within sequences. For rate r, timesteps data[i], data[i-r], ... data[i - length] are used for create a sample sequence. stride: Period between successive output sequences. For stride s, consecutive output samples would be centered around data[i], data[i+s], data[i+2*s], etc. start_index: Data points earlier than start_index will not be used in the output sequences. This is useful to reserve part of the data for test or validation. end_index: Data points later than end_index will not be used in the output sequences. This is useful to reserve part of the data for test or validation. shuffle: Whether to shuffle output samples, or instead draw them in chronological order. reverse: Boolean: if true, timesteps in each output sample will be in reverse chronological order. batch_size: Number of timeseries samples in each batch (except maybe the last one). Returns: A Sequence instance. Examples:

from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = TimeseriesGenerator(data, targets,
                               length=10, sampling_rate=2,
                               batch_size=2)
assert len(data_gen) == 20
batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
                      np.array([[[0], [2], [4], [6], [8]],
                                [[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
                      np.array([[10], [11]]))

Methods

get_config

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get_config()

Returns the TimeseriesGenerator configuration as Python dictionary.

Returns
A Python dictionary with the TimeseriesGenerator configuration.

on_epoch_end

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on_epoch_end()

Method called at the end of every epoch.

to_json

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to_json(
    **kwargs
)

Returns a JSON string containing the timeseries generator configuration. To load a generator from a JSON string, use keras.preprocessing.sequence.timeseries_generator_from_json(json_string).

Arguments
**kwargs	Additional keyword arguments to be passed to json.dumps().

Returns
A JSON string containing the tokenizer configuration.

__getitem__

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__getitem__(
    index
)

__iter__

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__iter__()

Create a generator that iterate over the Sequence.

__len__

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__len__()