tf.keras.preprocessing.sequence.TimeseriesGenerator

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Class TimeseriesGenerator

Utility class for generating batches of temporal data.

Inherits From: Sequence

Aliases:

  • Class tf.compat.v1.keras.preprocessing.sequence.TimeseriesGenerator
  • Class tf.compat.v2.keras.preprocessing.sequence.TimeseriesGenerator

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](/utils/#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]]))

__init__

__init__(
    data,
    targets,
    length,
    sampling_rate=1,
    stride=1,
    start_index=0,
    end_index=None,
    shuffle=False,
    reverse=False,
    batch_size=128
)

Initialize self. See help(type(self)) for accurate signature.

Methods

__getitem__

__getitem__(index)

__iter__

View source

__iter__()

Create a generator that iterate over the Sequence.

__len__

__len__()

get_config

get_config()

Returns the TimeseriesGenerator configuration as Python dictionary.

Returns

A Python dictionary with the TimeseriesGenerator configuration.

on_epoch_end

View source

on_epoch_end()

Method called at the end of every epoch.

to_json

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.

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