TimeseriesGenerator
Inherits From: Sequence
Defined in tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
.
Utility class for generating batches of temporal data.
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.
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, end_index: Data points earlier than start_index
or 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).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.
__getitem__
__getitem__(index)
Gets batch at position index
.
index
: position of the batch in the Sequence.A batch
__iter__
__iter__()
Creates an infinite generator that iterate over the Sequence.
Sequence items.
__len__
__len__()
Number of batch in the Sequence.
The number of batches in the Sequence.
on_epoch_end
on_epoch_end()
Method called at the end of every epoch.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/TimeseriesGenerator