|TensorFlow 1 version||View source on GitHub|
Utility class for generating batches of temporal data.
Compat aliases for migration
See Migration guide for more details.
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.
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).
A [Sequence](/utils/#sequence) instance.
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 x, y = batch_0 assert np.array_equal(x, np.array([[, , , , ], [, , , , ]])) assert np.array_equal(y, np.array([, ]))
__getitem__( index )
Create a generator that iterate over the Sequence.
Returns the TimeseriesGenerator configuration as Python dictionary.
A Python dictionary with the TimeseriesGenerator configuration.
Method called at the end of every epoch.
to_json( **kwargs )
Returns a JSON string containing the timeseries generator
configuration. To load a generator from a JSON string, use
**kwargs: Additional keyword arguments to be passed to `json.dumps()`.
A JSON string containing the tokenizer configuration.