View source on GitHub
|
Utility class for generating batches of temporal data.
Inherits From: Sequence
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
get_config()
Returns the TimeseriesGenerator configuration as Python dictionary.
| Returns | |
|---|---|
| A Python dictionary with the TimeseriesGenerator configuration. |
on_epoch_end
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. |
__getitem__
__getitem__(
index
)
__iter__
__iter__()
Create a generator that iterate over the Sequence.
__len__
__len__()