LSTM¶

path: keras.Sequential.LSTMTimeSeriesRegressor

description: this is a prediction model with double stacked LSTM layers used as a time series regressor. you can read more about it in the related paper.

see json.

argument	type	description
parameters
`X`	`numpy.ndarray`	n-dimensional array containing the input sequences for the model
`y`	`numpy.ndarray`	n-dimensional array containing the target sequences for the model
hyperparameters
`classification`	`bool`	indicator of whether this is a classification or regression model. Default is False
`epochs`	`int`	number of epochs to train the model. An epoch is an iteration over the entire X and y data provided. Default is 35
`callbacks`	`list`	list of callbacks to apply during training
`validation_split`	`float`	float between 0 and 1. Fraction of the training data to be used as validation data. Default is 0.2
`batch_size`	`int`	number of samples per gradient update. Default is 64
`input_shape`	`tuple`	tuple denoting the shape of an input sample
`dense_units`	`int`	number of values ahead to predict (target size). Default is 1.
`optimizer`	`str`	string (name of optimizer) or optimizer instance. Default is `keras.optimizers.Adam`
`loss`	`str`	string (name of the objective function) or an objective function instance. Default is `keras.losses.mean_squared_error`
`metrics`	`list`	list of metrics to be evaluated by the model during training and testing. Default is [“mse”]
`return_seqeunces`	`bool`	whether to return the last output in the output sequence, or the full sequence. Default is False
`layers`	`list`	list of keras layers which are the basic building blocks of a neural network
`verbose`	`bool`	verbosity mode. Default is False
`lstm_1_unit`	`int`	dimensionality of the output space for the first LSTM layer. Default is 80
`dropout_1_rate`	`float`	float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs for the first LSTM layer. Default: 0.3
`lstm_2_unit`	`int`	dimensionality of the output space for the second LSTM layer. Default is 80
`dropout_2_rate`	`float`	float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs for the second LSTM layer. Default: 0.3
output
`y`	`numpy.ndarray`	predicted values

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: X = np.array([1] * 100).reshape(1, -1, 1)

In [4]: y = np.array([[1]])

In [5]: primitive = load_primitive('keras.Sequential.LSTMTimeSeriesRegressor',
   ...:     arguments={"X": X, "y": y, "input_shape":(100, 1), "batch_size": 1, "validation_split": 0})
   ...: 

In [6]: primitive.fit()

In [7]: primitive.produce(X=X)
Out[7]: array([[0.83856668]])

ARIMA LSTM AE