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 



ndimensional array containing the input sequences for the model 


ndimensional array containing the target sequences for the model 
hyperparameters 



indicator of whether this is a classification or regression model. Default is False 


number of epochs to train the model. An epoch is an iteration over the entire X and y data provided. Default is 35 


list of callbacks to apply during training 


float between 0 and 1. Fraction of the training data to be used as validation data. Default is 0.2 


number of samples per gradient update. Default is 64 


tuple denoting the shape of an input sample 


number of values ahead to predict (target size). Default is 1. 


string (name of optimizer) or optimizer instance. Default is 


string (name of the objective function) or an objective function instance. Default is 


list of metrics to be evaluated by the model during training and testing. Default is [“mse”] 


whether to return the last output in the output sequence, or the full sequence. Default is False 


list of keras layers which are the basic building blocks of a neural network 


verbosity mode. Default is False 


dimensionality of the output space for the first LSTM layer. Default is 80 


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 


dimensionality of the output space for the second LSTM layer. Default is 80 


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 



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([[0.99832773]])