Reduced default number of hidden layers to 1
@@ -39,7 +39,7 @@ class Estimator(object):
Please see the tutorial for a detailed walk-through.
"""
- def __init__(self, features=None, n_hidden=(100, 100), activation="tanh"):
+ def __init__(self, features=None, n_hidden=(100,), activation="tanh"):
self.features = features
self.n_hidden = n_hidden
self.activation = activation
@@ -238,7 +238,7 @@ class ParameterizedRatioEstimator(Estimator):
n_hidden : tuple of int, optional
Units in each hidden layer in the neural networks. If method is 'nde' or 'scandal', this refers to the
- setup of each individual MADE layer. Default value: (100, 100).
+ setup of each individual MADE layer. Default value: (100,).
activation : {'tanh', 'sigmoid', 'relu'}, optional
Activation function. Default value: 'tanh'.
@@ -614,7 +614,7 @@ class DoubleParameterizedRatioEstimator(Estimator):
n_hidden : tuple of int, optional
Units in each hidden layer in the neural networks. If method is 'nde' or 'scandal', this refers to the
- setup of each individual MADE layer. Default value: (100, 100).
+ setup of each individual MADE layer. Default value: (100,).
activation : {'tanh', 'sigmoid', 'relu'}, optional
Activation function. Default value: 'tanh'.
@@ -1027,7 +1027,7 @@ class ScoreEstimator(Estimator):
n_hidden : tuple of int, optional
Units in each hidden layer in the neural networks. If method is 'nde' or 'scandal', this refers to the
- setup of each individual MADE layer. Default value: (100, 100).
+ setup of each individual MADE layer. Default value: (100,).
activation : {'tanh', 'sigmoid', 'relu'}, optional
Activation function. Default value: 'tanh'.
@@ -1515,7 +1515,7 @@ class LikelihoodEstimator(Estimator):
n_hidden : tuple of int, optional
Units in each hidden layer in the neural networks. If method is 'nde' or 'scandal', this refers to the
- setup of each individual MADE layer. Default value: (100, 100).
+ setup of each individual MADE layer. Default value: (100,).
activation : {'tanh', 'sigmoid', 'relu'}, optional
Activation function. Default value: 'tanh'.
