diff --git a/learning.py b/learning.py
index db25c42f3..427c15d8a 100644
--- a/learning.py
+++ b/learning.py
@@ -434,11 +434,11 @@ def predict(example):
def NeuralNetLearner(dataset, hidden_layer_sizes=[3],
- learning_rate=0.01, epoches=100):
+ learning_rate=0.01, epochs=100):
"""Layered feed-forward network.
hidden_layer_sizes: List of number of hidden units per hidden layer
learning_rate: Learning rate of gradient descent
- epoches: Number of passes over the dataset
+ epochs: Number of passes over the dataset
"""
i_units = len(dataset.inputs)
@@ -447,7 +447,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3],
# construct a network
raw_net = network(i_units, hidden_layer_sizes, o_units)
learned_net = BackPropagationLearner(dataset, raw_net,
- learning_rate, epoches)
+ learning_rate, epochs)
def predict(example):
@@ -510,7 +510,7 @@ def network(input_units, hidden_layer_sizes, output_units):
return net
-def BackPropagationLearner(dataset, net, learning_rate, epoches):
+def BackPropagationLearner(dataset, net, learning_rate, epochs):
"""[Figure 18.23] The back-propagation algorithm for multilayer network"""
# Initialise weights
for layer in net:
@@ -530,7 +530,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
o_nodes = net[-1]
i_nodes = net[0]
- for epoch in range(epoches):
+ for epoch in range(epochs):
# Iterate over each example
for e in examples:
i_val = [e[i] for i in idx_i]
@@ -583,13 +583,13 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
return net
-def PerceptronLearner(dataset, learning_rate=0.01, epoches=100):
+def PerceptronLearner(dataset, learning_rate=0.01, epochs=100):
"""Logistic Regression, NO hidden layer"""
i_units = len(dataset.inputs)
o_units = 1 # As of now, dataset.target gives only one index.
hidden_layer_sizes = []
raw_net = network(i_units, hidden_layer_sizes, o_units)
- learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epoches)
+ learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs)
def predict(example):
# Input nodes
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