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"epoches" to "epochs" #336

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Merged
merged 2 commits into from
Mar 18, 2017
Merged

"epoches" to "epochs" #336

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87a3e9a
"epoches" to "epochs"
antmarakis Mar 8, 2017
c487975
Merge branch 'master' into patch-1
norvig Mar 18, 2017
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14 changes: 7 additions & 7 deletions learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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):

Expand Down Expand Up @@ -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:
Expand All @@ -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]
Expand Down Expand Up @@ -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
Expand Down