From 87a3e9a0244469dec92d01e9e5e61c8f40a2deaf Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 8 Mar 2017 21:12:01 +0200 Subject: [PATCH] "epoches" to "epochs" --- learning.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/learning.py b/learning.py index df5d6fce3..8345c1b58 100644 --- a/learning.py +++ b/learning.py @@ -438,12 +438,12 @@ 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) @@ -452,7 +452,7 @@ 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): @@ -517,7 +517,7 @@ 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: @@ -537,7 +537,7 @@ 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] @@ -590,13 +590,13 @@ 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 pFad - Phonifier reborn

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