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Added activation functions #968

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Merged
merged 1 commit into from
Oct 4, 2018
Merged

Added activation functions #968

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23 changes: 20 additions & 3 deletions learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,8 @@
removeall, unique, product, mode, argmax, argmax_random_tie, isclose, gaussian,
dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement,
weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table,
open_data, sigmoid_derivative, probability, norm, matrix_multiplication, relu, relu_derivative
open_data, sigmoid_derivative, probability, norm, matrix_multiplication, relu, relu_derivative,
tanh, tanh_derivative, leaky_relu, leaky_relu_derivative, elu, elu_derivative
)

import copy
Expand Down Expand Up @@ -746,8 +747,15 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs, activation=sigmo
# The activation function used is relu or sigmoid function
if node.activation == sigmoid:
delta[-1] = [sigmoid_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
else:
elif node.activation == relu:
delta[-1] = [relu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
elif node.activation == tanh:
delta[-1] = [tanh_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
elif node.activation == elu:
delta[-1] = [elu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]
else:
delta[-1] = [leaky_relu_derivative(o_nodes[i].value) * err[i] for i in range(o_units)]


# Backward pass
h_layers = n_layers - 2
Expand All @@ -762,9 +770,18 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs, activation=sigmo
if activation == sigmoid:
delta[i] = [sigmoid_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
else:
elif activation == relu:
delta[i] = [relu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
elif activation == tanh:
delta[i] = [tanh_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
elif activation == elu:
delta[i] = [elu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]
else:
delta[i] = [leaky_relu_derivative(layer[j].value) * dotproduct(w[j], delta[i+1])
for j in range(h_units)]

# Update weights
for i in range(1, n_layers):
Expand Down
41 changes: 40 additions & 1 deletion utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
import random
import math
import functools
import numpy as np
from itertools import chain, combinations


Expand Down Expand Up @@ -273,9 +274,47 @@ def sigmoid(x):
"""Return activation value of x with sigmoid function"""
return 1 / (1 + math.exp(-x))



def relu_derivative(value):
if value > 0:
return 1
else:
return 0

def elu(x, alpha=0.01):
if x > 0:
return x
else:
return alpha * (math.exp(x) - 1)

def elu_derivative(value, alpha = 0.01):
if value > 0:
return 1
else:
return alpha * math.exp(value)

def tanh(x):
return np.tanh(x)

def tanh_derivative(value):
return (1 - (value ** 2))

def leaky_relu(x, alpha = 0.01):
if x > 0:
return x
else:
return alpha * x

def leaky_relu_derivative(value, alpha=0.01):
if value > 0:
return 1
else:
return alpha

def relu(x):
return max(0, x)

def relu_derivative(value):
if value > 0:
return 1
Expand Down