danieljoserodriguez

# Daniel J. Rodriguez
# https://github.com/danieljoserodriguez

import numpy as np


# A straight line function where activation is proportional to input
# ( which is the weighted sum from neuron ).

# In mathematics, an identity function, also called an identity relation or

danieljoserodriguez

# Daniel J. Rodriguez
# https://github.com/danieljoserodriguez

# This Gist is for information purposes only to demonstrate how to perform the task at hand. 
# I do not advise using this in a production environment - rather - for learning on your own

# multiple inputs and layers neural network
import numpy as np

# neuron layers

danieljoserodriguez

# Daniel J. Rodriguez
# https://github.com/danieljoserodriguez

# This Gist is for information purposes only to demonstrate how to perform the task at hand. 
# I do not advise using this in a production environment - rather - for learning on your own.

# simple multiple inputs neural network
########################################################################
# just python - no extra libraries
def sum_weights(inputs, weights):

danieljoserodriguez

# Daniel J. Rodriguez
# https://github.com/danieljoserodriguez

# This Gist is for information purposes only to demonstrate how to perform the task at hand. 
# I do not advise using this in a production environment - rather - for learning on your own.

# simplest neural network - single input / neuron
neuron_weight = 0.1
test_scores = [99, 75]
neuron_prediction = test_scores[0] * neuron_weight

danieljoserodriguez

# Daniel J. Rodriguez
# https://github.com/danieljoserodriguez
import numpy as np


# Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a 
# probability value between 0 and 1
def cross_entropy(y_hat, y):
    return -np.log(y_hat) if y == 1 else -np.log(1 - y_hat)