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Python_pytorch_ANN.py
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Python_pytorch_ANN.py
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# Python tutorial using pytorch for a Artificial Neural Network (ANN) on a sudo generated dataset.
# An Artificial Neural Network is based on the structure of a biological brain.
# These systems learn to perform tasks or classify based on data, without the need to be programmed specific task rules.
# Python is an interpreted, high-level, general-purpose programming language.
# Pytorch is an high-level machine learning library for python, based on the Torch library.
# Import python libraries
import torch
import torch.nn as nn
# Define the input and target datasets (X & Y)
X = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype=torch.float) # 3 X 2 tensor
y = torch.tensor(([92], [100], [89]), dtype=torch.float) # 3 X 1 tensor
# Define the X input that the model will predict the output
xPredicted = torch.tensor(([4, 8]), dtype=torch.float) # 1 X 2 tensor
# Analyze the X dataset values
print('\nThe X dataset values: \n', X, '\n')
# Analyze the size of the X (input dataset)
print('The Size of X: \n', X.size())
# Analyze the Y dataset values
print('\nthe Y dataset values: \n', y, '\n')
# Analyze the size of the Y (output dataset)
print('the Size of Y: \n', y.size(), '\n')
# Scale and normailze the data
X_max, _ = torch.max(X, 0)
# Analyze the max values for all indexs in the X dataset tensor
print('X_max: \n', X_max, '\n')
# Scale and normailze the data
xPredicted_max, _ = torch.max(xPredicted, 0)
# Analyze the max values for all indexs in the X predicted dataset tensor
print('xPredicted_Max: \n', xPredicted_max, '\n')
# Scale and normailze the data
X = torch.div(X, X_max)
# Analyze the scaled X dataset tensor
print('X_Scaled: \n', X, '\n')
# Scale and normailze the data
xPredicted = torch.div(xPredicted, xPredicted_max)
# Anaylze the scaled X predicted dataset tensor
print('xPredicted_Scaled: \n', xPredicted, '\n')
# Scale and normailze the data
y = y / 100 # max test score is 100
# Analyze the scaled Y dataset tensor
print('Y_Scaled: \n', y, '\n')
# Define the Neural_Network Class
class Neural_Network(nn.Module):
def __init__(self, ):
super(Neural_Network, self).__init__()
# parameters
# TODO: parameters can be parameterized instead of declaring them here
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 3
# weights
self.W1 = torch.randn(self.inputSize, self.hiddenSize) # 3 X 2 tensor
self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor
def forward(self, X):
self.z = torch.matmul(X, self.W1) # 3 X 3 ".dot" does not broadcast in PyTorch
self.z2 = self.sigmoid(self.z) # activation function
self.z3 = torch.matmul(self.z2, self.W2)
o = self.sigmoid(self.z3) # final activation function
return o
def sigmoid(self, s):
return 1 / (1 + torch.exp(-s))
def sigmoidPrime(self, s):
# derivative of sigmoid
return s * (1 - s)
def backward(self, X, y, o):
self.o_error = y - o # error in output
self.o_delta = self.o_error * self.sigmoidPrime(o) # derivative of sig to error
self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))
self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)
self.W1 += torch.matmul(torch.t(X), self.z2_delta)
self.W2 += torch.matmul(torch.t(self.z2), self.o_delta)
def train(self, X, y):
# forward + backward pass for training
o = self.forward(X)
self.backward(X, y, o)
def saveWeights(self, model):
# we will use the PyTorch internal storage functions
torch.save(model, "NN")
# you can reload model with all the weights and so forth with:
# torch.load("NN")
def predict(self):
print ("Predicted data based on trained weights: ")
print ("Input (scaled): \n" + str(xPredicted))
print ("Output: \n" + str(self.forward(xPredicted)))
NN = Neural_Network()
for i in range(1000): # trains the NN 1,000 times
print ("#" + str(i) + " Loss: " + str(torch.mean((y - NN(X))**2).detach().item())) # mean sum squared loss
NN.train(X, y)
NN.saveWeights(NN)
NN.predict()
# If the loss values are decreasing per epoch then the ANN model is learning!