# y = our output of our neural network. This is a supervised method.
y = np.array(([1], [0], [0], [0], [0], [0], [0], [1]), dtype=float)
# what value we want to predict
xPredicted = np.array(([0, 0, 1]), dtype=float)
# Normalize xPredicted
X = X / np.amax(X, axis=0) # maximum of X input array
# maximum of xPredicted (our input data for the prediction)
xPredicted = xPredicted / np.amax(xPredicted, axis=0)
# set up our Loss file for graphing
lossFile = open("SumSquaredLossList.csv", "w")
myNeuralNetwork = Neural_Network(hidden_layer_size=10)
# trainingEpochs = 1000
trainingEpochs = 100000
for i in range(trainingEpochs):
# train myNeuralNetwork 1,000 times print ("Epoch # " + str(i) + "\n")
print("Network Input : \n" + str(X))
print("Expected Output of XOR Gate Neural Network: \n" + str(y))
print("Actual Output from XOR Gate Neural Network: \n" +
str(myNeuralNetwork.feedForward(X))) # mean sum squared loss
Loss = np.mean(np.square(y - myNeuralNetwork.feedForward(X)))
myNeuralNetwork.saveSumSquaredLossList(i, Loss)
print("Sum Squared Loss: \n" + str(Loss))
print("\n")
myNeuralNetwork.trainNetwork(X, y)
myNeuralNetwork.saveWeights()
myNeuralNetwork.predictOutput(xPredicted)
