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Use the model parameter instead of the nanoNeuron instance #14

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Use the model parameter instead of the nanoNeuron instance #14

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8 changes: 4 additions & 4 deletions NanoNeuron.js
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,7 @@ function forwardPropagation(model, xTrain, yTrain) {
const predictions = [];
let cost = 0;
for (let i = 0; i < m; i += 1) {
const prediction = nanoNeuron.predict(xTrain[i]);
const prediction = model.predict(xTrain[i]);
cost += predictionCost(yTrain[i], prediction);
predictions.push(prediction);
}
Expand Down Expand Up @@ -147,8 +147,8 @@ function trainModel({model, epochs, alpha, xTrain, yTrain}) {
const [dW, dB] = backwardPropagation(predictions, xTrain, yTrain);

// Adjust our NanoNeuron parameters to increase accuracy of our model predictions.
nanoNeuron.w += alpha * dW;
nanoNeuron.b += alpha * dB;
model.w += alpha * dW;
model.b += alpha * dB;
}

// Let's return cost history from the function to be able to log or to plot it after training.
Expand Down Expand Up @@ -188,7 +188,7 @@ console.log('NanoNeuron parameters:', {w: nanoNeuron.w, b: nanoNeuron.b}); // i.
// Evaluate our model accuracy for test data-set to see how well our NanoNeuron deals with new unknown data predictions.
// The cost of predictions on test sets is expected to be be close to the training cost.
// This would mean that NanoNeuron performs well on known and unknown data.
[testPredictions, testCost] = forwardPropagation(nanoNeuron, xTest, yTest);
const [testPredictions, testCost] = forwardPropagation(nanoNeuron, xTest, yTest);
console.log('Cost on new testing data:', testCost); // i.e. -> 0.0000023

// Now, since we see that our NanoNeuron "kid" has performed well in the "school" during the training
Expand Down