Don’t complain about others’ mistakes but roll up your sleeves and help solve the problem by being kind to those who did

input_train = np.array([[0, 1, 0], [0, 1, 1], [0, 0, 0], [10, 0, 0], [10, 1, 1], [10, 0, 1]]) output_train = np.array([[0], [0], [0], [1], [1], [1]]) input_pred = np.array([1, 1, 0]) input_test = np.array([[1, 1, 1], [10, 0, 1], [0, 1, 10], [10, 1, 10], [0, 0, 0], [0, 1, 1]]) output_test = np.array([[0], [1], [0], [1], [0], [0]])

def forward(self, X): self.z = np.matmul(X, self.W1) self.z2 = self.sigmoid(self.z) self.z3 = np.matmul(self.z2, self.W2) o = self.sigmoid(self.z3) return o

Look at a situation like an outsider would do. What happened? What went wrong, and how can it be fixed? Admitting to being wrong or have done something incorrectly won’t make you look weak or stupid; on the contrary, taking responsibility for your actions will make you appear aware of your limit and prone to learn how to do better in the future.

The purpose of the forward pass function is to iterate forward through the different layers of the neural network to predict output for that particular epoch. Then, looking at the difference between the predicted output and the actual output, the weights will be updated during backward propagation.

In the example, I have chosen a neural network with three input nodes, three nodes in the hidden layer, and one output node. The above __init__ function initializes variables describing the size of the neural network. inputSize is the number of input nodes, which should be equal to the number of features in our input data. outputSize is equal to the number of output nodes, and hiddenSize describes the number of nodes in the hidden layer. Further, there will be weights between the different nodes in our network that will be adjusted during training.

scaler = MinMaxScaler() input_train_scaled = scaler.fit_transform(input_train) output_train_scaled = scaler.fit_transform(output_train) input_test_scaled = scaler.fit_transform(input_test) output_test_scaled = scaler.fit_transform(output_test)

To calculate the values at each node in every layer, the values at the nodes in the previous layer will be matrix multiplied with the applicable weights before a non-linear activation function will be applied to widen the possibilities for the final output function. In this example, we have chosen the Sigmoid as the activation function, but there are also many other alternatives.

def backward(self, X, y, o): self.o_error = y - o self.o_delta = self.o_error * self.sigmoidPrime(o) self.z2_error = np.matmul(self.o_delta, np.matrix.transpose(self.W2)) self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2) self.W1 = np.matmul(np.matrix.transpose(X), self.z2_delta) self.W2 = np.matmul(np.matrix.transpose(self.z2), self.o_delta)

In our training and test data sets the values are in a relatively small range, and it might therefore not be necessary to do feature scaling. It is, however, included here so that people can use their own numbers without changing too much of the code. Doing feature scaling is extremely easy in Python due to the Scikit-learn package, and its MinMaxScaler class. Simply create a MinMaxScaler object, and use the fit_transform function with your non-scaled data as input, and the function will return the same data scaled. There are also other scaling functions in the Scikit-learn package that I encourage you to try.

One of the easiest ways to get familiar with all the elements of a neural network is to create a neural network class. Such a class should include all the variables and functions that will be necessary for the neural network to work properly.

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In addition to the variables describing the size of the neural network and its weights, I have created several variables that are initialized during the creation of a NeuralNetwork object that will be used for evaluation purposes. The error_list will contain the mean absolute error (MAE) for each of the epochs, and the limit will describe the boundary for when a vector should be classified as a vector with element 10 as the first element and not. Then, there are variables that will be used to store the number of true positives, false positives, true negatives, and false negatives.

According to Guy Winch Ph.D. article on Psychology Today, “Some people have such a fragile ego, such brittle self-esteem, such a weak “psychological constitution,” that admitting they made a mistake or that they were wrong is fundamentally too threatening for their egos to tolerate.”

In this simple neural network, we will classify 1x3 vectors with 10 as the first element. Input and output training and test sets are created using NumPy’s array function, and input_pred is created to test a prediction function that will be defined later. Both the training and the test data are comprised of six samples with three features each, and since the output is given, we understand that this is an example of supervised learning.

Many machine learning models are not able to understand the difference between e.g. units, and will naturally apply more weight to features of high magnitudes. This can destroy an algorithm’s ability to predict new data points well. Further, training machine learning models with features of high magnitude will be slower than necessary, at least if gradient descent is used. This is because gradient descent converges faster when the input values are in approximately the same range.