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I have FINALLY been able to implement backpropagation, but there are still some bugs I need to fix. The main is issue the following: My ReLU activation function produces really big dJdW values (derivative of error function wrt weights). When this gets subtracted from the weights, my output becomes a matrix of -int or inf. How do I stop this? As of now, the only solution I have is to make my learning rate scalar variable REALLY small.

import numpy as np


class Neural_Network(object):
    def __init__(self, input_, hidden_, output_, numHiddenLayer_, numExamples_):
        # Define Hyperparameters
        self.inputLayerSize = input_
        self.outputLayerSize = output_
        self.hiddenLayerSize = hidden_
        self.numHiddenLayer = numHiddenLayer_
        self.numExamples = numExamples_
        self.scalar = 0.0000000001 # LEARNING RATE: Why does ReLU produce such large dJdW values?
        # in -> out
        self.weights = [] # stores matrices of each layer of weights
        self.z = [] # stores matrices of each layer of weighted sums
        self.a = [] # stores matrices of each layer of activity 
        self.biases = [] # stores all biases

        # Biases are matrices that are added to activity matrix
        # Dimensions -> numExamples_*hiddenLayerSize or numExamples_*outputLayerSize
        for i in range(self.numHiddenLayer):
            # Biases for hidden layer
            b = [np.random.random() for x in range(self.hiddenLayerSize)];
            B = [b for x in range(self.numExamples)];
            self.biases.append(np.mat(B))
        # Biases for output layer
        b = [np.random.random() for x in range(self.outputLayerSize)]
        B = [b for x in range(self.numExamples)];
        self.biases.append(np.mat(B))


        # Weights (Parameters)
        # Weight matrix between input and first layer
        W = np.random.rand(self.inputLayerSize, self.hiddenLayerSize)
        self.weights.append(W)

        for i in range(self.numHiddenLayer-1):
            # Weight matrices between hidden layers
            W = np.random.rand(self.hiddenLayerSize, self.hiddenLayerSize)
            self.weights.append(W)
        # Weight matric between hiddenlayer and outputlayer
        self.weights.append(np.random.rand(self.hiddenLayerSize, self.outputLayerSize))

    def setBatchSize(self, numExamples):
        # Changes the number of rows (examples) for biases
        if (self.numExamples > numExamples):
            self.biases = [b[:numExamples] for b in self.biases]

    def sigmoid(self, z):
        # Apply sigmoid activation function
        return 1/(1+np.exp(-z))

    def sigmoidPrime(self, z):
        # Derivative of sigmoid function
        return 1-self.sigmoid(z)

    def ReLU(self, z):
        # Apply activation function
        for (i, j), item in np.ndenumerate(z):
            if (item < 0):
                item *= 0.01
            else:
                item = item
        return z        


    def ReLUPrime(self, z):
        # Derivative of ReLU activation function
        for (i, j), item in np.ndenumerate(z):
            if (item < 0):
                item = 0.01
            else:
                item = 1

        return z

    def forward(self, X):
        # Propagate outputs through network

        self.z.append(np.dot(X, self.weights[0]) + self.biases[0])
        self.a.append(self.ReLU(self.z[0]))

        for i in range(1, self.numHiddenLayer):
            self.z.append(np.dot(self.a[-1], self.weights[i]) + self.biases[i])
            self.a.append(self.ReLU(self.z[-1]))

        self.z.append(np.dot(self.z[-1], self.weights[-1]) + self.biases[-1])
        self.a.append(self.ReLU(self.z[-1]))
        yHat = self.ReLU(self.z[-1])
        return yHat

    def backProp(self, X, y):
        # Compute derivative wrt W
        # out -> in
        dJdW = [] # stores matrices of each dJdW (equal in size to self.weights[])
        delta = [] # stores matrices of each backpropagating error

        self.yHat = self.forward(X)
        delta.insert(0,np.multiply(-(y-self.yHat), self.ReLUPrime(self.z[-1]))) # delta = (y-yHat)(sigmoidPrime(final layer unactivated))
        dJdW.insert(0, np.dot(self.a[-2].T, delta[0])) # dJdW
        for i in range(len(self.weights)-1, 1, -1):
            # Iterate from self.weights[-1] -> self.weights[1]
            delta.insert(0, np.multiply(np.dot(delta[0], self.weights[i].T), self.ReLUPrime(self.z[i-1])))
            dJdW.insert(0, np.dot(self.a[i-2].T, delta[0]))

        delta.insert(0, np.multiply(np.dot(delta[0], self.weights[1].T), self.ReLUPrime(self.z[0])))
        dJdW.insert(0, np.dot(X.T, delta[0]))


        return dJdW

    def train(self, X, y):
        for t in range(60000):
            dJdW = self.backProp(X, y)
            for i in range(len(dJdW)):
                self.weights[i] -= self.scalar*dJdW[i]

# Instantiating Neural Network
inputs = [int(np.random.randint(0,100)) for x in range(100)]
x = np.mat([x for x in inputs]).reshape(100,1)
y = np.mat([x+1 for x in inputs]).reshape(100,1)
NN = Neural_Network(1,3,1,1,100)


# Training
print("INPUT: ", end = '\n')
print(x, end = '\n\n')

print("BEFORE TRAINING", NN.forward(x), sep = '\n', end = '\n\n')
NN.train(x,y)
print("AFTER TRAINING", NN.forward(x), sep = '\n', end = '\n\n')

# Testing
test = np.mat([int(np.random.randint(0,100)) for x in range(100)]).reshape(100,1)
print("TEST INPUT:", test, sep = '\n', end = '\n\n')
print(NN.forward(test), end = '\n\n')


NN.setBatchSize(1) # changing settings to receive one input at a time

while True:
    # Give numbers between 0-100 (I need to fix overfitting) and it will get next value
    inputs = input()
    x = np.mat([int(i) for i in inputs.split(" ")])
    print(NN.forward(x))

I first made the ANN using sigmoid but Leaky ReLU is faster. The code is a bit much so here is a summary:

  1. Neural Network Class
    • define hyperparameter and stuff (include really small learning rate scalar)
    • activation functions and their derivatives (ReLU and sigmoid)
    • Member functions: forward propagation, backpropagation, setBatchSize etc.
  2. Instantiating ANN
    • setting hyperparameters (topology of ANN)
    • creating data (one array has values x and the output array has values x+1)
  3. Training
    • using inputs generated in step 2 to train ANN
  4. Testing
    • Testing using randomly generated inputs
    • User can give inputs

Hope that helps you help me. Thanks!

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  • $\begingroup$ Can you try initializing all biases and weights as zeros? $\endgroup$ – See Jian Shin Nov 10 '17 at 8:02
  • $\begingroup$ How are you initialising your weights? $\endgroup$ – Hima Varsha Nov 10 '17 at 17:27
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I suspect the problem is the fact that your input data values are very high. You're trying to map the input variable $x \in (0,100)$ to $y = x+1$ in your code, but neural networks work best when the data has much lower values. A good strategy is to normalise the data before training so that each feature has zero mean and unit variance. Try scaling your data down like so (I've also changed the code that originally generates the inputs to make it more efficient in numpy):

# Instantiating Neural Network
x = np.random.randint(0, 100, size=100).reshape(100,1)
y = x + 1

# normalize data to have zero mean and unit variance
x_normalized = (x - x.mean()) / x.std()
y_normalized = (y - y.mean()) / y.std()

Train your network with x_normalized and y_normalized instead of x and y. Then, during testing, you normalize your input data like above, and you can scale your predictions back up to the original scale by rearranging the above formula.

# generate test data & normalize using train data mean&std
test = np.random.randint(0, 100, size=100).reshape(100,1)
test_x_normalized = (test - x.mean()) / x.std()

# input to network to get normalized outputs
test_y_normalized = NN.forward(test_x_normalized)

# rescale normalized outputs to original 0-100 scale
test_y = (test_y_normalized * y.std()) + y.mean()

print("TEST INPUT:", test, sep = '\n', end = '\n\n')
print(test_y, end = '\n\n')
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