python version of multiple hidden layers neural network library

[pythonAIdeveloper]

` import random import math

def sigmoid(x, a, b): return 1/(1 + math.exp(-x))

def dsigmoid(y, a, b): return y * (1 - y)

class Matrix(): def init(self, rows, cols): self.rows = rows self.cols = cols self.data = []

    for i in range(self.rows):
        self.data.append([])
        for j in range(self.cols):
            self.data[i].append(0)

#vector operations @staticmethod def MatrixMultiply(m1, m2): if m1.cols != m2.rows: return "you did something wrong" else: result = Matrix(m1.rows, m2.cols) for i in range(result.rows): for j in range(result.cols): sum = 0 for k in range(m1.cols): sum += m1.data[i][k] * m2.data[k][j] result.data[i][j] = sum return result

#converting array into matrix @staticmethod def fromArray(arr): m = Matrix(len(arr), 1) for i in range(len(arr)): m.data[i][0] = arr[i] return m

#converting matrix into array def toArray(self): arr = [] for i in range(self.rows): for j in range(self.cols): arr.append(self.data[i][j]) return arr

#scalar operations def multiply(self, n): if type(n) == Matrix: for i in range(self.rows): for j in range(self.cols): self.data[i][j] *= n.data[i][j] else: for i in range(self.rows): for j in range(self.cols): self.data[i][j] *= n

def add(self, n):
    if type(n) == Matrix:
        for i in range(self.rows):
            for j in range(self.cols):
                self.data[i][j] += n.data[i][j]

    else:
        for i in range(self.rows):
            for j in range(self.cols):
                self.data[i][j] += n

@staticmethod
def subtract(a, b):
    result = Matrix(a.rows, a.cols)
    for i in range(a.rows):
        for j in range(a.cols):
            result.data[i][j] = a.data[i][j] - b.data[i][j]
    return result

randomization

def randomize(self):
    for i in range(self.rows):
        for j in range(self.cols):
            self.data[i][j] = random.uniform(-1, 1)

transposing a matrix

@staticmethod
def transpose(m):
    result = Matrix(m.cols, m.rows)
    for i in range(m.rows):
        for j in range(m.cols):
            result.data[j][i] = m.data[i][j]
    return result

applying a function

def map(self, func):
    for i in range(self.rows):
        for j in range(self.cols):
            self.data[i][j] = func(self.data[i][j], i, j)

@staticmethod
def staticMap(m, func):
    result = Matrix(m.rows, m.cols)
    for i in range(m.rows):
        for j in range(m.cols):
            val = m.data[i][j]
            result.data[i][j] = func(val, 0, 0)
    return result

printing a matrix

def printMatrix(self):
    b = []
    a = []
    for i in range(self.rows):
        a = []
        for j in range(self.cols):
            a.append([self.data[i][j]])
        b.append(a)
    for i in range (len(b)):
        print(b[i])
    print()

class SingleLayerNewralNetwork(): def init(self, inputNodes, hiddenNodes, OutputNodes):

    self.inputNodes = inputNodes
    self.hiddenNodes = hiddenNodes
    self.outputNodes = OutputNodes

    self.weights_ih = Matrix(self.hiddenNodes, self.inputNodes)
    self.weights_ho = Matrix(self.outputNodes, self.hiddenNodes)

    self.bias_h = Matrix(self.hiddenNodes, 1)
    self.bias_o = Matrix(self.outputNodes, 1)

    self.weights_ho.randomize()
    self.weights_ih.randomize()
    self.bias_h.randomize()
    self.bias_o.randomize()

    self.learningRate = 0.1

def feedForward(self, inputArray):
    # generating hidden outputs
    inputs = Matrix.fromArray(inputArray)

    hidden = Matrix.MatrixMultiply(self.weights_ih, inputs)
    hidden.add(self.bias_h)
    # activation function
    hidden.map(sigmoid)

    # generating the output's output
    output = Matrix.MatrixMultiply(self.weights_ho, hidden)
    output.add(self.bias_o)
    # activation function
    output.map(sigmoid)

    # done!
    return output.toArray()

def train(self, inputs_array, targets_array):

    # generating hidden's outputs
    inputs = Matrix.fromArray(inputs_array)
    hidden = Matrix.MatrixMultiply(self.weights_ih, inputs)
    hidden.add(self.bias_h)
    # activation function
    hidden.map(sigmoid)

    # generating the output's outputs
    outputs = Matrix.MatrixMultiply(self.weights_ho, hidden)
    outputs.add(self.bias_o)
    # activation function
    outputs.map(sigmoid)

    targets = Matrix.fromArray(targets_array)

    # error = targets - outputs

    # formula->
    # del(W) = lr * E * ($ * ($-1) * H)

    outputErrors = Matrix.subtract(targets, outputs)

    # calculating gradient
    gradients = Matrix.staticMap(outputs, dsigmoid)
    gradients.multiply(outputErrors)
    gradients.multiply(self.learningRate)

    # calculationg deltas
    hiddenT = Matrix.transpose(hidden)
    weight_ho_deltas = Matrix.MatrixMultiply(gradients, hiddenT)

    # adjust the weights and biases
    self.weights_ho.add(weight_ho_deltas)
    self.bias_o.add(gradients)

    # calculating hidden error
    weight_ho_t = Matrix.transpose(self.weights_ho)
    hiddenErrors = Matrix.MatrixMultiply(weight_ho_t, outputErrors)

    # calculating hidden layer gradient
    hiddenGradient = Matrix.staticMap(hidden, dsigmoid)
    hiddenGradient.multiply(hiddenErrors)
    hiddenGradient.multiply(self.learningRate)

    # calculate input -> hidden deltas
    inputsT = Matrix.transpose(inputs)
    weight_ih_deltas = Matrix.MatrixMultiply(hiddenGradient, inputsT)

    # adjust the weights and biases
    self.weights_ih.add(weight_ih_deltas)
    self.bias_h.add(hiddenGradient)

class MultiLayerNewralNetwork():

def __init__(self, inputNodes, arrayOfHiddenNodes, OutputNodes):

    self.inputNodes = inputNodes
    self.hiddenLayers = arrayOfHiddenNodes
    self.outputNodes = OutputNodes

    self.weights_ih = Matrix(self.hiddenLayers[0], self.inputNodes)
    self.weights_ho = Matrix(self.outputNodes, self.hiddenLayers[len(self.hiddenLayers)-1])
    self.weights_h = []
    for i in range(len(self.hiddenLayers)):
        if i != 0:
            self.weights_h.append(Matrix(self.hiddenLayers[i], self.hiddenLayers[i-1]))

    self.bias_o = Matrix(self.outputNodes, 1)
    self.bias_h = []
    for i in range(len(self.hiddenLayers)):
        self.bias_h.append(Matrix(self.hiddenLayers[i], 1))


    for i in range(len(self.weights_h)):
        self.weights_h[i].randomize()
    self.weights_ho.randomize()
    self.weights_ih.randomize()

    for i in range(len(self.bias_h)):
        self.bias_h[i].randomize()
    self.bias_o.randomize()

    self.learningRate = 0.1

def feedForward(self, inputArray):
    # generating hidden outputs
    inputs = Matrix.fromArray(inputArray)


    hidden = Matrix.MatrixMultiply(self.weights_ih, inputs)
    hidden.add(self.bias_h[0])
    hidden.map(sigmoid)

    hiddenVals = []
    hiddenVals.append(hidden)
    for i in range(len(self.weights_h)):
        a = Matrix.MatrixMultiply(self.weights_h[i], hiddenVals[len(hiddenVals)-1])
        a.add(self.bias_h[i+1])
        a.map(sigmoid)
        hiddenVals.append(a)

    output = Matrix.MatrixMultiply(self.weights_ho, hiddenVals[len(hiddenVals)-1])
    output.add(self.bias_o)
    output.map(sigmoid)

    # done!
    return output.toArray()

def train(self, inputs_array, targets_array):

    # generating hidden outputs
    inputs = Matrix.fromArray(inputs_array)

    hidden = Matrix.MatrixMultiply(self.weights_ih, inputs)
    hidden.add(self.bias_h[0])
    hidden.map(sigmoid)

    hiddenVals = []
    hiddenVals.append(hidden)
    for i in range(len(self.weights_h)):
        a = Matrix.MatrixMultiply(self.weights_h[i], hiddenVals[len(hiddenVals) - 1])
        a.add(self.bias_h[i + 1])
        a.map(sigmoid)
        hiddenVals.append(a)

    output = Matrix.MatrixMultiply(self.weights_ho, hiddenVals[len(hiddenVals) - 1])
    output.add(self.bias_o)
    output.map(sigmoid)

    """
    feed forward part over
    """

    targets = Matrix.fromArray(targets_array)
    outputErrors = Matrix.subtract(targets, output)

    # calculating gradient
    gradients = Matrix.staticMap(output, dsigmoid)  # dsigmoid the next layer
    gradients.multiply(outputErrors)  # multiply errors of nest layer
    gradients.multiply(self.learningRate)  # multiply lr

    # calculationg deltas
    hiddenT = Matrix.transpose(hiddenVals[len(hiddenVals) - 1])  # transpose previouse layer
    weight_ho_deltas = Matrix.MatrixMultiply(gradients, hiddenT)  # multiply to gradient

    # adjust the weights and biases
    self.weights_ho.add(weight_ho_deltas)  # add deltas
    self.bias_o.add(gradients)  # add gradients to the next layer

    hiddenErrors = [outputErrors]

    hiddenGradients = []
    hiddenGradients.append(gradients)
    # gradients.printMatrix()
    weightss = []

    for i in self.weights_h:
        weightss.append(i)

    weightss.append(self.weights_ho)

    for i in range(len(self.weights_h)):
        weight  = weightss[len(weightss) - 1 - i]
        weightT = Matrix.transpose(weight)
        error = Matrix.MatrixMultiply(weightT, hiddenErrors[0])
        hiddenErrors.insert(0, error)

        gradient = Matrix.staticMap(hiddenVals[len(hiddenVals) - 1 - i], dsigmoid)
        gradient.multiply(error)
        gradient.multiply(self.learningRate)

        previous_layer_t = Matrix.transpose(hiddenVals[len(hiddenVals) - 2 - i])
        delta = Matrix.MatrixMultiply(gradient, previous_layer_t)
        self.weights_h[len(self.weights_h) - 1 - i].add(delta)
        self.bias_h[len(self.bias_h) - 1 - i].add(gradient)

        hiddenGradients.append(gradient)

    wT = Matrix.transpose(weightss[0])
    e = Matrix.MatrixMultiply(wT, hiddenErrors[0])

    g = Matrix.staticMap(hiddenVals[0], dsigmoid)
    g.multiply(e)
    g.multiply(self.learningRate)

    # calculate input -> hidden deltas
    inputsT = Matrix.transpose(inputs)
    weight_ih_deltas = Matrix.MatrixMultiply(g, inputsT)

    # adjust the weights and biases
    self.weights_ih.add(weight_ih_deltas)
    self.bias_h[0].add(g)

if name == 'main': print("This is a Newral Network Library") print("-By CHAITANYA JAIN") `

please please please add it. It is the first time I am ever contributing. I don't even know how to give my code but i have just tried.