Table of Contents


In [1]:
from __future__ import division, print_function, absolute_import
#import tensorflow as tf

# Latest version curve generation helper functions
import numpy as np
import scipy as sp
import math
from scipy.interpolate import splprep, splev
from multiprocessing import Process, Queue, Pool
import json

In [2]:
n_planar_curves_list = []
planar_curves_list = []


def add_noise(inputs, mean, std_dev):
    result = map(lambda x: x + np.random.normal(mean,std_dev,x.shape), inputs)
    return list(result)

#Step autoencoder
_debug_verbose = False 

class AutoEncoder(object):
    def __init__(self, arch):        
        self.num_layers = len(arch)
        self.input_layer_size = arch[0]
        self.output_layer_size = arch[-1]
        self.num_hidden_layers = len(arch)-2
        self.costs = []
        self.weights = [np.random.randn(y, x)
                        for x, y in zip(arch[:-1], arch[1:])]        
        self.biases = [np.random.randn(y, 1) for y in arch[1:]]

    def getParams(self):
        #Get weights and biases unrolled into vector:
        params = [(x.ravel(), y.ravel()) for x, y in zip(self.weights, self.biases)]
        return params
    
    def forward(self, X):
        for b, w in zip(self.biases, self.weights):
            if (_debug_verbose): print("weights: ", w)
            if (_debug_verbose): print("biases: ", b)
            if (_debug_verbose): print("inputs :", X)
            if (_debug_verbose): print("dot product :", np.dot(w, X))
            #print("matrix dot product :", w.dot(X))
            X = self.unit_step(np.dot(w, X) + b)
            if (_debug_verbose): print("result :", X)
        return X.reshape(3,1)
    
    def unit_step(self, z):
        #return (lambda x: 0 if (x) < 0 else 1, z)[1]
        return z
    
    def unit_step_prime(self, z):
        return (1)

    def cost_function(self, X):
        self.yHat = self.forward(X)
        if (_debug_verbose): print ("squared error of X:{0} - Xhat:{1} is {2} & sum is {3}\n".format(X, self.yHat, ((X-self.yHat)**2), sum((X-self.yHat)**2)))
        J = 0.5*sum((X-self.yHat)**2)
        #self.costs.append(J) 
        return J
    
    def cost_derivative(self, output_activations, y):
        return (output_activations-y)   
    
    def cost_function_by_epoch(self, test_data, n_test):
        y_hat = [(self.forward(y)) for (y) in test_data[0:n_test]]
        y = [(y) for (y) in test_data[0:n_test]]
        #print([float(a[0][0]) for a in y])
        np.seterr( over='ignore' )
        #costs = []
        costs = [0.5*((a - b)**2) for a, b in zip(y, y_hat)]
        #costs.append([max(math.sqrt(0.5*(round(a[0][0],2) - round(b[0][0],2))**2),1000) for a, b in zip(y, y_hat)])
        #costs.append([0.5*math.sqrt((float(a[1][0]) - float(b[1][0]))**2) for a, b in zip(y, y_hat)])
        #costs.append([0.5*math.sqrt((float(a[2][0]) - float(b[2][0]))**2) for a, b in zip(y, y_hat)])
        self.costs.append(sum(costs)) #/n_test)
        #self.costs.append(sum(costs[:][:]))
        #self.costs.append([sum(costs[0]),sum(costs[1]),sum(costs[2])])
        if (_debug_verbose): print ("Total Cost {1} for Epoch {0} complete".format(len(self.costs), sum(self.costs[-1])))
        if (_debug_verbose): print ("Axis-wise Cost is {0} ".format((self.costs[-1])))
        return self.costs[-1]

 
    def GD(self, training_data, epochs, learning_rate, test_data=None):
        """Train the neural network using batch-wise 
        gradient descent. If ``test_data`` is provided then the
        network will be evaluated against the test data after each
        epoch, and partial progress printed out."""
        if test_data: n_test = len(test_data)
        n = len(training_data)
        for j in range(epochs):
            np.random.shuffle(training_data)
            self.process_batch(training_data, learning_rate)
            if test_data:
                result = self.evaluate(test_data, n_test)
                if (_debug_verbose): print ("Epoch {0}: Score {1} / {2}".format(j, result, n_test))
            else:
                if (_debug_verbose): print ("Epoch {0} complete".format(j))

                        
    def process_batch(self, batch, learning_rate):
        """Update the network's weights by applying
        gradient descent using backpropagation to a single batch.
        """
        base_w = [np.zeros(w.shape) for w in self.weights]
        base_b = [np.zeros(b.shape) for b in self.biases]
        count=0
        for x in batch:
            delta_error_b , delta_error_w = self.backprop(x)
            updated_b = [nb+dnb for nb, dnb in zip(base_b, delta_error_b)]
            updated_w = [nw+dnw for nw, dnw in zip(base_w, delta_error_w)]
            count=count+1
        #print ("Process {0} inputs backprop ".format(count))    
            eta=learning_rate   
            self.weights = [w-(eta/len(batch))*nw
                        for w, nw in zip(self.weights, updated_w)]
            self.biases = [b-(eta/len(batch))*nb
                       for b, nb in zip(self.biases, updated_b)]
        
    def backprop(self, x):
        """Return ``( delta_w)`` representing the
        gradient for the cost function C_x. """
        
        if (_debug_verbose): print ("input: ", x)
        
        delta_w = [np.zeros(w.shape) for w in self.weights]
        delta_b = [np.zeros(b.shape) for b in self.biases]
        
        activation = x
        activations = [x] # list to store all the activations, layer by layer
        zs = [] # list to store all the activation (z) vectors, layer by layer
        
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation) + b
            zs.append(z)
            activation = self.unit_step(z)
            activations.append(activation)
        
        if (_debug_verbose): print ("activations: ", activations)
        
        # backward pass
        delta = self.cost_derivative(activations[-1], x) *             self.unit_step_prime(zs[-1]) 
        delta_b[-1] = delta    
        delta_w[-1] = np.dot(delta, activations[-2].transpose())
        
        if (_debug_verbose): print ("cost derivative: ", self.cost_derivative(activations[-1], x))
        if (_debug_verbose): print ("unit step: ", self.unit_step_prime(zs[-1]))
        if (_debug_verbose): print("delta: ",delta)
        
        for l in range(2, self.num_layers):
            z = zs[-l]
            step1 = np.dot(self.weights[-l+1].transpose(), delta)
            delta = step1 * z
            delta_b[-l] = delta
            delta_w[-l] = np.dot(delta, activations[-l-1].transpose())
            if (_debug_verbose): print ("delta b updated: ", delta_b)
            if (_debug_verbose): print ("delta w updated:", delta_w)
            
        #print ("delta b: ", delta_b) 
        #print ("delta w:", delta_w)    
            
        return (delta_b, delta_w)

    def evaluate(self, test_data, n_test):
        """Return the number of test inputs for which the neural
        network outputs the correct result. Note that the neural
        network's output is assumed to be the index of whichever
        neuron in the final layer has the highest activation."""
        self.cost_function_by_epoch(test_data, n_test)
        test_results = [self.forward(x)
                        for (x) in test_data]
        return sum(((x) - (x_hat))**2 for (x, x_hat) in zip(test_data, test_results))/n_test      
    
    def reconstruct(self, inputs):
        return [self.forward(x) for (x) in inputs]

In [8]:
import time
start = time.time()
def process_curves(curves_array):
    errors = []
    start = time.time()
    for i in range(len(curves_array[:])):
        print("Analysing curve [", i ,"]")
        start = time.time()
        Planar_Data = list(np.asarray(zip(curves_array[i][0], curves_array[i][1], curves_array[i][2])).reshape(300,3,1)) 
        ae = AutoEncoder([3,2,3])
        #print(Planar_Data)
        #print("AE params:", ae.getParams())
        ae.GD(Planar_Data, 500, 0.1, Planar_Data)
        #print("costs: ", ae.costs)
        print("error for this curve: ", errors)
        #scale errors
        scale=[curves_array[i][j].max() - curves_array[i][j].min() for j in [0,1,2]]
        #scaled_error = [ a[0]/b for a,b in zip(ae.costs[-1],scale)]
        #print("scaled error for this curve: ", scale,  scaled_error)
        #errors.append(scaled_error)
        errors.append(ae.costs[-1])
        planarity_errors = [sum(i[:][:]) for i in errors]
        print("\nTotal Sum Error: ", planarity_errors)
        end = time.time()
        print("Time spent in this curve: ", end - start)
    print(end - start)
    print(errors)
    return planarity_errors

data = {}
data['planar_curves_error'] = []
data['non_planar_curves_error'] = []

In [ ]:
#p_curve_data_1488329066.88168.json
#np_curve_data_1488329083.428903.json
data['planar_curves_error'] = []
with open('p_curve_data_cube_method_1489173944.8.json') as infile:
    c = json.load(infile)
    planar_curves_array = np.asarray(c['planar_curves'])
print("Planar curves read array shape: ", planar_curves_array.shape)
planar_errors = process_curves(planar_curves_array)
data['planar_curves_error'].append(np.asarray(planar_errors).tolist())

import os
os.system('say "your program has finished"')


Planar curves read array shape:  (100, 3, 300)
Analysing curve [ 0 ]
input:  [[-0.86733502]
 [-0.09135316]
 [ 1.02132887]]
activations:  [array([[-0.86733502],
       [-0.09135316],
       [ 1.02132887]]), array([[ 0.2718419 ],
       [ 0.24988635]]), array([[ 0.61081816],
       [ 0.63460069],
       [ 0.04982504]])]
cost derivative:  [[ 1.47815318]
 [ 0.72595385]
 [-0.97150382]]
unit step:  1
delta:  [[ 1.47815318]
 [ 0.72595385]
 [-0.97150382]]
delta b updated:  [array([[ 0.3482355 ],
       [-0.33421657]]), array([[ 1.47815318],
       [ 0.72595385],
       [-0.97150382]])]
delta w updated: [array([[-0.30203685, -0.03181241,  0.35566297],
       [ 0.28987773,  0.03053174, -0.34134503]]), array([[ 0.40182397,  0.36937031],
       [ 0.19734468,  0.18140596],
       [-0.26409545, -0.24276555]])]
input:  [[-0.75549339]
 [-0.01066178]
 [ 1.0795801 ]]
activations:  [array([[-0.75549339],
       [-0.01066178],
       [ 1.0795801 ]]), array([[ 0.31574717],
       [ 0.23894797]]), array([[ 0.68675125],
       [ 0.62188451],
       [ 0.08434051]])]
cost derivative:  [[ 1.44224464]
 [ 0.63254629]
 [-0.99523959]]
unit step:  1
delta:  [[ 1.44224464]
 [ 0.63254629]
 [-0.99523959]]
delta b updated:  [array([[ 0.39654827],
       [-0.29111018]]), array([[ 1.44224464],
       [ 0.63254629],
       [-0.99523959]])]
delta w updated: [array([[-0.2995896 , -0.00422791,  0.42810562],
       [ 0.21993182,  0.00310375, -0.31427675]]), array([[ 0.45538466,  0.34462143],
       [ 0.1997247 ,  0.15114566],
       [-0.31424408, -0.23781048]])]
input:  [[-0.80786871]
 [-0.86671599]
 [ 0.47927961]]
activations:  [array([[-0.80786871],
       [-0.86671599],
       [ 0.47927961]]), array([[ 0.17091487],
       [ 0.01565293]]), array([[ 0.68840464],
       [ 0.85289382],
       [ 0.14421316]])]
cost derivative:  [[ 1.49627334]
 [ 1.71960981]
 [-0.33506645]]
unit step:  1
delta:  [[ 1.49627334]
 [ 1.71960981]
 [-0.33506645]]
delta b updated:  [array([[ 0.21092773],
       [-0.03924027]]), array([[ 1.49627334],
       [ 1.71960981],
       [-0.33506645]])]
delta w updated: [array([[-0.17040192, -0.18281444,  0.10109336],
       [ 0.03170098,  0.03401017, -0.01880706]]), array([[ 0.25573537,  0.02342106],
       [ 0.29390689,  0.02691693],
       [-0.05726784, -0.00524477]])]
input:  [[ 0.8841156 ]
 [-0.79645963]
 [ 0.55486465]]
activations:  [array([[ 0.8841156 ],
       [-0.79645963],
       [ 0.55486465]]), array([[ 0.66601246],
       [-0.4763024 ]]), array([[ 1.9120734 ],
       [ 0.98224495],
       [ 0.77408257]])]
cost derivative:  [[ 1.0279578 ]
 [ 1.77870458]
 [ 0.21921792]]
unit step:  1
delta:  [[ 1.0279578 ]
 [ 1.77870458]
 [ 0.21921792]]
delta b updated:  [array([[ 0.56240551],
       [ 1.17102558]]), array([[ 1.0279578 ],
       [ 1.77870458],
       [ 0.21921792]])]
delta w updated: [array([[ 0.49723148, -0.44793328,  0.31205894],
       [ 1.03532198, -0.93267459,  0.6497607 ]]), array([[ 0.6846327 , -0.48961877],
       [ 1.18463941, -0.84720126],
       [ 0.14600187, -0.10441402]])]
input:  [[-0.85040337]
 [-0.65782519]
 [ 0.62489984]]
activations:  [array([[-0.85040337],
       [-0.65782519],
       [ 0.62489984]]), array([[ 0.18993707],
       [ 0.08652804]]), array([[ 0.64645704],
       [ 0.79081769],
       [ 0.10886156]])]
cost derivative:  [[ 1.4968604 ]
 [ 1.44864287]
 [-0.51603828]]
unit step:  1
delta:  [[ 1.4968604 ]
 [ 1.44864287]
 [-0.51603828]]
delta b updated:  [array([[ 0.23728436],
       [-0.18932778]]), array([[ 1.4968604 ],
       [ 1.44864287],
       [-0.51603828]])]
delta w updated: [array([[-0.20178742, -0.15609163,  0.14827896],
       [ 0.16100498,  0.12454458, -0.1183109 ]]), array([[ 0.28430928,  0.12952039],
       [ 0.27515099,  0.12534822],
       [-0.0980148 , -0.04465178]])]
input:  [[-0.81402889]
 [-0.83872816]
 [ 0.49878306]]
activations:  [array([[-0.81402889],
       [-0.83872816],
       [ 0.49878306]]), array([[ 0.17276735],
       [ 0.0250599 ]]), array([[ 0.68054   ],
       [ 0.84338794],
       [ 0.13931697]])]
cost derivative:  [[ 1.49456888]
 [ 1.6821161 ]
 [-0.35946609]]
unit step:  1
delta:  [[ 1.49456888]
 [ 1.6821161 ]
 [-0.35946609]]
delta b updated:  [array([[ 0.21293479],
       [-0.06167234]]), array([[ 1.49456888],
       [ 1.6821161 ],
       [-0.35946609]])]
delta w updated: [array([[-0.17333507, -0.17859441,  0.10620827],
       [ 0.05020307,  0.05172633, -0.03076112]]), array([[ 0.2582127 ,  0.03745375],
       [ 0.29061473,  0.04215366],
       [-0.062104  , -0.00900818]])]
input:  [[ 0.63635401]
 [-1.09331501]
 [ 0.34311697]]
activations:  [array([[ 0.63635401],
       [-1.09331501],
       [ 0.34311697]]), array([[ 0.54942182],
       [-0.48530044]]), array([[ 1.74468633],
       [ 1.04113226],
       [ 0.70883723]])]
cost derivative:  [[ 1.10833232]
 [ 2.13444727]
 [ 0.36572027]]
unit step:  1
delta:  [[ 1.10833232]
 [ 2.13444727]
 [ 0.36572027]]
delta b updated:  [array([[ 0.4873303 ],
       [ 1.40491036]]), array([[ 1.10833232],
       [ 2.13444727],
       [ 0.36572027]])]
delta w updated: [array([[ 0.31011459, -0.53280554,  0.16721129],
       [ 0.89402034, -1.53600959,  0.48204858]]), array([[ 0.60894196, -0.53787417],
       [ 1.17271191, -1.03584821],
       [ 0.2009347 , -0.17748421]])]
input:  [[ 0.69887632]
 [-1.1126556 ]
 [ 0.330548  ]]
activations:  [array([[ 0.69887632],
       [-1.1126556 ],
       [ 0.330548  ]]), array([[ 0.56394904],
       [-0.51094311]]), array([[ 1.79102016],
       [ 1.05204118],
       [ 0.7345627 ]])]
cost derivative:  [[ 1.09214384]
 [ 2.16469678]
 [ 0.40401471]]
unit step:  1
delta:  [[ 1.09214384]
 [ 2.16469678]
 [ 0.40401471]]
delta b updated:  [array([[ 0.49119499],
       [ 1.49484949]]), array([[ 1.09214384],
       [ 2.16469678],
       [ 0.40401471]])]
delta w updated: [array([[ 0.34328455, -0.54653086,  0.16236352],
       [ 1.04471491, -1.66325266,  0.4941195 ]]), array([[ 0.61591347, -0.55802337],
       [ 1.22077867, -1.10603692],
       [ 0.22784371, -0.20642853]])]
input:  [[-0.89329364]
 [-0.24810329]
 [ 0.91115396]]
activations:  [array([[-0.89329364],
       [-0.24810329],
       [ 0.91115396]]), array([[ 0.23898464],
       [ 0.21341071]]), array([[ 0.59360376],
       [ 0.67234995],
       [ 0.05474091]])]
cost derivative:  [[ 1.4868974 ]
 [ 0.92045325]
 [-0.85641305]]
unit step:  1
delta:  [[ 1.4868974 ]
 [ 0.92045325]
 [-0.85641305]]
delta b updated:  [array([[ 0.30366435],
       [-0.33383042]]), array([[ 1.4868974 ],
       [ 0.92045325],
       [-0.85641305]])]
delta w updated: [array([[-0.27126143, -0.07534012,  0.27668497],
       [ 0.29820859,  0.08282443, -0.30417091]]), array([[ 0.35534564,  0.31731984],
       [ 0.21997419,  0.19643458],
       [-0.20466956, -0.18276772]])]
input:  [[ 0.3062507 ]
 [-0.86339003]
 [ 0.49898272]]
activations:  [array([[ 0.3062507 ],
       [-0.86339003],
       [ 0.49898272]]), array([[ 0.4887938],
       [-0.3231067]]), array([[ 1.49320595],
       [ 0.94815453],
       [ 0.56399642]])]
cost derivative:  [[ 1.18695525]
 [ 1.81154456]
 [ 0.06501371]]
unit step:  1
delta:  [[ 1.18695525]
 [ 1.81154456]
 [ 0.06501371]]
delta b updated:  [array([[ 0.47365622],
       [ 0.81884971]]), array([[ 1.18695525],
       [ 1.81154456],
       [ 0.06501371]])]
delta w updated: [array([[ 0.14505755, -0.40895005,  0.23634626],
       [ 0.2507733 , -0.70698668,  0.40859185]]), array([[ 0.58017637, -0.3835132 ],
       [ 0.88547175, -0.58532219],
       [ 0.0317783 , -0.02100636]])]
input:  [[-0.21431142]
 [-0.37798482]
 [ 0.8307879 ]]
activations:  [array([[-0.21431142],
       [-0.37798482],
       [ 0.8307879 ]]), array([[ 0.4131216 ],
       [-0.02930914]]), array([[ 1.09128246],
       [ 0.76762144],
       [ 0.32270817]])]
cost derivative:  [[ 1.30559388]
 [ 1.14560626]
 [-0.50807972]]
unit step:  1
delta:  [[ 1.30559388]
 [ 1.14560626]
 [-0.50807972]]
delta b updated:  [array([[ 0.45709944],
       [ 0.05223803]]), array([[ 1.30559388],
       [ 1.14560626],
       [-0.50807972]])]
delta w updated: [array([[-0.09796163, -0.17277665,  0.37975268],
       [-0.01119521, -0.01974518,  0.04339872]]), array([[ 0.53936903, -0.03826583],
       [ 0.47327469, -0.03357673],
       [-0.20989871,  0.01489138]])]
input:  [[ 0.27353785]
 [-0.83480259]
 [ 0.51849199]]
activations:  [array([[ 0.27353785],
       [-0.83480259],
       [ 0.51849199]]), array([[ 0.4831816 ],
       [-0.30574565]]), array([[ 1.46672268],
       [ 0.9367955 ],
       [ 0.54921701]])]
cost derivative:  [[ 1.19318483]
 [ 1.77159809]
 [ 0.03072501]]
unit step:  1
delta:  [[ 1.19318483]
 [ 1.77159809]
 [ 0.03072501]]
delta b updated:  [array([[ 0.47100058],
       [ 0.76065589]]), array([[ 1.19318483],
       [ 1.77159809],
       [ 0.03072501]])]
delta w updated: [array([[ 0.12883649, -0.3931925 ,  0.24421003],
       [ 0.20806818, -0.63499751,  0.39439399]]), array([[ 0.57652495, -0.36481107],
       [ 0.85600359, -0.54165841],
       [ 0.01484576, -0.00939404]])]
input:  [[ 0.18458517]
 [-0.75451282]
 [ 0.57333073]]
activations:  [array([[ 0.18458517],
       [-0.75451282],
       [ 0.57333073]]), array([[ 0.4695886 ],
       [-0.25669233]]), array([[ 1.39775458],
       [ 0.90646898],
       [ 0.50833406]])]
cost derivative:  [[ 1.21316941]
 [ 1.66098179]
 [-0.06499667]]
unit step:  1
delta:  [[ 1.21316941]
 [ 1.66098179]
 [-0.06499667]]
delta b updated:  [array([[ 0.46824416],
       [ 0.6064412 ]]), array([[ 1.21316941],
       [ 1.66098179],
       [-0.06499667]])]
delta w updated: [array([[ 0.08643093, -0.35329622,  0.26845876],
       [ 0.11194005, -0.45756766,  0.34769138]]), array([[ 0.56969052, -0.31141129],
       [ 0.77997811, -0.42636129],
       [-0.0305217 ,  0.01668415]])]
input:  [[-0.78790495]
 [-0.95398039]
 [ 0.41848079]]
activations:  [array([[-0.78790495],
       [-0.95398039],
       [ 0.41848079]]), array([[ 0.16070049],
       [-0.01773895]]), array([[ 0.70087508],
       [ 0.87562742],
       [ 0.16063494]])]
cost derivative:  [[ 1.48878002]
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result : [[ 0.53910809]
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weights:  [[ 1.45140399 -0.96368958]
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 [ 0.61723482 -0.65300792]]
biases:  [[ 0.33030415]
 [ 0.80817505]
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inputs : [[ 0.53910809]
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dot product : [[ 1.44949426]
 [ 0.19525547]
 [ 0.78474445]]
result : [[ 1.77979841]
 [ 1.00343052]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.33030415]
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inputs : [[ 0.2702312 ]
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dot product : [[ 0.22172857]
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result : [[ 0.55203272]
 [ 0.54322   ]
 [ 0.12723915]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[-0.88785181]
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dot product : [[-0.47279606]
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result : [[ 0.19523756]
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weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
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dot product : [[ 0.10024378]
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result : [[ 0.43054793]
 [ 0.57366232]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
 [-0.3393786   0.67566089 -0.50934367]]
biases:  [[ 0.66803362]
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inputs : [[-0.6015206 ]
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dot product : [[-0.37553494]
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result : [[ 0.29249868]
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weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
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inputs : [[ 0.29249868]
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dot product : [[ 0.30385583]
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result : [[ 0.63415998]
 [ 0.56728095]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[ 0.80545352]
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dot product : [[-0.13914111]
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result : [[ 0.52889251]
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weights:  [[ 1.45140399 -0.96368958]
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dot product : [[ 1.43388564]
 [ 0.20007785]
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result : [[ 1.76418979]
 [ 1.0082529 ]
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Total Cost [ 553.27727253] for Epoch 1 complete
Axis-wise Cost is [[ 192.66517576]
 [ 310.24903499]
 [  50.36306179]] 
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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inputs : [[-0.86733502]
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dot product : [[-0.45418163]
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result : [[ 0.21385199]
 [ 0.20940052]]
weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
 [ 0.80817505]
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inputs : [[ 0.21385199]
 [ 0.20940052]]
dot product : [[ 0.10858854]
 [-0.25774335]
 [-0.0047433 ]]
result : [[ 0.43889269]
 [ 0.5504317 ]
 [ 0.07122321]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[-0.75549339]
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dot product : [[-0.41027408]
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result : [[ 0.25775954]
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biases:  [[ 0.33030415]
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inputs : [[ 0.25775954]
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dot product : [[ 0.18494677]
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result : [[ 0.51525092]
 [ 0.53636766]
 [ 0.10688315]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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result : [[ 0.1106527 ]
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dot product : [[ 0.21704898]
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result : [[ 0.54735313]
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inputs : [[ 0.59698241]
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dot product : [[ 1.46763785]
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result : [[ 1.797942  ]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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dot product : [[-0.5370286 ]
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result : [[ 0.13100502]
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weights:  [[ 1.45140399 -0.96368958]
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result : [[ 0.54045768]
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result : [[ 0.48145021]
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result : [[ 1.63858077]
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Epoch 0: Score [[ 1.28443451]
 [ 2.0683269 ]
 [ 0.33575375]] / 300
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unit step:  1
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delta b updated:  [array([[ 0.33130482],
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delta w updated: [array([[ 0.28636519, -0.33039061,  0.13714436],
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delta b updated:  [array([[ 0.27376686],
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delta w updated: [array([[-0.15234777, -0.02573097,  0.28042782],
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cost derivative:  [[ 0.88559437]
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delta b updated:  [array([[ 0.42898297],
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delta w updated: [array([[ 0.34734163, -0.10471736,  0.40346279],
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cost derivative:  [[ 0.88874812]
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delta b updated:  [array([[ 0.41380186],
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delta w updated: [array([[ 0.34391379, -0.14649394,  0.3574723 ],
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delta b updated:  [array([[ 0.09662389],
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delta w updated: [array([[-0.07744042, -0.08650268,  0.04438868],
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cost derivative:  [[ 1.30970057]
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unit step:  1
delta:  [[ 1.30970057]
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delta b updated:  [array([[ 0.19551953],
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delta w updated: [array([[-0.17215625, -0.02611696,  0.19386827],
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input:  [[ 0.85862676]
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activations:  [array([[ 0.85862676],
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cost derivative:  [[ 0.93538534]
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unit step:  1
delta:  [[ 0.93538534]
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delta b updated:  [array([[ 0.32572446],
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delta w updated: [array([[ 0.27967574, -0.33059189,  0.13076724],
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input:  [[ 0.05949974]
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cost derivative:  [[ 1.10961747]
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unit step:  1
delta:  [[ 1.10961747]
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delta b updated:  [array([[ 0.29461408],
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delta w updated: [array([[ 0.01752946, -0.18778996,  0.19249662],
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input:  [[-0.53784878]
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activations:  [array([[-0.53784878],
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cost derivative:  [[ 1.21818765]
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unit step:  1
delta:  [[ 1.21818765]
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delta b updated:  [array([[ 0.27286213],
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delta w updated: [array([[-0.14675856, -0.0290955 ,  0.27716469],
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input:  [[ 0.53959763]
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delta w updated: [array([[-0.13350922, -0.0385408 ,  0.27044036],
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cost derivative:  [[ 0.99869413]
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delta w updated: [array([[ 0.18151608, -0.31813154,  0.10193563],
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delta w updated: [array([[ 0.26628112, -0.325843  ,  0.12070159],
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cost derivative:  [[ 1.10152453]
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delta w updated: [array([[-0.08742102, -0.0479909 ,  0.07443006],
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delta w updated: [array([[ 0.17800093, -0.26811821,  0.07903193],
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delta w updated: [array([[ 0.24458953, -0.24911665,  0.13429727],
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delta w updated: [array([[-0.09816897, -0.03505181,  0.20839223],
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delta w updated: [array([[-0.09032234, -0.04240233,  0.20552945],
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delta w updated: [array([[-0.07747587, -0.04445196,  0.06491395],
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dot product : [[ 1.43851933]
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result : [[ 1.66044328]
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weights:  [[ 0.28003415  0.29361948 -0.19522318]
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dot product : [[-0.08722927]
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biases:  [[ 0.64419214]
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weights:  [[ 1.41438055 -0.93577526]
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dot product : [[-0.40300802]
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Total Cost [ 510.79911772] for Epoch 2 complete
Axis-wise Cost is [[ 170.39250908]
 [ 290.65966707]
 [  49.74694157]] 
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inputs : [[ 0.08575923]
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dot product : [[ 0.20934922]
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result : [[ 0.43127316]
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result : [[ 0.2378194]
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result : [[ 0.47618504]
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