Apr-18-2018, 07:35 PM
I am fairly new to Python. I have spent some time trying to implement this and wanted to ask the community for some advice.
I am receiving an index out of bounds error. I would like help addressing that as well as any advice on a way to write this code more efficiently because mine is very rough and I am not sure it is doing what I think it is doing...
Here I have attached program what i am trying and what output I got.
I have implemented a backpropagation algorithm neural network. In that, I am using relu activation function.
I am getting an error in it.Tried many times but not getting a solution for it.
Thanking you in advance.
I am receiving an index out of bounds error. I would like help addressing that as well as any advice on a way to write this code more efficiently because mine is very rough and I am not sure it is doing what I think it is doing...
Here I have attached program what i am trying and what output I got.
I have implemented a backpropagation algorithm neural network. In that, I am using relu activation function.
I am getting an error in it.Tried many times but not getting a solution for it.
Thanking you in advance.
import numpy as np
import math
X = np.array(([65, 65, 77, 76, 80, 69, 78, 69, 84, 68, 68, 76, 71, 75, 65, 73, 75, 83, 68, 67, 73, 64, 71, 66, 75, 73, 65, 65, 70, 59, 70, 72, 83, 76, 77, 67],
[195, 200, 188, 187, 204, 203, 183, 175, 176, 203, 206, 190, 180, 193, 199, 196, 203, 198, 202, 208, 192, 176, 179, 180, 183, 198, 199, 184, 195, 188, 177, 177, 167, 160, 170, 198],
[19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 19, 20, 20, 20, 20, 19, 20],
[223, 207, 175, 185, 180, 204, 194, 196, 216, 176, 220, 219, 204, 183, 221, 212, 194, 212, 129, 198, 226, 220, 185, 208, 199, 187, 213, 132, 157, 182, 220, 194, 181, 167, 187, 173]), dtype=float)
y = np.array(([10], [12], [15], [17]), dtype=float)
xPredicted = np.array(([19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 20, 20, 20, 20, 19, 19, 19, 19, 19, 20, 20, 20, 20, 19, 20]), dtype=float)
# scale units
X = X/np.amax(X, axis=0)
xPredicted = xPredicted/np.amax(xPredicted, axis=0)
y = y/20
class Neural_Network(object):
def __init__(self):
self.inputSize = 36
self.outputSize = 1
self.hiddenSize = 18
self.W1 = np.random.randn(self.inputSize, self.hiddenSize)
self.W2 = np.random.randn(self.hiddenSize, self.outputSize)
def forward(self, X):
self.z = np.dot(X, self.W1)
self.z2 = self.relu(self.z)
self.z3 = np.dot(self.z2, self.W2)
o = self.relu(self.z3)
return o
def relu(self, s):
for i in range(0, len(str(s))):
for k in range(len(str(s[i]))):
if s[i][k] > 0:
s[i][k] = 1
else:
s[i][k] = 0
return s
def reluPrime(self, s) :
for i in range(0, len(str(s))):
for k in range(0, len(str(s[i]))):
if s[i][k] > 0:
pass # do nothing since it would be effectively replacing x with x
else:
s[i][k] = 0
return s
def backward(self, X, y, o):
self.o_error = y - o
self.o_delta = self.o_error*self.reluPrime(o)
self.z2_error = self.o_delta.dot(self.W2.T)
self.z2_delta = self.z2_error*self.reluPrime(self.z2)
self.W1 += X.T.dot(self.z2_delta)
self.W2 += self.z2.T.dot(self.o_delta)
def train (self, X, y):
o = self.forward(X)
self.backward(X, y, o)
def saveWeights(self):
np.savetxt("w1.txt", self.W1, fmt="%s")
np.savetxt("w2.txt", self.W2, fmt="%s")
def predict(self) :
print("Predicted data based on trained weights: ")
print("Input (scaled): \n" + str(xPredicted))
print("Output: \n" + str(self.forward(xPredicted)))
NN = Neural_Network()
for i in range(120):
print("Input: \n" + str(X))
print("Actual Output: \n" + str(y))
print("Predicted Output: \n" + str(NN.forward(X)))
print("Loss: \n" + str(np.mean(np.square(y - NN.forward(X)))))
print("\n")
NN.train(X, y)
NN.predict()
NN.saveWeights()output:$python main.py
Input:
[[ 0.29147982 0.31400966 0.40957447 0.40641711 0.39215686 0.33823529
0.40206186 0.35204082 0.38888889 0.33497537 0.30909091 0.34703196
0.34803922 0.38860104 0.29411765 0.34433962 0.36945813 0.39150943
0.33663366 0.32211538 0.32300885 0.29090909 0.38378378 0.31730769
0.37688442 0.36868687 0.30516432 0.35326087 0.35897436 0.31382979
0.31818182 0.37113402 0.45856354 0.45508982 0.41176471 0.33838384]
[ 0.87443946 0.96618357 1. 1. 1. 0.99509804
0.94329897 0.89285714 0.81481481 1. 0.93636364 0.86757991
0.88235294 1. 0.90045249 0.9245283 1. 0.93396226
1. 1. 0.84955752 0.8 0.96756757 0.86538462
0.91959799 1. 0.9342723 1. 1. 1.
0.80454545 0.91237113 0.92265193 0.95808383 0.90909091 1. ]
[ 0.08520179 0.09178744 0.10106383 0.10160428 0.09803922 0.09803922
0.10309278 0.10204082 0.08796296 0.09359606 0.08636364 0.08675799
0.09313725 0.10362694 0.09049774 0.09433962 0.09852217 0.08962264
0.09405941 0.09134615 0.0840708 0.09090909 0.10810811 0.09615385
0.10050251 0.0959596 0.08920188 0.10326087 0.0974359 0.10106383
0.09090909 0.10309278 0.11049724 0.11976048 0.10160428 0.1010101 ]
[ 1. 1. 0.93085106 0.98930481 0.88235294 1. 1.
1. 1. 0.86699507 1. 1. 1.
0.94818653 1. 1. 0.95566502 1. 0.63861386
0.95192308 1. 1. 1. 1. 1.
0.94444444 1. 0.7173913 0.80512821 0.96808511 1. 1.
1. 1. 1. 0.87373737]]
Actual Output:
[[ 0.5 ]
[ 0.6 ]
[ 0.75]
[ 0.85]]
Traceback (most recent call last):
File "main.py", line 86, in <module>
print("Predicted Output: \n" + str(NN.forward(X)))
File "main.py", line 29, in forward
o = self.relu(self.z3)
File "main.py", line 38, in relu
if s[i][k] > 0:
IndexError: index 1 is out of bounds for axis 0 with size 1[python][python][python][/python][/python][/python]