IndexError

in

$1$ # test back$\_$prop

$2$ nn $=$ Network$([2,3,2])$

$---->3$ nn$.$back$\_$prop$($X$\_$train$[0,$:$],$ y$\_$train$[0,$:$])$

$4$ print$($nn$.$W$[0])$

in back$\_$prop$($self$,$ x$,$ y$)$

$114$ # Backward pass through the network

$115$ for l in reversed$($range$(1,$ self.L$))$:

$-->116$ self.delta$[$l $-1]=$ np$.$dot$($self$.$W$[$l$].$T$,$ self.delta$[$l$])*$ self.g$\_$prime$($self$.$z$[$l $-1])$

$117$

$118$ # Compute gradients for weights and biases

IndexError: list index out of range

I am getting the above error in the code below kindly solve this

import numpy as np

from sklearn import datasets

import matplotlib.pyplot as plt

from matplotlib.colors import colorConverter, ListedColormap

$\%$matplotlib inline

class Network:

def $\_\_$init$\_\_($self$,$ sizes$)$:

$"""$

Initialize the neural network

:param sizes: a list of the number of neurons in each layer

$"""$

# save the number of layers in the network

self.L $=$ len$($sizes$)$

# store the list of layer sizes

self.sizes $=$ sizes

# initialize the bias vectors for each hidden and output layer

self.b $=[$np$.$random.randn$($n$,1)$ for n in self.sizes$[1$:$]]$

# initialize the matrices of weights for each hidden and output layer

self.W $=[$np$.$random.randn$($n$,$ m$)$ for $($m$,$ n$)$ in zip$($self$.$sizes$[$:$-1],$ self.sizes$[1$:$])]$

# initialize the derivatives of biases for backprop

self.db $=[$np$.$zeros$(($n$,1))$ for n in self.sizes$[1$:$]]$

# initialize the derivatives of weights for backprop

self.dW $=[$np$.$zeros$(($n$,$ m$))$ for $($m$,$n$)$ in zip$($self$.$sizes$[$:$-1],$ self.sizes$[1$:$])]$

# initialize the activities on each hidden and output layer

self.z $=[$np$.$zeros$(($n$,1))$ for n in self.sizes$]$

# initialize the activations on each hidden and output layer

self.a $=[$np$.$zeros$(($n$,1))$ for n in self.sizes$]$

# initialize the deltas on each hidden and output layer

self.delta $=[$np$.$zeros$(($n$,1))$ for n in self.sizes$]$

def g$($self$,$ z$)$:

$"""$

sigmoid activation function

:param z: vector of activities to apply activation to

$"""$

return $1\mathrm{.}0/(1\mathrm{.}0+$ np$.$exp$(-$z$))$

def g$\_$prime$($self$,$ z$)$:

$"""$

derivative of sigmoid activation function

:param z: vector of activities to apply derivative of activation to

$"""$

return self.g$($z$)*(1\mathrm{.}0-$ self.g$($z$))$

def grad$\_$loss$($self$,$ a$,$ y$)$:

$"""$

evaluate gradient of cost function for squared$-$loss C$($a$,$y$)=($a$-$y$)^2/2$

:param a: activations on output layer

:param y: vector$-$encoded label

$"""$

return $($a $-$ y$)$

def forward$\_$prop$($self$,$ x$)$:

$"""$

take an feature vector and propagate through network

:param x: input feature vector

$"""$

if len$($x$.$shape$)==1$:

x $=$ x$.$reshape$(-1,1)$

# TODO: step $1.$ Initialize activation on initial layer to x

# self.act$=$g$($self$.$z$)$

# self.Z$=$np$.$dot$($self$.$act,self.W$[0])$

# self.yhat$=$g$($self$.$Z$)$

# your code here check the logic self.a$[$l$-1]$

self.a$[0]=$ x

# Step $2-4$: Loop over layers and compute activities and activations

for layer in range$($self$.$L$-1)$:

self.z$[$layer$]=$ np$.$dot$($self$.$W$[$layer$],$ self.a$[$layer $-1])+$ self.b$[$layer$]$

self.a$[$layer$]=$ self.g$($self$.$z$[$layer$])$

# Return the final prediction

return self.a$[-1]$

## TODO: step $2-4.$ Loop over layers and compute activities and activations

## Use Sigmoid activation function defined above

# your code here

def back$\_$prop$($self$,$ x$,$ y$)$:

$"""$

Back propagation to get derivatives of cost function wrt weights and biases for a given training example

:param x: training features

:param y: vector$-$encoded label

$"""$

# Ensure input and output have correct shape

if len$($x$.$shape$)==1$:

x $=$ x$.$reshape$(-1,1)$

if len$($y$.$shape$)==1$:

y $=$ y$.$reshape$(-1,1)$

# Forward propagation to fill in activities and activations

self.forward$\_$prop$($x$)$

# Calculate delta for the output layer

self.delta$[-1]=$ self.grad$\_$loss$($self$.$a$[-1],$ y$)*$ self.g$\_$prime$($self$.$z$[-1])$

# Backward pass through the network

for l in reversed$($range$(1,$ self.L$))$:

self.delta$[$l $-1]=$ np$.$dot$($self$.$W$[$l$].$T$,$ self.delta$[$l$])*$ self.g$\_$prime$($self$.$z$[$l $-1])$

# Compute gradients for weights and biases

for l in range$($self$.$L $-1)$:

if l $==0$:

self.dW$[$l$]=$ np$.$dot$($self$.$delta$[$l$],$ x$.$T$)$

else:

self.dW$[$l$]=$ np$.$dot$($self$.$delta$[$l$],$ self.a$[$l $-1].$T$)$

self.db$[$l$]=$ self.delta$[$l$]$