i am new to python,trying to learn machine learning in python.i have tried to write a neural network from scratch with one hidden layer on the famous iris dataset.this is a three class classifier with out put as one hot vectors.i have also taken help from already written algos for help.for instance i used the same training set as my testing set.

it is a huge code to go through,i would like you to tell me, that how do we subtract 'y' output( which is one hot vector) of dimensions (150,3) and my out y softmax will be of vector (150,21).this is my biggest problem.i tried to look online everyone have used this method but since i am weak in python i don't understand it.this is the line of code `delta3[range(m1), y] -= 1`
`arrays used as indices must be of integer (or boolean) type` if m1 is sie of(150)
and if i give size m1(150,3) then
`delta3[range(m1), y] -= 1
TypeError: range() integer end argument expected, got tuple.`

```
remember m1=150
my y vector=150,3
softmax=150,21

```

my code is

```
#labels or classes
#1=iris-setosa
#2=iris-versicolor
#0=iris-virginica

#features
#sepallength
#sepalwidth
#petallengthcm
#petalwidth

import pandas as pd
import matplotlib.pyplot as plt
import csv
import numpy as np
df=pd.read_csv('Iris.csv')

df.convert_objects(convert_numeric=True)
df.fillna(0,inplace=True)

df.drop(['Id'],1,inplace=True)
#function to convert three labels into values 0,1,2
def handle_non_numericaldata(df):
    columns=df.columns.values
    for column in columns:
        text_digit_vals={}
        def convert_to_int(val):
            return text_digit_vals[val]
        if df[column].dtype!=np.int64 and df[column].dtype!=np.float:       
            column_contents=df[column].values.tolist()
            unique_elements=set(column_contents)
            x=0
            for unique in unique_elements:
                if unique not in text_digit_vals:
                    text_digit_vals[unique]=x
                    x+=1
            df[column]=list(map(convert_to_int,df[column]))
     return(df)
handle_non_numericaldata(df)    

x=np.array(df.drop(['Species'],1).astype(float))
c=np.array(df['Species'])

n_values=(np.max(c)+1)
y=(np.eye(n_values)[c])

m1=np.size(c)

theta=np.ones(shape=(4,1))
theta2=np.ones(shape=(1,21))
#no of examples "m"

#learning rate alpha
alpha=0.01
#regularization parameter
lamda=0.01
for i in range(1,1000):
    z1=np.dot(x,theta)  
    sigma=1/(1+np.exp(-z1))

    #activation layer 2.
    a2=sigma
    z2=np.dot(a2,theta2)

    probs=np.exp(z2)

    softmax=probs/np.sum(probs,axis=1,keepdims=True)
    delta3=softmax

    delta3[range(m1), y] -= 1

    A2=np.transpose(a2)
    dw2 = (A2).dot(delta3)
    W2=np.transpose(theta2)

    delta2=delta3.dot(W2)*sigma*(1-sigma)
    X2=np.transpose(x)
    dw1=np.dot(X2,delta2)

    dw2=dw2-lamda*theta2
    dw1=dw1-lamda*theta

    theta =theta -alpha* dw1

     theta2= theta2-alpha * dw2
    correct_logprobs=0
    correct_logprobs=correct_logprobs-np.log(probs[range(m1),y])
    data_loss=np.sum(correct_logprobs)
    data_loss+=lamda/2*(np.sum(np.square(theta))+ np.square(theta2))
    loss=1./m1*data_loss
    if 1000%i==0:
        print("loss after iteration%i:%f",loss)

final1=x.dot(theta)
sigma=1/(1+np.exp(-final1))
z2=sigma.dot(theta2)
exp_scores=np.exp(z2)
probs=exp_scores/np.sum(exp_scores,axis=1,keepdims=True)
print(np.argmax(probs,axis=1))

```