<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Title
Step1:  Load Iris Flower Dataset
Step2:  Standardize Features
Step3:  Train Support Vector Classifier
Step4:  View Support Vectors
Step5:  View Indices Of Support Vectors
Step6:  View Number Of Support Vectors With Each Class
<ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
Explanation: Title: Find Support Vectors  
Slug: find_support_vectors 
Summary: How to find support vectors in Scikit-Learn 
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Support Vector Machines
Authors: Chris Albon  
<a alt="Find Support Vectors" href="https://machinelearningflashcards.com">
    <img src="find_support_vectors/Support_Vectors_print.png" class="flashcard center-block">
</a>
Preliminaries
End of explanation
#Load data with only two classes
iris = datasets.load_iris()
X = iris.data[:100,:]
y = iris.target[:100]
Explanation: Load Iris Flower Dataset
End of explanation
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
Explanation: Standardize Features
End of explanation
# Create support vector classifier object
svc = SVC(kernel='linear', random_state=0)
# Train classifier
model = svc.fit(X_std, y)
Explanation: Train Support Vector Classifier
End of explanation
# View support vectors
model.support_vectors_
Explanation: View Support Vectors
End of explanation
# View indices of support vectors
model.support_
Explanation: View Indices Of Support Vectors
End of explanation
# View number of support vectors for each class
model.n_support_
Explanation: View Number Of Support Vectors With Each Class
End of explanation
<END_TASK>