Digits Recognizer using Python and React. Train the model.
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published April 4, 2018
Today's topic is an introduction to machine learning combined with computer vision. I will show you a kNN fitting model with the handwritten digits data taken from the MNIST Database with accuracy assessment.
Today's topic is an introduction to machine learning combined with computer vision. I will show you a kNN fitting model with the handwritten digits data taken from the MNIST Database with accuracy assessment.
#Environment setup
I chose Anaconda as my environment, so I'll skip the dependencies installation step.
#Import dependencies
I chose Anaconda as my environment, so I'll skip the dependencies installation step.
#Import dependencies
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
import cv2#Prepare data
The dataset from MNIST Database is available in the datasets module of sklearn, so let's start loading the data.
The dataset from MNIST Database is available in the datasets module of sklearn, so let's start loading the data.
digits = datasets.load_digits()We need to have two different datasets: one for testing and the other for training our model.
(X_train, X_test, y_train, y_test) = train_test_split(
digits.data, digits.target, test_size=0.25, random_state=42
)#Fit the model
Let's find the best parameter k. We can't just make up the k value, so let's train the model and evaluate k parameter accuracy.
Let's find the best parameter k. We can't just make up the k value, so let's train the model and evaluate k parameter accuracy.
ks = np.arange(2, 10)
scores = []
for k in ks:
model = KNeighborsClassifier(n_neighbors=k)
score = cross_val_score(model, X_train, y_train, cv=5)
score.mean()
scores.append(score.mean())
plt.plot(scores, ks)
plt.xlabel('accuracy')
plt.ylabel('k')As the output we can see such plot:
Looking at this chart, we can understand that the best accuracy was reached when k was 3. So from now, we'll be using k=3 for our model.
#Evaluate the model on the test data
#Evaluate the model on the test data
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)
z = model.predict(X_test) Let's now create a classification report to see the accuracy.
print(classification_report(y_test, z))
Amazing! We've reached 99% accuracy!
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Originally published at teimurjan.github.io.
Teimur Gasanov
Python/Go/Javascript full stack developer
