MNIST 97% Accuracy

MNIST 97% Accuracy

#doing k neighbors to get 97% accurace
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
import numpy as np
import os
import tensorflow as tf
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
np.random.seed(42)
(trainX, trainY), (testX, testY) = tf.keras.datasets.mnist.load_data()
trainX=trainX.reshape(-1,784)
testX=testX.reshape(-1,784)
#data=shuffle(data)
​
param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}]
model = KNeighborsClassifier()
grid_search =GridSearchCV(model,param_grid,cv=5, scoring="accuracy", verbose=3, n_jobs=-1)
grid_search.fit(trainX,trainY)
Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 666.1min finished
GridSearchCV(cv=5, error_score='raise-deprecating',
             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                            metric='minkowski',
                                            metric_params=None, n_jobs=None,
                                            n_neighbors=5, p=2,
                                            weights='uniform'),
             iid='warn', n_jobs=-1,
             param_grid=[{'n_neighbors': [3, 4, 5],
                          'weights': ['uniform', 'distance']}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring='accuracy', verbose=3)
grid_search.best_params_
grid_search.best_score_
y_pred = grid_search.predict(testX)
accuracy_score(testY, y_pred)