code.py

# -*- coding: utf-8 -*-

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VoCHyZAfikFTcceEcwLmU6CHPdRoOC7n
"""

import cv2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from keras.datasets import cifar10
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout
from keras.models import Sequential, load_model
from keras.callbacks import EarlyStopping

#loading the CIFAR-10 dataset
from keras.datasets import cifar10
train, test = cifar10.load_data()

x_train=train[0]
y_train=train[1]
x_test=test[0]
y_test=test[1]

from PIL import Image

#concatenating the train and test data in order to split the data according to our liking
X=np.concatenate((x_train,x_test))
m=60000
Y=np.concatenate((y_train,y_test))

#splitting data
i = int((0.8) * X.shape[0]) 
o = np.random.permutation(X.shape[0])   
train_x, test_x = np.split(np.take(X,o,axis=0), [i])
train_y,test_y = np.split(np.take(Y,o), [i])

plt.imshow(train_x[0])

train_x.shape

test_x.shape

train_y.shape

test_y.shape

#grayscaling the image
x_train=np.array([cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) for image in train_x])
x_test=np.array([cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) for image in test_x])

x_train.shape

labels = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

#Resizing the image 
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)

x_train.shape

#reshape to 2d array
nsamples, nx, ny, nrgb = x_train.shape
x_train2 = x_train.reshape((nsamples,nx*ny*nrgb))

#reshaping the test data to 2d array
nsamples, nx, ny, nrgb = x_test.shape
x_test2 = x_test.reshape((nsamples,nx*ny*nrgb))

#applying the naive bayes model on the data
from sklearn.naive_bayes import GaussianNB

nb=GaussianNB()

nb.fit(x_train2,train_y)

y_pred_nb=nb.predict(x_test2)

y_pred_nb

from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
accuracy_score(y_pred_nb,test_y)
print(classification_report(y_pred_nb,test_y))

"""Here we are getting an accuracy of 27%."""

confusion_matrix(y_pred_nb,test_y)