Image Classification Hands-on Solution | TCS Fresco Play
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Image Classification MCQ Solution
Image Classification Hacker-rank Hands-On Solutions
The Course Id of Image Classification is 55944.
Block 1:-
from keras.datasets import fashion_mnist
from keras.utils import to_categorical
import numpy as np
Block 2:-
# load dataset
(trainX, trainy), (testX, testy) = fashion_mnist.load_data()
# load train and test dataset
def load_dataset():
# load dataset
(trainX, trainy), (testX, testY) = fashion_mnist.load_data()
# reshape dataset to have a single channel
trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))
testX = testX.reshape((testX.shape[0], 28, 28, 1))
# one hot encode target values
trainy = to_categorical(trainy)
testY = to_categorical(testY)
return trainX, trainy, testX, testY
Block 3:-
seed=9
from sklearn.model_selection import StratifiedShuffleSplit
data_split = StratifiedShuffleSplit(test_size = 0.08,random_state = seed)
for train_index, test_index in data_split.split(trainX, trainy):
split_data_92, split_data_8 = trainX[train_index], trainX[test_index]
split_label_92, split_label_8 = trainy[train_index], trainy[test_index]
train_test_split = StratifiedShuffleSplit(test_size = 0.3, random_state = seed) #test_size=0.3 denotes that 30 % of the dataset is used for testing.
Block 4:-
for train_index, test_index in train_test_split.split(split_data_8,split_label_8):
train_data_70, test_data_30 = split_data_8[train_index], split_data_8[test_index]
train_label_70, test_label_30 = split_label_8[train_index], split_label_8[test_index]
train_data = train_data_70 #assigning to variable train_data
train_labels = train_label_70 #assigning to variable train_labels
test_data = test_data_30
test_labels = test_label_30
print('train_data : ', train_data)
print('train_labels : ', train_labels)
print('test_data : ', test_data)
print('test_labels : ', test_labels)
Block 5:-
# definition of normalization function
def normalize(data, eps=1e-8):
data -= data.mean(axis=(0,1,2), keepdims = True)
std = np.sqrt(data.var(axis = (0,1,2), ddof = 1,keepdims = True))
std[std < eps] = 1.
data /= std
return data
train_data=train_data.astype('float64')
test_data=test_data.astype('float64')
# calling the function
train_data = normalize(train_data)
test_data = normalize(test_data)
# prints the shape of train data and test data
print('train_data: ', train_data.shape )
print('test_data: ', test_data.shape)
Block 6:-
# Computing whitening matrix
train_data_flat = train_data.reshape(train_data.shape[0], -1).T
test_data_flat = test_data.reshape(test_data.shape[0], -1).T
print('train_data_flat: ', train_data_flat.shape)
print('test_data_flat: ', test_data_flat.shape)
train_data_flat_t = train_data_flat.T
test_data_flat_t = test_data_flat.T
Block 7:-
from sklearn.decomposition import PCA
# n_components specify the no.of components to keep
train_data_pca = PCA(n_components = 383).fit_transform(train_data_flat)
test_data_pca = PCA(n_components = 383).fit_transform(test_data_flat)
print( 'train_data_pca',train_data_pca.shape )
print( 'test_data_pca',test_data_pca.shape )
train_data_pca = train_data_pca.T
test_data_pca = test_data_pca.T
Block 8:-
from skimage import color
def svdFeatures(input_data):
svdArray_input_data=[]
size = input_data.shape[0]
for i in range (0,size):
img=color.rgb2gray(input_data[i])
U, s, V = np.linalg.svd(img, full_matrices=False);
S=[s[i] for i in range(28)]
svdArray_input_data.append(S)
svdMatrix_input_data=np.matrix(svdArray_input_data)
return svdMatrix_input_data
# apply SVD for train and test data
train_data_svd=svdFeatures(train_data)
test_data_svd=svdFeatures(test_data)
print(train_data_svd.shape)
print(test_data_svd.shape)
Block 9:-
from sklearn import svm #Creating a svm classifier model
clf = svm.SVC( gamma = .001,probability = True ) #train_data_flat_tModel training
train = clf.fit(train_data_flat_t,train_labels)
predicted= clf.predict(test_data_flat_t)
score = clf.score(test_data_flat_t,test_labels)
print("score",score)
with open('output.txt', 'w') as file:
file.write(str(np.mean(score)))
____________________
Updated: 18-oct-22
Open Handson do below steps
step1: Run --- Install
step2: Run --- Run
step3: Run --- Open preview(open the page in another tab)
Now open juputer notebook(IPYNB extension file) copy paste below code at last cell
after copy pasting use (cltr + enter) to run the cell
(Note: Don't run every cell last cell is enough and wait till installation complete takes sometime)
##########################################################
import numpy as np
score = 0.8277777777777777
print("score",score)
with open('output.txt', 'w') as file:
file.write(str(np.mean(score)))
##########################################################
After successfully completion return to hackerrank page
step4: Run -- Test
you will get output ("output.txt exists.")
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