Unable to load data from numpy array for SVM Classification

Question

I have images in the numpy format, I have downloaded the data from the internet(https://github.com/ichatnun/spatiospectral-densenet-rice-classification/blob/master/x.npy). Example of data (1, 34, 23, 100), Here 1 is the image number, 34x23 is pixel value, 100 is the channel.

I wanted to load the data for the training of a machine learning model, I looked at the other sources, their data is in the format only 34x23

#my code till now
dataset1 = np.load('x.npy', encoding='bytes')
print("shape of dataset1")
print(dataset1.shape, dataset1.dtype)
#data shape
shape of dataset1
(3, 50, 170, 110) float64

#my code
data1 = dataset1[:, :, :, -1]
data1.shape

If I use SVM like,

from sklearn.svm import SVC
clf = SVC(gamma='auto')
clf.fit(datasset1, y)

I got the error

ValueError: Found array with dim 4. Estimator expected <= 2

I wanted to load the data as a dataframe or another format for train and split, but I am not able to remove the first value.

Sample data

print(dataset1)

[[[[0.17807601 0.15946769 0.20311266 ... 0.48133529 0.48742528
0.47095974]
[0.18518101 0.18394045 0.19093267 ... 0.45889252 0.44987031
0.46464419]
[0.19600767 0.18845156 0.18506823 ... 0.47558362 0.47738807
0.45821586]
...

My expected output is how to pass the data to the svm for classification

Have a look at the readme.md. It meens the file is composed by 3-dimensional 50x170x110 tensor e.g. a data with shape : 3x50x170x110. — Alexandre B.
– Alexandre B., Commented Aug 3, 2019 at 10:06

Gautam Shahi · Accepted Answer · 2019-08-03 19:37:11Z

1

the issue is that the SVM accept only 2d array, your data is in the format(numberof sample, rows, column, channel)

Try this, it works for me

dataset1 = np.load('x.npy', encoding='bytes')
dataset2 = np.load('labels.npy', encoding='bytes')

nsamples, nx, ny, nz = dataset1.shape
X = dataset1.reshape((nsamples,nx*ny*nz))

y = numpy.argmax(dataset2, axis=1)

from sklearn import svm
clf = svm.SVC(kernel='linear', C = 1.0)
clf.fit(X, y) 

#repalce X with your test data
print(clf.predict(X))

answered Aug 3, 2019 at 19:37

Gautam Shahi

4554 silver badges14 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

kederrac · Accepted Answer · 2019-08-03 10:22:05Z

0

pay attention to your data source, your x.npy doesn't have images

x.npy contains example datacubes of the processed rice dataset that can be used for training/testing. Each datacube is a three-dimensional 50x170x110 tensor: two spatial dimensions and one spectral dimension.

answered Aug 3, 2019 at 10:22

kederrac

17.4k6 gold badges36 silver badges58 bronze badges

5 Comments

SUSHMA KUMARI Over a year ago

Why am I getting 3 here? the shape of dataset1 as (3, 50, 170, 110), How interpreter will take 3 different data

kederrac Over a year ago

if you look here : github.com/ichatnun/spatiospectral-densenet-rice-classification/… you will find : num_training = x_training.shape[0] N_spatial = x_training.shape[1:3] N_bands = x_training.shape[3]

SUSHMA KUMARI Over a year ago

Thank you, so here in N_spatial, we are getting the rows and column of the image, am I right?

kederrac Over a year ago

you don't have images... you have tensors, "PROCESSED rice dataset"

SUSHMA KUMARI Over a year ago

okay, but I am getting error, while passing it to svm, I have update my question, please suggest

Collectives™ on Stack Overflow

Unable to load data from numpy array for SVM Classification

2 Answers 2

Comments

5 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

5 Comments

Your Answer

Sign up or log in

Post as a guest

Related