Let us load the Fashion MNIST dataset from Cloudxlab's below mentioned folder location (this dataset is copied from Zalando Research repository).
Location - '/cxldata/datasets/project/fashion-mnist/'
You need to load the below 4 dataset files:
The class labels for Fashion MNIST are:
Label Description
0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot
Out datasets consists of 60,000 images and each image has 784 features. An image consists of 28x28 pixels, and each pixel is a value from 0 to 255 describing the pixel intensity. 0 for white and 255 for black.
Please define following string variables to store the location path of the dataset files. The dataset file location path should contain the file name also (appended in the end of the path).
The below variable contains location path for Training dataset
filePath_train_set = << your code comes here >>
The below variable contains location path for Training labels (target dataset)
filePath_train_label = << your code comes here >>
The below variable contains location path for Test dataset
filePath_test_set = << your code comes here >>
The below variable contains location path for Test labels
filePath_test_label = << your code comes here >>
Please create variables - (trainLabel, trainSet, testLabel, testSet) - using the below mentioned code. You can copy the below code as it is.
with gzip.open(filePath_train_label, 'rb') as trainLbpath:
trainLabel = np.frombuffer(trainLbpath.read(), dtype=np.uint8,
offset=8)
with gzip.open(filePath_train_set, 'rb') as trainSetpath:
trainSet = np.frombuffer(trainSetpath.read(), dtype=np.uint8,
offset=16).reshape(len(trainLabel), 784)
with gzip.open(filePath_test_label, 'rb') as testLbpath:
testLabel = np.frombuffer(testLbpath.read(), dtype=np.uint8,
offset=8)
with gzip.open(filePath_test_set, 'rb') as testSetpath:
testSet = np.frombuffer(testSetpath.read(), dtype=np.uint8,
offset=16).reshape(len(testLabel), 784)
trainLabel - contains Training label (target dataset)
trainSet - contains Training dataset
testLabel - contains Test label
testSet - contains Test dataset
Please copy the values of above created variables - trainSet, testSet, trainLabel and testLabel - in new variables - X_train, X_test, y_train and y_test respectively.
To get a feel of the data, you can view the article image at say index 0 of the Training dataset(X_train) and its corresponding label in the Target dataset (y_train). You can use showImage() function, that we defined earlier, for the same, e.g. showImage(X_train[0]).
Note - Having trouble with the assessment engine? Follow the steps listed here
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