ANN Using Python

The CIFAR-10 dataset is a collection of 60000 32x32 colour images in 10 classes, with 6000 images per

class.

• True

• False

The code (x_train, y_train), (x_test,y_test) = datasets.cifar10.load_data(), is used to print the

data type

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• False

In #Get the shape of x_train

print(‘x_train shape:’, x_train.shape), x_train – contains all the training images

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• False

print(‘The image label is: ‘, y_train[index]), the error in the code is:

• y should be capital

• the punctuation should be replaced

• We should remove the punctuation

The error in def plot_sample(X, y, index): is

X should be small x

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• False

The code: ann = models.Sequential([

layers.Flatten(input_shape=(32,32,3)),

layers.Dense(3000, activation=’relu’),

layers.Dense(1000, activation=’relu’),

layers.Dense(10, activation=’softmax’)

]) is used to create the ANN.

• True

• False