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Cat vs Non-cat Classifier - Split the data

We split the test data in order to use a part of it for validation purposes. After that, let us have a look at the shape of the train, validation, and test datasets.

Note:

np.squeeze() function is used when we want to remove single-dimensional entries from the shape of an array.

For example, let us make a numpy array as follows:

arr = np.arange(1,5)
print(arr)

and reshape it in the shape of (1,2,2) as follows:

arr = arr.reshape(1,2,2)

We could use np.sqeeze to remove the one-dimensional entry to make it of shape (2,2)

arr_squeezed = np.squeeze(arr)
print(arr_squeezed)
print(arr_squeezed.shape)
INSTRUCTIONS
  • The test data contains 50 samples. Let the first 25 samples form the validation data, while the rest 25 samples form the test data.

    validation_x = test_set_x_orig[:25]
    validation_y = << your code comes here >>[:25]
    print("Validation data shape: ",validation_x.shape)
    
    test_set_x =<< your code comes here >>[25:]
    test_set_y = test_set_y_orig[25:]
    print("Test data shape: ",<< your code comes here >>)
    
  • Write the following code to know about the total number of samples and the shape of the train, validation, and test datasets.

    m_train = np.squeeze(train_set_y_orig.shape) 
    m_val = np.squeeze(validation_y.shape)
    m_test = np.squeeze(test_set_y.shape)
    num_px = train_set_x_orig.shape[1]
    
    print ("Number of training examples: m_train = " + str(m_train))
    print ("Number of validation examples: m_test = " + str(m_val))
    print ("Number of testing examples: m_test = " + str(m_test))
    print ("Height/Width of each image: num_px = " + str(num_px))
    print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
    

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