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Cats vs Non-cats using Transfer Learning - Shape of the data

Let us first 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.

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]
    
    test_set_x =<< your code comes here >>[25:]
    test_set_y = test_set_y_orig[25:]
    
  • Print the shape of both train_set_x_orig and train_set_y_orig

    print("train_set_x shape: ", train_set_x_orig.shape)
    print("train_set_y shape: ", train_set_y_orig.shape)
    
  • Print the shape of both validation_x and validation_y

    print("Validation data size: ", << your code comes here >>)
    print("Validation data size: ", << your code comes here >>)
    
  • Print the shape of both test_set_x and test_set_y

    print("test_set_x shape: ", << your code comes here >>)
    print("test_set_y shape: ", << your code comes here >>)
    

We observe that we have very small data.

So using transfer learning, we could come up with a decent model yielding reasonable accuracy by using our tiny dataset.


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