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

• 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.