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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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