MNIST Example returns difference in predictions: 1.0

I’m using Google Collab GPU runtime to run MNIST example using Keras 1.2.0.

This snippet from MNIST Example

from deeplift.util import compile_func
import numpy as np
from keras import backend as K

deeplift_model = revealcancel_model
deeplift_prediction_func = compile_func([deeplift_model.get_layers()[0].get_activation_vars()],
                                       deeplift_model.get_layers()[-1].get_activation_vars())
original_model_predictions = keras_model.predict(X_test, batch_size=200)
converted_model_predictions = deeplift.util.run_function_in_batches(
                                input_data_list=[X_test],
                                func=deeplift_prediction_func,
                                batch_size=200,
                                progress_update=None)
print("difference in predictions:",np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)))
assert np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)) < 10**-5
predictions = converted_model_predictions

However, I’m getting the following result:

difference in predictions: 1.0
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-4-3f4204ab927a> in <module>()
     13                                 progress_update=None)
     14 print("difference in predictions:",np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)))
---> 15 assert np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)) < 10**-5
     16 predictions = converted_model_predictions

I used the provided shell script to download .h5 model file.

Furthermore, the Compute Importance Scores snippet results in the following error:

RuntimeError: You set the target layer to an activation layer, which is unusual so I am throwing an error - did you mean to set the target layer to the layer *before* the activation layer instead? (recommended for  classification)

[AvantiShri]

Btw, I added an example notebook that works on Keras 2 to the keras2compat branch: https://github.com/kundajelab/deeplift/blob/keras2compat/examples/mnist/MNIST_replicate_figures.ipynb - let me know if you have any issues.

[donigian]

Excellent, thanks for the clarification, I’ll give it a test drive.