{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/309666155","pull_request_review_id":269578694,"id":309666155,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDMwOTY2NjE1NQ==","diff_hunk":"@@ -357,7 +357,7 @@ def _fit(self, X, y, incremental=False):\n         activations = [X]\n         activations.extend(np.empty((batch_size, n_fan_out))\n                            for n_fan_out in layer_units[1:])\n-        deltas = [np.empty_like(a_layer) for a_layer in activations]\n+        deltas = [np.empty_like(a_layer) for a_layer in 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can be:\r\n\r\n```suggestion\r\n        deltas = [None] * (len(activations) - 1)\r\n```\r\n\r\nbecause of how we do backprop only requires a list with `len(activations) - 1` 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