{"action":"created","comment":{"url":"https://api.github.com/repos/civisanalytics/muffnn/pulls/comments/87026187","pull_request_review_id":7654173,"id":87026187,"diff_hunk":"@@ -0,0 +1,682 @@\n+\"\"\"\n+Autoencoder in scikit-learn style with TensorFlow\n+\"\"\"\n+import logging\n+import re\n+import warnings\n+\n+import numpy as np\n+import tensorflow as tf\n+import scipy.sparse as sp\n+\n+from sklearn.base import TransformerMixin, BaseEstimator\n+from sklearn.utils import check_array, check_random_state\n+from sklearn.utils.validation import NotFittedError\n+\n+from ..core import TFPicklingBase, affine\n+\n+\n+_LOGGER = logging.getLogger(__name__)\n+\n+\n+class Autoencoder(TFPicklingBase, TransformerMixin, BaseEstimator):\n+    \"\"\"\n+    An autoencoder using TensorFlow.\n+\n+    Parameters\n+    ----------\n+    hidden_units : tuple or list, optional\n+        A list of integers indicating the number of hidden layers and their\n+        sizes. The list is reflected about the last element to form the\n+        autoencoder. For example, if hidden_units is (256, 32, 16) then\n+        the autoencoder has layers\n+\n+            [input_layer, 256, 32, 16, 32, 256, input_layer]\n+\n+    batch_size : int, optional\n+        The batch size for learning and prediction. If there are fewer\n+        examples than the batch size during fitting, then the the number of\n+        examples will be used instead.\n+    n_epochs : int, optional\n+        The number of epochs (iterations through the training data) when\n+        fitting.\n+    dropout : float or None, optional\n+        The dropout probability. If None, then dropout will not be used.\n+        Note that dropout is not applied to the input layer.\n+    activation : (callable, callable), optional\n+        The activation functions for\n+\n+            (the bulk of the autoencoder and encoding layer, output layer).","path":"muffnn/autoencoder/autoencoder.py","position":49,"original_position":49,"commit_id":"e618ebb58b649e58e85606e7c27f92e2c905be7f","original_commit_id":"e618ebb58b649e58e85606e7c27f92e2c905be7f","user":{"login":"mheilman","id":181744,"avatar_url":"https://avatars.githubusercontent.com/u/181744?v=3","gravatar_id":"","url":"https://api.github.com/users/mheilman","html_url":"https://github.com/mheilman","followers_url":"https://api.github.com/users/mheilman/followers","following_url":"https://api.github.com/users/mheilman/following{/other_user}","gists_url":"https://api.github.com/users/mheilman/gists{/gist_id}","starred_url":"https://api.github.com/users/mheilman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mheilman/subscriptions","organizations_url":"https://api.github.com/users/mheilman/orgs","repos_url":"https://api.github.com/users/mheilman/repos","events_url":"https://api.github.com/users/mheilman/events{/privacy}","received_events_url":"https://api.github.com/users/mheilman/received_events","type":"User","site_admin":false},"body":"unfinished here?","created_at":"2016-11-08T16:25:47Z","updated_at":"2016-11-08T16:54:21Z","html_url":"https://github.com/civisanalytics/muffnn/pull/9#discussion_r87026187","pull_request_url":"https://api.github.com/repos/civisanalytics/muffnn/pulls/9","_links":{"self":{"href":"https://api.github.com/repos/civisanalytics/muffnn/pulls/comments/87026187"},"html":{"href":"https://github.com/civisanalytics/muffnn/pull/9#discussion_r87026187"},"pull_request":{"href":"https://api.github.com/repos/civisanalytics/muffnn/pulls/9"}}},"pull_request":{"url":"https://api.github.com/repos/civisanalytics/muffnn/pulls/9","id":91712207,"html_url":"https://github.com/civisanalytics/muffnn/pull/9","diff_url":"https://github.com/civisanalytics/muffnn/pull/9.diff","patch_url":"https://github.com/civisanalytics/muffnn/pull/9.patch","issue_url":"https://api.github.com/repos/civisanalytics/muffnn/issues/9","number":9,"state":"open","locked":false,"title":"WIP autoencoder","user":{"login":"beckermr","id":5296416,"avatar_url":"https://avatars.githubusercontent.com/u/5296416?v=3","gravatar_id":"","url":"https://api.github.com/users/beckermr","html_url":"https://github.com/beckermr","followers_url":"https://api.github.com/users/beckermr/followers","following_url":"https://api.github.com/users/beckermr/following{/other_user}","gists_url":"https://api.github.com/users/beckermr/gists{/gist_id}","starred_url":"https://api.github.com/users/beckermr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/beckermr/subscriptions","organizations_url":"https://api.github.com/users/beckermr/orgs","repos_url":"https://api.github.com/users/beckermr/repos","events_url":"https://api.github.com/users/beckermr/events{/privacy}","received_events_url":"https://api.github.com/users/beckermr/received_events","type":"User","site_admin":false},"body":"Thus PR adds an autoencoder to muffnn. The autoencoder supports a variety of metrics and is compatible with the scikit-learn transform interface. \r\n\r\nI additionally \r\n\r\n1. refactored the MLP classes into their own subpackage.\r\n2. pinned conda to 4.0.5 to avoid conda bugs\r\n3. upgraded TensorFlow to 0.11.0rc2\r\n\r\nCloses #8 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