{"action":"created","issue":{"url":"https://api.github.com/repos/alexjc/neural-doodle/issues/89","repository_url":"https://api.github.com/repos/alexjc/neural-doodle","labels_url":"https://api.github.com/repos/alexjc/neural-doodle/issues/89/labels{/name}","comments_url":"https://api.github.com/repos/alexjc/neural-doodle/issues/89/comments","events_url":"https://api.github.com/repos/alexjc/neural-doodle/issues/89/events","html_url":"https://github.com/alexjc/neural-doodle/issues/89","id":154806954,"number":89,"title":"Experiences with layerwise code in `forward` branch.","user":{"login":"alexjc","id":445208,"avatar_url":"https://avatars.githubusercontent.com/u/445208?v=3","gravatar_id":"","url":"https://api.github.com/users/alexjc","html_url":"https://github.com/alexjc","followers_url":"https://api.github.com/users/alexjc/followers","following_url":"https://api.github.com/users/alexjc/following{/other_user}","gists_url":"https://api.github.com/users/alexjc/gists{/gist_id}","starred_url":"https://api.github.com/users/alexjc/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alexjc/subscriptions","organizations_url":"https://api.github.com/users/alexjc/orgs","repos_url":"https://api.github.com/users/alexjc/repos","events_url":"https://api.github.com/users/alexjc/events{/privacy}","received_events_url":"https://api.github.com/users/alexjc/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/alexjc/neural-doodle/labels/advice","name":"advice","color":"9319e7"}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":59,"created_at":"2016-05-13T21:49:15Z","updated_at":"2016-07-05T18:14:07Z","closed_at":null,"body":"If you're using the new code, feel free to post reports and experiences here.\r\n\r\n* You need to **download the new model**; it's bigger but goes all the way to `6_1`.\r\n* When specifying layers, **always use `*_1`**. The others aren't trained to match up.\r\n* You must order the layers in decreasing order, e.g. `5_1 4_1 3_1`. I'll add an assert.\r\n* `--frames` will save your images to disk. Logging happens every iteration by default.\r\n\r\nThese are my command lines so far. See [my Twitter](https://twitter.com/alexjc) for the resulting images.\r\n\r\n**Portraits** (352x512)\r\n\r\n```python3.5 doodle.py --content samples/Seth.jpg --style samples/Gogh.jpg --semantic-weight=5.0 --variety 0.1 0.0 --balance 0.8 1.0 --layers 4_1 3_1 --iterations 6 6```\r\n\r\n**Doodles** (1024x640)\r\n\r\n```python3.5 doodle.py --output samples/Landscape.png --style samples/Renoir.jpg --semantic-weight=3.0 --iterations 6 4 2 --variety 0.6 0.6 0.6 --layers 6_1 5_1 4_1 --shapes 3 3 3 --balance 1.0 1.0 1.0```\r\n\r\n**Style Transfer** (1152x736)\r\n\r\n```python3 doodle.py --content samples/Cezanne_paint.jpg --style samples/Cezanne_sketch.png --device=cpu --iterations 4 3 2 --layers 5_1 4_1 3_1 --shapes 3 3 2 --variety 0.5 1.0 0.0 --balance 0.75 0.70 0.65```\r\n\r\n**Texture Synthesis** (1024x1024)\r\n\r\n```python3.5 doodle.py --style textures/Stones.jpg --output-size=1024x1024 --layers 6_1 5_1 4_1 3_1 --iterations 10 8 6 2 --variety 0.8 0.4 0.1 0.0 --slices=32```"},"comment":{"url":"https://api.github.com/repos/alexjc/neural-doodle/issues/comments/230558041","html_url":"https://github.com/alexjc/neural-doodle/issues/89#issuecomment-230558041","issue_url":"https://api.github.com/repos/alexjc/neural-doodle/issues/89","id":230558041,"user":{"login":"htoyryla","id":15064373,"avatar_url":"https://avatars.githubusercontent.com/u/15064373?v=3","gravatar_id":"","url":"https://api.github.com/users/htoyryla","html_url":"https://github.com/htoyryla","followers_url":"https://api.github.com/users/htoyryla/followers","following_url":"https://api.github.com/users/htoyryla/following{/other_user}","gists_url":"https://api.github.com/users/htoyryla/gists{/gist_id}","starred_url":"https://api.github.com/users/htoyryla/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/htoyryla/subscriptions","organizations_url":"https://api.github.com/users/htoyryla/orgs","repos_url":"https://api.github.com/users/htoyryla/repos","events_url":"https://api.github.com/users/htoyryla/events{/privacy}","received_events_url":"https://api.github.com/users/htoyryla/received_events","type":"User","site_admin":false},"created_at":"2016-07-05T18:14:07Z","updated_at":"2016-07-05T18:14:07Z","body":"OK, I got Deconv2DLayer working, but then I get this (which I have seen before today/yesterday already):\r\n\r\n```\r\npython3 doodle.py --content vastarakki.png --style viiva2.png --device=cpu --output koe8377.png --layers  4_1 3_1 --variety 0.11 0.1 --content-weight 0.13 0.06 --iterations 3 2 --shapes 2 2 --noise-weight 0.04 0.02 --previous-weight 0.1\r\n\r\n\r\nHigh-quality image synthesis powered by Deep Learning!\r\n  - Code licensed as AGPLv3, models under CC BY-NC-SA.\r\n  - Using the device `cpu` for heavy computation.\r\n\r\nStyle 768x1024 at scale 1.0\r\n  - Loading `viiva2.png` for style data.\r\n  - Layer 4_1 as (12065, 258) patches (2, 2) in 12,451kb.\r\n  - Layer 3_1 as (48705, 130) patches (2, 2) in 25,326kb.\r\n\r\nContent 960x720 at scale 1.0\r\n  - Loading `vastarakki.png` for content data.\r\n  - Layer 4_1 as (258, 90, 120) array in 2,786kb.\r\n  - Layer 3_1 as (130, 180, 240) array in 5,616kb.\r\n\r\nPhase 4_1: variety 0.11, weights c=0.1 p=0.1 n=0.0, iterations 3.\r\nTraceback (most recent call last):\r\n  File \"doodle.py\", line 582, in <module>\r\n    generator.run()\r\n  File \"doodle.py\", line 573, in run\r\n    Xn = self.evaluate((self.content_img[0] + 1.0) * 127.5)\r\n  File \"doodle.py\", line 528, in evaluate\r\n    best_idx, best_val = self.evaluate_slices(desired_feature, l, variety)\r\n  File \"doodle.py\", line 485, in evaluate_slices\r\n    layer.W.set_value(weights)\r\n  File \"/usr/local/lib/python3.4/dist-packages/theano/compile/sharedvalue.py\", line 127, in set_value\r\n    self.container.value = copy.deepcopy(new_value)\r\n  File \"/usr/local/lib/python3.4/dist-packages/theano/gof/link.py\", line 468, in __set__\r\n    self.storage[0] = self.type.filter(value, **kwargs)\r\n  File \"/usr/local/lib/python3.4/dist-packages/theano/tensor/type.py\", line 194, in filter\r\n    \" dimension.\", data.shape, self.broadcastable)\r\nTypeError: ('Non-unit value on shape on a broadcastable dimension.', (6032, 258, 2, 2), (True, False, False, False), 'Container name \"None\"')\r\n```\r\n\r\nThe versions are:\r\n\r\n```\r\nCython==0.24\r\nLasagne==0.2.dev1\r\nPillow==3.3.0\r\nTheano==0.8.2\r\ncolorama==0.3.7\r\nscikit-learn==0.17.1\r\nscipy==0.17.1\r\n```\r\n"}}
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