Overfit jiqiujia
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| def architecture_string(layer): | |
| model_arch = '' | |
| for i, layer in enumerate(lasagne.layers.get_all_layers(layer)): | |
| name = string.ljust(layer.__class__.__name__, 28) | |
| model_arch += " %2i %s %s " % (i, name, | |
| lasagne.layers.get_output_shape(layer)) | |
| if hasattr(layer, 'filter_size'): | |
| model_arch += str(layer.filter_size[0]) |
jiqiujia
/ genSpectrogram.m
Created
October 16, 2016 13:41
generate the spectrogram of one dimensional signal
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| %%reference: http://www.mathworks.com/help/signal/ref/spectrogram.html | |
| dat = load('/media/dl/data1/kaggle_eeg_2016/train_1/1_1_1.mat'); | |
| dat = dat.dataStruct.data; | |
| dat = dat(:, 1); | |
| Nx = length(dat); %sample number | |
| nsc = floor(Nx/600); %number of signal sections | |
| nov = floor(nsc/2); %overlap between adajacent windows | |
| nff = max(256,2^nextpow2(nsc)); %number of samples to compute the FFT |
jiqiujia
/ pg-pong.py
Created
September 22, 2016 06:11
— forked from karpathy/pg-pong.py
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
jiqiujia
/ min-char-rnn.py
Created
July 29, 2016 06:34
— forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
jiqiujia
/ states_boundingbox.json
Created
July 15, 2014 06:32
— forked from Skarlit/states_boundingbox.json
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "Mississippi": [30.1477890014648, 34.9960556030273, -91.6550140380859, -88.0980072021484], | |
| "Oklahoma": [33.6191940307617, 37.0021362304688, -103.002571105957, -94.4312133789062], | |
| "Delaware": [38.4511260986328, 39.8394355773926, -75.7890472412109, -74.9846343994141], | |
| "Minnesota": [43.4994277954102, 49.3844909667969, -97.2392654418945, -89.4833831787109], | |
| "Illinois": [36.9701309204102, 42.5083045959473, -91.513053894043, -87.0199203491211], | |
| "Arkansas": [33.0041046142578, 36.4996032714844, -94.6178131103516, -89.6422424316406], | |
| "New Mexico": [31.3323001861572, 37.0001411437988, -109.050178527832, -103.000862121582], | |
| "Indiana": [37.7717399597168, 41.7613716125488, -88.0997085571289, -84.7845764160156], | |
| "Louisiana": [28.9210300445557, 33.019458770752, -94.0431518554688, -88.817008972168], |
jiqiujia
/ continental-usa-states-bb
Created
July 15, 2014 06:30
— forked from mishari/continental-usa-states-bb
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| [{"place_id":"97994878","licence":"Data \u00a9 OpenStreetMap contributors, ODbL 1.0. http:\/\/www.openstreetmap.org\/copyright","osm_type":"relation","osm_id":"161950","boundingbox":["30.1375217437744","35.0080299377441","-88.4731369018555","-84.8882446289062"],"lat":"33.2588817","lon":"-86.8295337","display_name":"Alabama, United States of America","place_rank":"8","category":"boundary","type":"administrative","importance":0.83507032450272,"icon":"http:\/\/nominatim.openstreetmap.org\/images\/mapicons\/poi_boundary_administrative.p.20.png"}] | |
| [{"place_id":"97421560","licence":"Data \u00a9 OpenStreetMap contributors, ODbL 1.0. http:\/\/www.openstreetmap.org\/copyright","osm_type":"relation","osm_id":"162018","boundingbox":["31.3321762084961","37.0042610168457","-114.818359375","-109.045196533203"],"lat":"34.395342","lon":"-111.7632755","display_name":"Arizona, United States of America","place_rank":"8","category":"boundary","type":"administrative","importance":0.83922181098242,"icon":"http:\/\/nominatim.openst |
NewerOlder