| # before filling the null keep track of them | |
| dfX['construction_year_missing'] = (dfX['construction_year']==0)*1 | |
| dates.append( 'construction_year_missing' ) # list of dates related fields | |
| # to fill missing dates, can use : mean, median or oldest | |
| mean_year = dfX[dfX['construction_year']>0]['construction_year'].mean() | |
| dfX.loc[dfX['construction_year']==0, 'construction_year'] = int(mean_year) |
| addToIndex = function (bucketName, docName, docContent, context) { | |
| var csd = new AWS.CloudSearchDomain({ | |
| endpoint: CS_NAME+'.'+SERVICES_REGION+'.cloudsearch.amazonaws.com', | |
| apiVersion: '2013-01-01' | |
| }); | |
| // see documentation at : | |
| // http://docs.aws.amazon.com/cloudsearch/latest/developerguide/preparing-data.html#creating-document-batches | |
| var jbatch = [ {"type": "add", |
WARNING : Python 2.7 is much easier to run with Caffe than Python 3.5
https://iqbalnaved.wordpress.com/2016/05/08/installing-caffe-and-pycaffe-on-ubuntu-14-04/ WARNING : Command line at step 2 is actually 2 lines WARNING : You dont have to use cmake for compiling, see berkeleyvision
http://caffe.berkeleyvision.org/install_apt.html WARNING : you probably dont need all the dependencies, especially the "manual" ones
| modelWRD = Sequential() | |
| # 1st layer is a dummy-permutation=identity to specify input shape | |
| modelWRD.add( Permute((1,), input_shape=(n_words,)) ) | |
| modelWRD.add( EMBEDDING ) | |
| modelWRD.add( Lambda( | |
| lambda x : K.sum(x,axis=1), # sum over words |
| if pretrained: | |
| EMBEDDING = Embedding(vocab_size, | |
| embedding_dimension, | |
| weights=[glove_embedding_matrix]) | |
| else: | |
| EMBEDDING = Embedding(vocab_size, | |
| embedding_dimension) | |
| fcn32model = fcn32_blank() | |
| fcn32shape = fcn32model.layers[-1].output_shape | |
| fcn32size = fcn32shape[2] # INFO: =32 when images are 512x512 | |
| sp4 = Convolution2D(21, 1, 1, | |
| border_mode='same', # INFO : border_mode does not matter for 1x1 | |
| activation=None, | |
| name='score_pool4') |