zmjjmz

Keybase proof

I hereby claim:

• I am zmjjmz on github.
• I am zach (https://keybase.io/zach) on keybase.
• I have the public key with fingerprint 2184 B50B 782E 9A0F 0915  016B 4C67 75A0 5B06 0619

To claim this, I am signing this object:

zmjjmz

        a = np.cos(norm_lines[:,1])
        b = np.sin(norm_lines[:,1])
        x0 = a*norm_lines[:,0]
        y0 = b*norm_lines[:,0]
        norm_line_segs = np.vstack([x0-segment_length*b,
                                    y0+segment_length*a,
                                    x0+segment_length*b,
                                    y0-segment_length*a]).T
        norm_line_segs = np.vectorize(int)(norm_line_segs)

zmjjmz

        for line in norm_lines:
            a = np.cos(line[1])
            b = np.sin(line[1])        
            x0 = a*line[0]
            y0 = b*line[0]
            #print(a, b)
            #print(line[1]*180/np.pi)
            #line_func = lambda t: (x0 + b*t, y0 - a*t)
            #norm_line_seg = np.hstack([line_func(segment_length), line_func(-1*segment_length)])
            norm_line_seg = [x0 - segment_length*b,

zmjjmz

from __future__ import division

import os
import sys
import subprocess
import time

import numpy as np
import tensorflow
from tensorflow.python.tools import inspect_checkpoint

zmjjmz

 def export_keras_model_simple(model, export_dir, version, input_name_map={}, output_name_map={}, model_name=None, conflict_policy='fail',  
         exclude_outputs=[], verbose=True):                                                                                                 
     # only supports one input / one output models                                                                                          
     # version is supposed to be an uint!                                                                                                   
     model_format = 'keras'                                                                                                                 
     version_str = str(version)                                                                                                             
     if datautil.is_valid_path(model):                                                                                                      
         if verbose:                 

zmjjmz

def keras_avgpool_linear_pad_endtoend(word_map_emb_pair, pad_length,                          
                                      n_classes, random_seed, oov_thresh=0.9, embed_config={}, model_config={}):                                                                             
    # expect these in the order filter_embeddings returns them                                
    # nastyyy                                                                                 
    word_ind_map, embedding_mat = word_map_emb_pair                                           
    numpy.random.seed(random_seed)                                                            
                                                                                              
    inp = keras.layers.Input(shape=(1,), name='text', dtype='string')                         
                                                                                              
    # assume word_ind_map and embedding_mat has been fucked with according

zmjjmz

from  __future__ import division

import os
import sys
import subprocess
import time

import numpy as np
import tensorflow
from tensorflow.python.tools import inspect_checkpoint

zmjjmz

Using TensorFlow backend.
2017-12-20 14:02:35.782187: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
output from keras
[[[ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]]

 [[ 0.  0.  0.]

zmjjmz

Using TensorFlow backend.
2017-12-20 14:04:22.144670: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
output from keras
[[[ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]]

 [[ 0.  0.  0.]

zmjjmz

from __future__ import division

import urlparse
import os

import numpy
import boto3
import tensorflow
from tensorflow.python.keras._impl import keras
from tensorflow.python.estimator.export.export_output import PredictOutput