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  | import numpy as np | |
| import tensorflow as tf | |
| SIZE = int(64e6) | |
| def _bytes_feature(value): | |
| return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) | 
  
    
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  | export PYTHON_CONFIGURE_OPTS=--enable-framework | |
| export EDITOR=vim | |
| export CLICOLOR=1 | |
| export LANG=en_US.UTF-8 | 
  
    
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  | import tensorflow as tf | |
| # volume shape: H x W x D | |
| # cartesian notation (x,y): | |
| # (0,0) (1,0) (2,0) ... (W-1,0) | |
| # (0,1) (1,1) (2,1) ... (W-1,1) | |
| # index notation (i,j): | |
| # (0,0) (0,1) (0,2) ... (0,W-1) | 
  
    
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  | import math | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from matplotlib.widgets import Slider | |
| def show_volumes_projection(volumes): | |
| n = len(volumes) | 
  
    
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  | <html> | |
| <head></head> | |
| <body> | |
| <ul></ul> | |
| <script> | |
| const PARSER = 'https://raw.githubusercontent.com/ORCID/bibtexParseJs/master/bibtexParse.js' | |
| fetch(PARSER) | |
| .then(res => res.text()) | |
| .then(eval) | 
  
    
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  | name: "VGG_ILSVRC_16_layer" | |
| layer { | |
| name: "data" | |
| type: "DenseImageData" | |
| top: "data" | |
| top: "label" | |
| dense_image_data_param { | |
| source: "/SegNet/CamVid/train.txt" # Change this to the absolute path to your data file | |
| batch_size: 4 # Change this number to a batch size that will fit on your GPU | |
| shuffle: true | 
  
    
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  | name: "ResNet-101" | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 224 | |
| input_dim: 224 | |
| layer { | |
| bottom: "data" | |
| top: "conv1" | 
  
    
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  | name: "${NET_ID}" | |
| layer { | |
| name: "data" | |
| type: "ImageSegData" | |
| top: "data" | |
| top: "label" | |
| #top: "data_dim" | |
| include { | |
| phase: TRAIN |