hussius

"""
* Converts images to GGB (grayscale)
* Creates subsets for training and validation
* Adds columns to indicate training or validation (useful for analysis of the deployed model)
* Adds rotated images to the dataset
* Creates comma-separated CSV file
* Creates zip archive
"""

import pandas as pd

hussius

from pathlib import Path
import os
import sys

from fire import Fire
import numpy as np
import pandas as pd
from tqdm import tqdm

hussius

def read_single_cossmo_example(serialized_example, n_tissues=1, coord_sys='rna1'):
    """Decode a single COSSMO example
    
    coord_sys must be one of 'rna1' or 'dna0', if 'dna0' then an extra 'strand' field
    must exist in the tfrecord and is extracted.
    """

    assert coord_sys in ['dna0', 'rna1']

    context_features = {

hussius

1. Install appropriate version of the Tensorflow (Python) framework from https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html

In my case (Mac OS X 10.11), I did: 

- Get the .whl file (this is more likely to work than a direct pip install) 
wget https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.11.0-py3-none-any.whl

- Install using non-Anaconda pip
/usr/local/bin/pip3 install tensorflow-0.11.0-py3-none-any.whl_

hussius

library(tensorflow)

tf$reset_default_graph()

x_data <- runif(100, min=0, max=1)
y_data <- x_data * 0.1 + 0.3 + rnorm(n, mean=0, sd=0.025)

W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))
y <- W * x_data + b

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import sys
import gzip 

if len(sys.argv)<3:
    sys.exit("python sum_per_gene.py <cDNA FASTA file> <TPM table>")

ensg = {}

mapf = gzip.open(sys.argv[1])
ctr = 0

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args = commandArgs(trailingOnly=TRUE)
path=args[1]
files=Sys.glob(paste0(path,"/*/abundance.tsv"))
#print(files)
merge_two <- function(x,y){
	  #print(dim(x))
	  if ("tpm" %in% colnames(x)){
	     x_ <- x[,c(1,5)]
	     }
	   else{

hussius

import sys

f = open(sys.argv[1])

wts = [] # This will be a list of list of lists (=list of matrices) with weights
bias_in = [] # List of lists of bias values in the input part of each sublayer
bias_out = [] # List of lists of bias values in the output part of each sublayer
prefs = [] # Prefixes for file names

state = None # state can be None, 'weight', 'bias_in' or 'bias_out'

hussius

import sys

for line in open(sys.argv[1]):
    if 'layer' in line:
        fname = '_'.join(line.strip().split()) + '_activities.txt'
        outf = open(fname,'w')
    else:
        outf.write(line)

hussius

import tensorflow as tf
import numpy as np
import math
#import pandas as pd
#import sys

input = np.array([[2.0, 1.0, 1.0, 2.0],
                 [-2.0, 1.0, -1.0, 2.0],
                 [0.0, 1.0, 0.0, 2.0],
                 [0.0, -1.0, 0.0, -2.0],