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import tensorflow as tf

data_arr = [
    {
        "img": np.random.randn(10, 30)
    },
    {
         "img": np.random.randn(10, 30)
    }
]

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# Skriv records fil med et sample og forsoeg at loade det igen med tf.data


import numpy as np
import tensorflow as tf

## WRITE SINGLE EXAMPLE
label = 1
FILEPATH = "TEST_RECORDS"

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(pytorch_build) sorson@hyperion:~$ git clone https://github.com/pytorch/pytorch.git
Cloning into 'pytorch'...
remote: Counting objects: 24898, done.
remote: Compressing objects: 100% (38/38), done.
remote: Total 24898 (delta 8), reused 0 (delta 0), pack-reused 24860
Receiving objects: 100% (24898/24898), 15.81 MiB | 7.68 MiB/s, done.
Resolving deltas: 100% (18207/18207), done.
Checking connectivity... done.
(pytorch_build) sorson@hyperion:~$ cd pytorch
(pytorch_build) sorson@hyperion:~/pytorch$ export CUDA_HOME=/usr/local/cuda

skaae

import numpy as np
import theano
import theano.tensor as T
from lasagne import nonlinearities
from lasagne import init
from lasagne.utils import unroll_scan

from lasagne.layers import MergeLayer, Layer, InputLayer, DenseLayer
from lasagne.layers import helper
from lasagne.layers import Gate

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def fasta_names_to_dict(fasta_filename):
handin6.py

    """Extract FASTA IDs from file to dictionary"""

    names_dict = {}

    fasta_file = open(fasta_filename)

    for line in fasta_file:

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def fasta_names_to_list(fasta_filename):
    '''Extract FASTA IDs from file to list'''
    
    names = []

    # Open fasta file
    fasta_file = open(fasta_filename)
        
    # Iterate over lines
    for line in fasta_file:

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def find_prot2(fasta_dict, re_str):
    '''Search through a dictionary of fasta entries using a regular expression'''

    # Import re module
    import re   # typically imported at top of module file

    # Create pattern object
    pattern = re.compile(re_str)          

    # Create empty list to store results

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def find_prot(fasta_dict, protein_name):
    '''Function to find protein in fasta_dictionary'''

    # Check if fasta_dict contains the protein name key
    if protein_name in fasta_dict:

        # Return biological sequence
        return fasta_dict[protein_name]

    else:

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def read_fasta(fasta_filename):
    '''Function to parse a fasta sequence file'''

    # Initialize dictionary
    fasta_dict = {}

    # Open file and iterate over lines
    for line in open(fasta_filename): 

        # Remove newline and other whitespace from end of line

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# Initialize column sums
column_sums = [0., 0.]

# Iterate over all rows
for row in row_list:
    # For each of the two column values, add to the corresponding sum
    column_sums[0] += row[0]
    column_sums[1] += row[1]

# Calculate the average by dividing by the length