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import tensorflow as tf
import numpy as np

class tnpg:
    def __init__(self, tf_arr):
        self.arr = tf_arr
        
    def __getitem__(self, idx):
        if any([type(x) not in [slice, tf.python.framework.ops.Tensor, list] for x in idx]):
            raise ValueError("Unsupported type (not slice, tensor or list)! "

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def load_experiment_backup(model_name):
    filename = 'h_backup/%s.pickle' % model_name
    if os.path.isfile(filename):
        with open(filename, 'rb') as handle:
            return pickle.load(handle)
    else:
        return Trials()
    
def update_experiment_backup(trials, model_name):
    with open('h_backup/%s.pickle' % model_name, 'wb') as f:

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from tqdm import tqdm_notebook as tqdm
from joblib import Parallel, delayed
import time

import random

def func(x):
    time.sleep(random.randint(1, 10))
    return x

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# example code for EC500 K1 / CS591 S2 Deep Learning (Spring 2017)

import tensorflow as tf
import numpy as np


def main_save():
    with tf.Graph().as_default() as g:
        with tf.variable_scope('to_save'):
            a = tf.get_variable('a_name', [100, 100])

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# based on https://github.com/google/seq2seq/blob/master/bin/tools/generate_beam_viz.py

# extracts probabilities and sequences from .npz file generated during beam search. 
# and pickles a list of the length n_samples that has beam_width most probable tuples 
# (path, logprob, prob) 
# where probs are scaled to 1. 

import numpy as np
import networkx as nx
import pickle