stared

Dref360

import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

simecek

bzamecnik

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 32, 10)        0                                            
____________________________________________________________________________________________________
lstm_1 (LSTM)                    (None, 32, 10)        840         input_1[0][0]                    
____________________________________________________________________________________________________
add_1 (Add)                      (None, 32, 10)        0           input_1[0][0]                    
                                                                   lstm_1[0][0]                     
____________________________________________________________________________________________________

0xjac

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

yxtay

# References
# - https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html
# - https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/word2vec/word2vec_basic.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math

tomokishii

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

kevinxhan

#We can check for Ctrl-C with KeyboardInterrupt exception as follows:

try:
    while True:
        print "Echo ", raw_input(">")
except KeyboardInterrupt:
    print "Good bye"

#When python process was killed, we will not get KeyboardInterrupt. But we can instead catch SIGTERM sent by kill command.
#In order to catch SIGTERM, we can do:

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without