kastnerkyle

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

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# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

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"""
Maxout implementation in Theano
"""

# W,b        - Parameters in neural network layer
# activation - Activation function
# maxoutsize - Number of input neurons to maxout units

# Output activation function
output = activation(T.dot(input,W) + b)

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# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

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#!/usr/bin/env python                                                                                                                                                                     
# gpu_stat.py [DELAY [COUNT]]                                                                                                                                                             
# dump gpu stats as a line of json                                                                                                                                                        
# {"time": 1474168378.146957, "pci_tx": 146000, "pci_rx": 1508000,                                                                                                                        
#     "gpu_util": 42, "mem_util": 24, "mem_used": 11710,                                                                                                                                  
#     "temp": 76, "fan_speed": 44 }                                                      

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# author: Aaditya Prakash

# NVIDIA-SMI does not show the full command, and when it was launched and its RAM usage.
# PS does but it does but you need PIDs for that
# lsof /dev/nvidia gives PIDs but only for the user invoking it

# usage:
#        python programs_on_gpu.py

# Sample Output

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from run_merlin import prepare_file_path_list, read_file_list
from io_funcs.binary_io import BinaryIOCollection
import numpy
import h5py
import pickle
from fuel.datasets.hdf5 import H5PYDataset

io_fun = BinaryIOCollection()
n_outs = 63  # 187

kastnerkyle

#!/usr/bin/env python
# Based on code from Laurent Dinh (laurent-dinh)
# Author: Kyle Kastner
# License: BSD 3-Clause

import requests
import time
import random
from bs4 import BeautifulSoup
import urllib