chrico-bu-uab

# Adapted from Jovian Lin's code (hyperlink unavailable)

import numpy as np
from scipy.special import expit
from enum import Enum
from typing import Union

np.seterr(divide='raise', over='raise', under='warn', invalid='raise')

logmax = np.log(np.finfo(np.float64).max)

jganzabal

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

asimihsan

• Base instance to launch
  • https://console.aws.amazon.com/ec2/home?region=us-west-2#launchAmi=ami-e1b93481
  • p2.xlarge
  • 32GB EBS mount
  • Configure security group to allow access to all for ports 22 and 8888
• References
  • http://expressionflow.com/2016/10/09/installing-tensorflow-on-an-aws-ec2-p2-gpu-instance/

ssh -i ~/.ssh/udacity-carnd.pem ubuntu@ec2-52-11-14-48.us-west-2.compute.amazonaws.com

giuseppebonaccorso

'''
Cifar-10 classification
Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information
See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information
'''
 
from __future__ import print_function

import numpy as np

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

rouseguy

%%time

# -*- coding: utf-8 -*-
'''An implementation of sequence to sequence learning for performing addition
Input: "535+61"
Output: "596"
Padding is handled by using a repeated sentinel character (space)
Input may optionally be inverted, shown to increase performance in many tasks in:
"Learning to Execute"
http://arxiv.org/abs/1410.4615

aaronpolhamus

n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_husky
n02096294 4 Australian_terrier
n02102040 5 English_springer
n02066245 6 grey_whale
n02509815 7 lesser_panda
n02124075 8 Egyptian_cat
n02417914 9 ibex
n02123394 10 Persian_cat

udibr

# 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,

evenv

# A simple cheat sheet of Spark Dataframe syntax
# Current for Spark 1.6.1

# import statements
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *

#creating dataframes
df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils

class DenoisingAutoencoder(object):

    """ Implementation of Denoising Autoencoders using TensorFlow.