JoostvDoorn

Simple test to figure out the format of hiddenOutput of cudnn LSTM, see the file below.

hiddenOutput of normal LSTM

(1,.,.) =
  0.3042  0.3042  0.3042
[torch.CudaTensor of size 1x1x3]

hiddenOutput of bidirection LSTM

JoostvDoorn

--
-- Author: Joost van Doorn <joost.vandoorn@student.uva.nl>
--
require 'rnn'
require 'cudnn'

function toCudnnLSTM(seqLSTM)
   local rnn = cudnn.LSTM(seqLSTM.inputsize, seqLSTM.outputsize, 1)
   local H, R, D = seqLSTM.outputsize, seqLSTM.outputsize, seqLSTM.inputsize
   local biases = rnn:biases()

JoostvDoorn

-- Simple Sentence Similarity Example with Siamese Encoder
-- Author: Joost van Doorn (joostvandoorn.com)
require 'nn'
require 'rnn'
require 'io'
local pl = require 'pl'
local dl = require 'dataload'

-- Options
local opt = {}

JoostvDoorn

0.023262095961369
0.043782837063209
0.092482087101952
0.18747380309514
0.36381674030978
0.70580721911491
1.1544929836985
1.7231447811158
2.396492313429
2.9533659130322

JoostvDoorn

Copyright (c) 2016, Joost van Doorn
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source caode must retain the above copyright notice, this
   list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation

JoostvDoorn

------------------------------------------------------------------------
--[[ SequencerCriterion ]]--
-- Applies a criterion to each of the inputs and targets in the 
-- corresponding input and target Tables.
-- Useful for nn.Repeater and nn.Sequencer.
-- WARNING : assumes that the decorated criterion is stateless, i.e. 
-- the backward doesn't need to be preceded by a commensurate forward.
------------------------------------------------------------------------
local SequencerCriterion, parent = torch.class('nn.SequencerCriterion', 'nn.Criterion')

JoostvDoorn

--[[

Example of "coupled" separate encoder and decoder networks using cudnn, e.g. for sequence-to-sequence networks.

]]--

require 'rnn'
require 'cunn'
require 'cudnn'

JoostvDoorn

local rnntest = {}
local precision = 1e-5
local mytester
local benchmark = false

local makeOldRecurrent_isdone = false


function rnntest.encoderdecoder()
   torch.manualSeed(123)

JoostvDoorn

package Test;

import static io.grpc.stub.ClientCalls.asyncUnaryCall;
import static io.grpc.stub.ClientCalls.asyncServerStreamingCall;
import static io.grpc.stub.ClientCalls.asyncClientStreamingCall;
import static io.grpc.stub.ClientCalls.asyncBidiStreamingCall;
import static io.grpc.stub.ClientCalls.blockingUnaryCall;
import static io.grpc.stub.ClientCalls.blockingServerStreamingCall;
import static io.grpc.stub.ClientCalls.futureUnaryCall;
import static io.grpc.MethodDescriptor.generateFullMethodName;

JoostvDoorn

package org.deeplearning4j.examples.regression

import org.canova.api.records.reader.RecordReader
import org.canova.api.records.reader.impl.CSVRecordReader
import org.canova.api.split.FileSplit
import org.deeplearning4j.datasets.canova.RecordReaderDataSetIterator
import org.deeplearning4j.datasets.iterator.DataSetIterator
import org.deeplearning4j.nn.api.OptimizationAlgorithm
import org.deeplearning4j.nn.conf.{ MultiLayerConfiguration, NeuralNetConfiguration }
import org.deeplearning4j.nn.conf.Updater