skaae

#!/bin/sh
#
# stdout/stderr redirection
#PBS -N NAME_OF_JOB1
#PBS -o $PBS_JOBNAME.$PBS_JOBID.out
#PBS -e $PBS_JOBNAME.$PBS_JOBID.err
#PBS -l nodes=1:gpus=1
# Execute the job from  the  current  working  directory
cd $PBS_O_WORKDIR

skaae

--[[ A Confusion Matrix class

Example:

    conf = optim.ConfusionMatrix( {'cat','dog','person'} )   -- new matrix
    conf:zero()                                              -- reset matrix
    for i = 1,N do
        conf:add( neuralnet:forward(sample), label )         -- accumulate errors
    end
    print(conf)                                              -- print matrix

skaae

--[[ A Confusion Matrix class

Example:

    conf = optim.ConfusionMatrix( {'cat','dog','person'} )   -- new matrix
    conf:zero()                                              -- reset matrix
    for i = 1,N do
        conf:add( neuralnet:forward(sample), label )         -- accumulate errors
    end
    print(conf)                                              -- print matrix

skaae

class LSTMLayer(Layer):
    '''
    A long short-term memory (LSTM) layer.  Includes "peephole connections" and
    forget gate.  Based on the definition in [#graves2014generating]_, which is
    the current common definition.

    :references:
        .. [#graves2014generating] Alex Graves, "Generating Sequences With
            Recurrent Neural Networks".

skaae

class LSTMLayer(Layer):
    '''
    A long short-term memory (LSTM) layer.  Includes "peephole connections" and
    forget gate.  Based on the definition in [#graves2014generating]_, which is
    the current common definition.

    :references:
        .. [#graves2014generating] Alex Graves, "Generating Sequences With
            Recurrent Neural Networks".
    '''

skaae

class LSTMLayer(Layer):
    '''
    A long short-term memory (LSTM) layer.  Includes "peephole connections" and
    forget gate.  Based on the definition in [#graves2014generating]_, which is
    the current common definition. Gate names are taken from [#zaremba2014],
    figure 1.

    :references:
        .. [#graves2014generating] Alex Graves, "Generating Sequences With
            Recurrent Neural Networks".

skaae

class LSTMLayer(Layer):
    '''
    A long short-term memory (LSTM) layer.  Includes "peephole connections" and
    forget gate.  Based on the definition in [#graves2014generating]_, which is
    the current common definition. Gate names are taken from [#zaremba2014],
    figure 1.

    :references:
        .. [#graves2014generating] Alex Graves, "Generating Sequences With
            Recurrent Neural Networks".

skaae

class BidirectionalLSTMLayer(Layer):
    '''
    A long short-term memory (LSTM) layer.  Includes "peephole connections" and
    forget gate.  Based on the definition in [#graves2014generating]_, which is
    the current common definition. Gate names are taken from [#zaremba2014],
    figure 1.

    :references:
        .. [#graves2014generating] Alex Graves, "Generating Sequences With
            Recurrent Neural Networks".

skaae

Function profiling
==================
  Message: experiment.py:196
  Time in 12 calls to Function.__call__: 8.222084e+01s
  Time in Function.fn.__call__: 8.221798e+01s (99.997%)
  Time in thunks: 8.196062e+01s (99.684%)
  Total compile time: 2.509097e+02s
    Number of Apply nodes: 1214
    Theano Optimizer time: 1.675486e+02s
       Theano validate time: 1.319681e+00s

skaae

Function profiling
==================
  Message: experiment.py:196
  Time in 1 calls to Function.__call__: 7.953641e+00s
  Time in Function.fn.__call__: 7.953413e+00s (99.997%)
  Time in thunks: 7.929524e+00s (99.697%)
  Total compile time: 1.766550e+02s
    Number of Apply nodes: 1214
    Theano Optimizer time: 1.688680e+02s