def connectionist_temporal_classification(x, t, blank_symbol, input_length=None, label_length=None):
 if (not isinstance(x, collections.Sequence)):
  raise TypeError('x must be a list of Variables')
 if (not isinstance(blank_symbol, int)):
  raise TypeError('blank_symbol must be non-negative integer.')
 assert (blank_symbol >= 0)
 assert (blank_symbol < x[0].shape[1])
 assert (len(x[0].shape) == 2)
 if (input_length is None):
  xp = cuda.get_array_module(x[0].data)
  input_length = chainer.Variable(xp.full((len(x[0].data),), len(x), dtype=numpy.int32), volatile='auto')
  label_length = chainer.Variable(xp.full((len(t.data),), len(t.data[0]), dtype=numpy.int32), volatile='auto')
 return ConnectionistTemporalClassification(blank_symbol)(input_length, label_length, t, *x)

def connectionist_temporal_classification ( x , t , blank_symbol , input_length = None , label_length = None ) : if ( not isinstance ( x , collections . Sequence ) ) : raise TypeError ( 'x must be a list of Variables' ) if ( not isinstance ( blank_symbol , int ) ) : raise TypeError ( 'blank_symbol must be non-negative integer.' ) assert ( blank_symbol >= 0 ) assert ( blank_symbol < x [ 0 ] . shape [ 1 ] ) assert ( len ( x [ 0 ] . shape ) == 2 ) if ( input_length is None ) : xp = cuda . get_array_module ( x [ 0 ] . data ) input_length = chainer . Variable ( xp . full ( ( len ( x [ 0 ] . data ) , ) , len ( x ) , dtype = numpy . int32 ) , volatile = 'auto' ) label_length = chainer . Variable ( xp . full ( ( len ( t . data ) , ) , len ( t . data [ 0 ] ) , dtype = numpy . int32 ) , volatile = 'auto' ) return ConnectionistTemporalClassification ( blank_symbol ) ( input_length , label_length , t , * x )
connectionist temporal classification loss function .