examples/lstm-chime.py
def batches(dataset):
    '''Returns a callable that chooses sequences from netcdf data.'''
    seq_lengths = dataset.variables['seqLengths'].data
    seq_begins = np.concatenate(([0], np.cumsum(seq_lengths)[:-1]))

    def sample():
        chosen = np.random.choice(
            list(range(len(seq_lengths))), BATCH_SIZE, replace=False)
        return batch_at(dataset.variables['inputs'].data,
                        dataset.variables['targetClasses'].data,
                        seq_begins[chosen],
                        seq_lengths[chosen])

    return sample
def batches(dataset):
    '''Returns a callable that chooses sequences from netcdf data.'''
    seq_lengths = dataset.variables['seqLengths'].data
    seq_begins = np.concatenate(([0], np.cumsum(seq_lengths)[:-1]))

    def sample():
        chosen = np.random.choice(
            list(range(len(seq_lengths))), BATCH_SIZE, replace=False)
        return batch_at(dataset.variables['inputs'].data,
                        dataset.variables['targetClasses'].data,
                        seq_begins[chosen],
                        seq_lengths[chosen])

    return sample
def batches ( dataset ) : seq_lengths = dataset . variables [ 'seqLengths' ] . data seq_begins = np . concatenate ( ( [ 0 ] , np . cumsum ( seq_lengths ) [ : - 1 ] ) ) def sample ( ) : chosen = np . random . choice ( list ( range ( len ( seq_lengths ) ) ) , BATCH_SIZE , replace = False ) return batch_at ( dataset . variables [ 'inputs' ] . data , dataset . variables [ 'targetClasses' ] . data , seq_begins [ chosen ] , seq_lengths [ chosen ] ) return sample
Returns a callable that chooses sequences from netcdf data.
Returns a callable that chooses sequences from netcdf data .