broken state reader sequence rework

def get_vocabulary(filename):

    with open(outputPath + filename + '_vocab' + config.reader.export_data_type, 'r') as outfile:

        return jsonlib.load(outfile)

def get_sequences(filename):

    with open(outputPath + filename + '_seqence' + config.reader.export_data_type, 'r') as outfile:

        return jsonlib.load(outfile)

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# lstm folder level init

from lstm import lstm_main

from lstm import lstm_train

from lstm import helpers

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    max_batches = 300

    batches_in_epoch = 10

    batch_size = 10

    PAD = 0

    EOS = 1

'''

import numpy as np

def batch(inputs, max_sequence_length=None):

    """

    Args:

        inputs:

            list of sentences (integer lists)

        max_sequence_length:

            integer specifying how large should `max_time` dimension be.

            If None, maximum sequence length would be used

    Outputs:

        inputs_time_major:

            input sentences transformed into time-major matrix

            (shape [max_time, batch_size]) padded with 0s

        sequence_lengths:

            batch-sized list of integers specifying amount of active

            time steps in each input sequence

    """

    sequence_lengths = [len(seq) for seq in inputs]

    batch_size = len(inputs)

    if max_sequence_length is None:

        max_sequence_length = max(sequence_lengths)

    inputs_batch_major = np.zeros(shape=[batch_size, max_sequence_length], dtype=np.int32) # == PAD

    for i, seq in enumerate(inputs):

        for j, element in enumerate(seq):

            inputs_batch_major[i, j] = element

    # [batch_size, max_time] -> [max_time, batch_size]

    inputs_time_major = inputs_batch_major.swapaxes(0, 1)

    return inputs_time_major, sequence_lengths

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        reader.basic_text_reader()

    if(config.trainer.train_on):

        trainer.train()

        trainer()

# class arguments