ayushidalmia

from onmt.translate.Translator import Translator
from onmt.translate.Translation import TranslationBuilder
import onmt
import onmt.ModelConstructor
from utils import *
from parameters import *
import opts
import argparse
import torch
from onmt.io import IO

ayushidalmia

	def decoder(self,encoder_outputs,dropout,mode):
	
		cell, decoder_initial_state = self.decoder_cell(encoder_outputs,dropout,mode)
		if mode != "INFER":
		tgt_text = self.tgt_text
		if self.time_major:
				tgt_text = tf.transpose(self.tgt_text)
			inputs = tf.nn.embedding_lookup(self.tgt_embedding,tgt_text)	  
		helper = tf.contrib.seq2seq.TrainingHelper(inputs, self.tgt_sequence_length, time_major=True)	
		basic_decoder = tf.contrib.seq2seq.BasicDecoder(cell, helper, decoder_initial_state)

ayushidalmia

def load_initial_weights(self, session):
    """
    As the weights from http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/ come 
    as a dict of lists (e.g. weights['conv1'] is a list) and not as dict of 
    dicts (e.g. weights['conv1'] is a dict with keys 'weights' & 'biases') we
    need a special load function
    """
    
    # Load the weights into memory
    weights_dict = np.load(self.WEIGHTS_PATH, encoding = 'bytes').item()

ayushidalmia

bad argument #2 to '?' (index out of bound)
stack traceback:
        [C]: at 0x2b4f0352a7e0
        [C]: in function '__index'
        ./onmt/translate/Beam.lua:97: in function 'func'
        ./onmt/utils/Tensor.lua:12: in function 'recursiveApply'
        ./onmt/utils/Tensor.lua:7: in function '_indexState'
        ./onmt/translate/BeamSearcher.lua:171: in function '_retrieveHypothesis'
        ./onmt/translate/BeamSearcher.lua:230: in function '_completeHypotheses'
        ./onmt/translate/BeamSearcher.lua:74: in function 'search'