def many2one_model_with_buckets(encoder_inputs_list, decoder_inputs, targets, weights,
buckets, seq2seq, softmax_loss_function=None,
per_example_loss=False, name=None, spscale=20):
# Modified model with buckets to accept 2 encoders
if len(encoder_inputs_list[0]) < buckets[-1][0]:
raise ValueError("Length of encoder_inputs (%d) must be at least that of la"
"st bucket (%d)." % (len(encoder_inputs), buckets[-1][0]))
if len(targets) < buckets[-1][1]:
raise ValueError("Length of targets (%d) must be at least that of last"
"bucket (%d)." % (len(targets), buckets[-1][1]))
if len(weights) < buckets[-1][1]:
raise ValueError("Length of weights (%d) must be at least that of last"
"bucket (%d)." % (len(weights), buckets[-1][1]))
all_inputs = encoder_inputs_list + decoder_inputs + targets + weights
losses = []
outputs = []
speech_buckets = [(x*spscale, y) for (x,y) in buckets]
with ops.op_scope(all_inputs, name, "many2one_model_with_buckets"):
for j, bucket in enumerate(buckets):
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True if j > 0 else None):
#bucket_outputs, _ = seq2seq(encoder_inputs[:bucket[0]], decoder_inputs[:bucket[1]])
x = encoder_inputs_list[0][:bucket[0]]
#print( x )
y = encoder_inputs_list[1][:speech_buckets[j][0]]
bucket_outputs, _ = seq2seq([x, y], decoder_inputs[:bucket[1]])
outputs.append(bucket_outputs)
if per_example_loss:
losses.append(sequence_loss_by_example(
outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
softmax_loss_function=softmax_loss_function))
else:
losses.append(sequence_loss(
outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
softmax_loss_function=softmax_loss_function))
return outputs, losses
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