def __init__(self, output_dim, num_senses, num_hyps, use_attention=False, return_attention=False, **kwargs):
# Set output_dim in kwargs so that we can pass it along to LSTM's init
kwargs['output_dim'] = output_dim
self.num_senses = num_senses
self.num_hyps = num_hyps
self.use_attention = use_attention
self.return_attention = return_attention
super(OntoAttentionLSTM, self).__init__(**kwargs)
# Recurrent would have set the input shape to cause the input dim to be 3. Change it.
self.input_spec = [InputSpec(ndim=5)]
if self.consume_less == "cpu":
# In the LSTM implementation in Keras, consume_less = cpu causes all gates' inputs to be precomputed
# and stored in memory. However, this doesn't work with OntoLSTM since the input to the gates is
# dependent on the previous timestep's output.
warnings.warn("OntoLSTM does not support consume_less = cpu. Changing it to mem.")
self.consume_less = "mem"
#TODO: Remove this dependency.
if K.backend() == "tensorflow" and not self.unroll:
warnings.warn("OntoLSTM does not work with unroll=False when backend is TF. Changing it to True.")
self.unroll = True
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