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@@ -166,7 +166,7 @@ class Seq2SeqAttentionModel(object):
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hps.num_hidden,
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initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=113),
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state_is_tuple=False)
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- (emb_encoder_inputs, fw_state, _) = tf.nn.bidirectional_rnn(
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+ (emb_encoder_inputs, fw_state, _) = tf.contrib.rnn.static_bidirectional_rnn(
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cell_fw, cell_bw, emb_encoder_inputs, dtype=tf.float32,
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sequence_length=article_lens)
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encoder_outputs = emb_encoder_inputs
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@@ -200,7 +200,7 @@ class Seq2SeqAttentionModel(object):
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# During decoding, follow up _dec_in_state are fed from beam_search.
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# dec_out_state are stored by beam_search for next step feeding.
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initial_state_attention = (hps.mode == 'decode')
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- decoder_outputs, self._dec_out_state = tf.nn.seq2seq.attention_decoder(
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+ decoder_outputs, self._dec_out_state = tf.contrib.legacy_seq2seq.attention_decoder(
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emb_decoder_inputs, self._dec_in_state, self._enc_top_states,
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cell, num_heads=1, loop_function=loop_function,
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initial_state_attention=initial_state_attention)
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@@ -234,7 +234,7 @@ class Seq2SeqAttentionModel(object):
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self._loss = seq2seq_lib.sampled_sequence_loss(
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decoder_outputs, targets, loss_weights, sampled_loss_func)
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else:
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- self._loss = tf.nn.seq2seq.sequence_loss(
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+ self._loss = tf.contrib.legacy_seq2seq.sequence_loss(
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model_outputs, targets, loss_weights)
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tf.summary.scalar('loss', tf.minimum(12.0, self._loss))
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Neal Wu