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fix problems to work on tensorflow 1.12 #88

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fix problems to work on tensorflow 1.12 #88

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3 changes: 2 additions & 1 deletion src/model/seq2seq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,8 @@
from tensorflow.python.ops import nn_ops
from tensorflow.contrib.rnn.python.ops import rnn, rnn_cell
from tensorflow.python.ops import variable_scope
linear = rnn_cell._linear # pylint: disable=protected-access
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
linear = core_rnn_cell._linear # pylint: disable=protected-access

def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
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15 changes: 9 additions & 6 deletions src/model/seq2seq_model.py
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Expand Up @@ -84,22 +84,25 @@ def __init__(self, encoder_masks, encoder_inputs_tensor,
self.encoder_masks = encoder_masks

# Create the internal multi-layer cell for our RNN.
single_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(attn_num_hidden, forget_bias=0.0, state_is_tuple=False)

if use_gru:
print("using GRU CELL in decoder")
single_cell = tf.contrib.rnn.core_rnn_cell.GRUCell(attn_num_hidden)
cell = single_cell
single_cell = tf.contrib.rnn.GRUCell(attn_num_hidden)
else:
single_cell = tf.contrib.rnn.BasicLSTMCell(attn_num_hidden, forget_bias=0.0, state_is_tuple=False)

if attn_num_layers > 1:
cell = tf.contrib.rnn.core_rnn_cell.MultiRNNCell([single_cell] * attn_num_layers, state_is_tuple=False)
cell = tf.contrib.rnn.MultiRNNCell([single_cell] * attn_num_layers, state_is_tuple=False)
else:
cell = single_cell

# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(lstm_inputs, decoder_inputs, seq_length, do_decode):

num_hidden = attn_num_layers * attn_num_hidden
lstm_fw_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
# Backward direction cell
lstm_bw_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(num_hidden, forget_bias=0.0, state_is_tuple=False)

pre_encoder_inputs, output_state_fw, output_state_bw = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, lstm_inputs,
initial_state_fw=None, initial_state_bw=None,
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