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Commit 30580a35 authored by Andrew M Dai's avatar Andrew M Dai Committed by Ryan Sepassi
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Increase minimum TF version for DEFINE_enum and rename variable mappings for... 

Increase minimum TF version for DEFINE_enum and rename variable mappings for change to RNN variable names. (#4123)
parent fea6447a
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Showing with 121 additions and 149 deletions
research/maskgan/README.md
View file @ 30580a35
......@@ -5,7 +5,7 @@ ______*](https://arxiv.org/abs/1801.07736) published at ICLR 2018.
## Requirements
* TensorFlow >= v1.3
* TensorFlow >= v1.5
## Instructions
......
research/maskgan/model_utils/variable_mapping.py
View file @ 30580a35
......@@ -163,52 +163,48 @@ def rnn_zaremba(hparams, model):
if v.op.name == str(model) + '/rnn/embedding'
][0]
lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
# Dictionary mapping.
if model == 'gen':
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1,
'Model/softmax_w': softmax_w,
'Model/softmax_b': softmax_b
}
else:
if FLAGS.dis_share_embedding:
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1
}
else:
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1
}
return variable_mapping
......@@ -356,24 +352,20 @@ def gen_encoder_seq2seq(hparams):
if v.op.name == 'gen/encoder/rnn/embedding'
][0]
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -385,24 +377,24 @@ def gen_encoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
encoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
else:
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
return variable_mapping
......@@ -418,24 +410,20 @@ def gen_decoder_seq2seq(hparams):
if v.op.name == 'gen/decoder/rnn/embedding'
][0]
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
decoder_softmax_b = [
v for v in tf.trainable_variables()
......@@ -450,13 +438,13 @@ def gen_decoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
decoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1,
str(model_str) + '/softmax_b':
decoder_softmax_b
......@@ -487,34 +475,34 @@ def dis_fwd_bidirectional(hparams):
][0]
fw_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
fw_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
fw_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
fw_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.dis_share_embedding:
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': fw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': fw_lstm_b_1
}
else:
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': fw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': fw_lstm_b_1
}
return variable_mapping
......@@ -537,26 +525,26 @@ def dis_bwd_bidirectional(hparams):
# Backward Discriminator Elements.
bw_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
bw_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
bw_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
bw_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': bw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': bw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': bw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': bw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': bw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': bw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': bw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': bw_lstm_b_1
}
return variable_mapping
......@@ -576,24 +564,20 @@ def dis_encoder_seq2seq(hparams):
## Encoder forward variables.
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -602,13 +586,13 @@ def dis_encoder_seq2seq(hparams):
model_str = 'model'
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
return variable_mapping
......@@ -624,24 +608,20 @@ def dis_decoder_seq2seq(hparams):
if v.op.name == 'dis/decoder/rnn/embedding'
][0]
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -653,24 +633,24 @@ def dis_decoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
decoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1
}
else:
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1,
}
return variable_mapping
......@@ -688,24 +668,20 @@ def dis_seq2seq_vd(hparams):
## Encoder variables.
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
## Attention.
......@@ -721,43 +697,39 @@ def dis_seq2seq_vd(hparams):
## Decoder.
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
# Standard variable mappings.
variable_mapping = {
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1,
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1
}
......
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