def __load_model(self,num_layers):
# Initial memory value for recurrence.
self.prev_mem = tf.zeros((self.train_batch_size, self.memory_dim))
# choose RNN/GRU/LSTM cell
with tf.variable_scope("train_test", reuse=True):
lstm = rnn_cell.LSTMCell(self.memory_dim)
# Stacks layers of RNN's to form a stacked decoder
self.cell = rnn_cell.MultiRNNCell([lstm] * num_layers)
# embedding model
if not self.attention:
with tf.variable_scope("train_test"):
self.dec_outputs, self.dec_memory = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("train_test", reuse = True):
self.dec_outputs_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
else:
with tf.variable_scope("train_test"):
self.dec_outputs, self.dec_memory = seq2seq.embedding_attention_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("train_test", reuse = True):
self.dec_outputs_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
def __load_model(self):
# Initial memory value for recurrence.
self.prev_mem = tf.zeros((self.train_batch_size, self.memory_dim))
# choose RNN/GRU/LSTM cell
with tf.variable_scope("train_test", reuse=True):
self.cell = rnn_cell.LSTMCell(self.memory_dim)
# embedding model
if not self.attention:
with tf.variable_scope("train_test"):
self.dec_outputs, self.dec_memory = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("train_test", reuse = True):
self.dec_outputs_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
else:
with tf.variable_scope("train_test"):
self.dec_outputs, self.dec_memory = seq2seq.embedding_attention_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("train_test", reuse = True):
self.dec_outputs_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp, self.dec_inp, self.cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
def __load_model(self, num_layers):
# Initial memory value for recurrence.
self.prev_mem = tf.zeros((self.train_batch_size, self.memory_dim))
# choose RNN/GRU/LSTM cell
with tf.variable_scope("forward"):
fw_single_cell = rnn_cell.GRUCell(self.memory_dim)
# Stacks layers of RNN's to form a stacked decoder
self.forward_cell = rnn_cell.MultiRNNCell([fw_single_cell] *
num_layers)
with tf.variable_scope("backward"):
bw_single_cell = rnn_cell.GRUCell(self.memory_dim)
# Stacks layers of RNN's to form a stacked decoder
self.backward_cell = rnn_cell.MultiRNNCell([bw_single_cell] *
num_layers)
# embedding model
if not self.attention:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse=True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse=True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
else:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse=True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse=True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
def __load_model(self, num_layers):
# Initial memory value for recurrence.
self.prev_mem = tf.zeros((self.train_batch_size, self.memory_dim))
# choose RNN/GRU/LSTM cell
with tf.variable_scope("forward"):
fw_single_cell = rnn_cell.GRUCell(self.memory_dim)
# Stacks layers of RNN's to form a stacked decoder
self.forward_cell = rnn_cell.MultiRNNCell([fw_single_cell] * num_layers)
with tf.variable_scope("backward"):
bw_single_cell = rnn_cell.GRUCell(self.memory_dim)
# Stacks layers of RNN's to form a stacked decoder
self.backward_cell = rnn_cell.MultiRNNCell([bw_single_cell] * num_layers)
# embedding model
if not self.attention:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse = True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse = True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
else:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse = True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse = True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
def __load_model(self):
# Initial memory value for recurrence.
self.prev_mem = tf.zeros((self.train_batch_size, self.memory_dim))
# choose RNN/GRU/LSTM cell
with tf.variable_scope("forward"):
self.forward_cell = rnn_cell.LSTMCell(self.memory_dim)
with tf.variable_scope("backward"):
self.backward_cell = rnn_cell.LSTMCell(self.memory_dim)
# embedding model
if not self.attention:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse=True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse=True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_rnn_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
else:
with tf.variable_scope("forward"):
self.dec_outputs_fwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("forward", reuse=True):
self.dec_outputs_fwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_fwd, self.dec_inp, self.forward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
with tf.variable_scope("backward"):
self.dec_outputs_bwd, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length)
with tf.variable_scope("backward", reuse=True):
self.dec_outputs_bwd_tst, _ = seq2seq.embedding_attention_seq2seq(\
self.enc_inp_bwd, self.dec_inp, self.backward_cell, \
self.vocab_size, self.vocab_size, self.seq_length, feed_previous=True)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
source_vocab_size,
target_vocab_size,
output_projection=output_projection,
feed_previous=do_decode)
def GRUSeq2Seq(enc_inp, dec_inp):
cell = rnn_cell.MultiRNNCell([rnn_cell.GRUCell(24)] * 2)
return seq2seq.embedding_attention_seq2seq(
enc_inp,
dec_inp,
cell,
classes,
classes,
output_projection=(w, b))
def testEmbeddingAttentionSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in xrange(2)]
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in xrange(3)]
cell = rnn_cell.BasicLSTMCell(2)
dec, mem = seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, 2, 5)
sess.run([tf.initialize_all_variables()])
res = sess.run(dec)
self.assertEqual(len(res), 3)
self.assertEqual(res[0].shape, (2, 5))
res = sess.run(mem)
self.assertEqual(len(res), 4)
self.assertEqual(res[0].shape, (2, 4))
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, 2, 5, output_projection=(w, b))
sess.run([tf.variables.initialize_all_variables()])
res = sess.run(dec)
self.assertEqual(len(res), 3)
self.assertEqual(res[0].shape, (2, 2))
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in xrange(3)]
tf.get_variable_scope().reuse_variables()
d1, _ = seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, 2, 5, feed_previous=True)
d2, _ = seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, 2, 5, feed_previous=True)
d3, _ = seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, 2, 5, feed_previous=tf.constant(True))
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs, cell, source_vocab_size,
target_vocab_size, output_projection=output_projection,
feed_previous=do_decode)
def GRUSeq2Seq(enc_inp, dec_inp):
cell = rnn_cell.MultiRNNCell([rnn_cell.GRUCell(24)] * 2)
return seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, classes, classes, output_projection=(w, b))
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode=False):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs, cell, vocab_size,
vocab_size, feed_previous=False)
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs, decoder_inputs,
cell, num_input_tokens,
num_target_tokens, output_projection=output_projection,
feed_previous=do_decode)