def __init__(self, data, FLAGS):
super(Model, self).__init__(data, FLAGS)
encoder_embedding_size = 32 * 4
encoder_vocabulary_length = len(data.idx2word_history)
decoder_lstm_size = 16 * 2
decoder_embedding_size = 16 * 2
decoder_sequence_length = data.batch_actions.shape[2]
decoder_vocabulary_length = len(data.idx2word_action)
with tf.name_scope('data'):
batch_histories = tf.Variable(data.batch_histories,
name='histories',
trainable=False)
batch_actions = tf.Variable(data.batch_actions,
name='actions',
trainable=False)
histories = tf.gather(batch_histories, self.batch_idx)
actions = tf.gather(batch_actions, self.batch_idx)
with tf.name_scope('model'):
batch_size = tf.shape(histories)[0]
encoder_embedding = embedding(input=histories,
length=encoder_vocabulary_length,
size=encoder_embedding_size,
name='encoder_embedding')
with tf.name_scope("UtterancesEncoder"):
conv3 = encoder_embedding
# conv3 = conv2d(
# input=conv3,
# filter=[1, 3, encoder_embedding_size, encoder_embedding_size],
# name='conv_utt_size_3_layer_1'
# )
# conv_s3 = conv2d(
# input=conv_s3,
# filter=[1, 3, encoder_embedding_size, encoder_embedding_size],
# name='conv_utt_size_3_layer_2'
# )
# print(conv3)
# k = encoder_sequence_length
# mp_s3 = max_pool(conv_s3, ksize=[1, 1, k, 1], strides=[1, 1, k, 1])
# print(mp_s3)
# encoded_utterances = mp_s3
encoded_utterances = tf.reduce_max(conv3, [2], keep_dims=True)
with tf.name_scope("HistoryEncoder"):
conv3 = encoded_utterances
# conv3 = conv2d(
# input=conv3,
# filter=[3, 1, encoder_embedding_size, encoder_embedding_size],
# name='conv_hist_size_3_layer_1'
# )
# conv_s3 = conv2d(
# input=conv_s3,
# filter=[3, 1, encoder_embedding_size, encoder_embedding_size],
# name='conv_hist_size_3_layer_2'
# )
# print(conv3)
# k = encoder_sequence_length
# mp_s3 = max_pool(conv_s3, ksize=[1, 1, k, 1], strides=[1, 1, k, 1])
# print(mp_s3)
encoded_history = tf.reduce_max(conv3, [1, 2])
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=encoder_embedding_size,
# name='linear_projection_1'
# )
# encoded_history = tf.nn.relu(projection)
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=encoder_embedding_size,
# name='linear_projection_2'
# )
# encoded_history = tf.nn.relu(projection)
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=decoder_lstm_size * 2,
# name='linear_projection_3'
# )
# encoded_history = tf.nn.relu(projection)
with tf.name_scope("Decoder"):
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size +
encoder_embedding_size,
use_peepholes=True,
)
initial_state = cell.zero_state(batch_size, tf.float32)
# decode all histories along the utterance axis
final_encoder_state = encoded_history
decoder_states, decoder_outputs, decoder_outputs_softmax = rnn_decoder(
cell=cell,
inputs=[
actions[:, word]
for word in range(decoder_sequence_length)
],
static_input=final_encoder_state,
initial_state=initial_state, # final_encoder_state,
embedding_size=decoder_embedding_size,
embedding_length=decoder_vocabulary_length,
sequence_length=decoder_sequence_length,
name='RNNDecoder',
reuse=False,
use_inputs_prob=self.use_inputs_prob)
self.predictions = tf.concat(1, decoder_outputs_softmax)
if FLAGS.print_variables:
for v in tf.trainable_variables():
print(v.name)
with tf.name_scope('loss'):
one_hot_labels = dense_to_one_hot(actions,
decoder_vocabulary_length)
self.loss = tf.reduce_mean(
-one_hot_labels *
tf.log(tf.clip_by_value(self.predictions, 1e-10, 1.0)),
name='loss')
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2),
tf.argmax(self.predictions, 2))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction,
'float'))
tf.scalar_summary('accuracy', self.accuracy)
def __init__(self, data, FLAGS):
super(Model, self).__init__(data, FLAGS)
encoder_embedding_size = 16 * 2
encoder_lstm_size = 16
encoder_vocabulary_length = len(data.idx2word_history)
history_length = data.train_set['histories'].shape[1]
encoder_sequence_length = data.train_set['histories'].shape[2]
decoder_lstm_size = 16
decoder_embedding_size = 16
decoder_sequence_length = data.batch_actions.shape[2]
decoder_vocabulary_length = len(data.idx2word_action)
with tf.name_scope('data'):
batch_histories = tf.Variable(data.batch_histories,
name='histories',
trainable=False)
batch_actions = tf.Variable(data.batch_actions,
name='actions',
trainable=False)
histories = tf.gather(batch_histories, self.batch_idx)
actions = tf.gather(batch_actions, self.batch_idx)
# inference model
with tf.name_scope('model'):
batch_size = tf.shape(histories)[0]
encoder_embedding = embedding(input=histories,
length=encoder_vocabulary_length,
size=encoder_embedding_size,
name='encoder_embedding')
with tf.name_scope("UtterancesEncoder"):
with tf.name_scope("RNNForwardUtteranceEncoderCell_1"):
cell_fw_1 = LSTMCell(num_units=encoder_lstm_size,
input_size=encoder_embedding_size,
use_peepholes=True)
initial_state_fw_1 = cell_fw_1.zero_state(
batch_size, tf.float32)
with tf.name_scope("RNNBackwardUtteranceEncoderCell_1"):
cell_bw_1 = LSTMCell(num_units=encoder_lstm_size,
input_size=encoder_embedding_size,
use_peepholes=True)
initial_state_bw_1 = cell_bw_1.zero_state(
batch_size, tf.float32)
with tf.name_scope("RNNForwardUtteranceEncoderCell_2"):
cell_fw_2 = LSTMCell(num_units=encoder_lstm_size,
input_size=cell_fw_1.output_size +
cell_bw_1.output_size,
use_peepholes=True)
initial_state_fw_2 = cell_fw_2.zero_state(
batch_size, tf.float32)
# the input data has this dimensions
# [
# #batch,
# #utterance in a history (a dialogue),
# #word in an utterance (a sentence),
# embedding dimension
# ]
# encode all utterances along the word axis
encoder_states_2d = []
for utterance in range(history_length):
encoder_outputs, _ = brnn(
cell_fw=cell_fw_1,
cell_bw=cell_bw_1,
inputs=[
encoder_embedding[:, utterance, word, :]
for word in range(encoder_sequence_length)
],
initial_state_fw=initial_state_fw_1,
initial_state_bw=initial_state_bw_1,
name='RNNUtteranceBidirectionalLayer',
reuse=True if utterance > 0 else None)
_, encoder_states = rnn(
cell=cell_fw_2,
inputs=encoder_outputs,
initial_state=initial_state_fw_2,
name='RNNUtteranceForwardEncoder',
reuse=True if utterance > 0 else None)
# print(encoder_states[-1])
encoder_states = tf.concat(
1, tf.expand_dims(encoder_states[-1], 1))
# print(encoder_states)
encoder_states_2d.append(encoder_states)
encoder_states_2d = tf.concat(1, encoder_states_2d)
# print('encoder_states_2d', encoder_states_2d)
with tf.name_scope("HistoryEncoder"):
# encode all histories along the utterance axis
with tf.name_scope("RNNForwardHistoryEncoderCell_1"):
cell_fw_1 = LSTMCell(num_units=encoder_lstm_size,
input_size=cell_fw_2.state_size,
use_peepholes=True)
initial_state_fw_1 = cell_fw_1.zero_state(
batch_size, tf.float32)
with tf.name_scope("RNNBackwardHistoryEncoderCell_1"):
cell_bw_1 = LSTMCell(num_units=encoder_lstm_size,
input_size=cell_fw_2.state_size,
use_peepholes=True)
initial_state_bw_1 = cell_fw_2.zero_state(
batch_size, tf.float32)
with tf.name_scope("RNNForwardHistoryEncoderCell_2"):
cell_fw_2 = LSTMCell(num_units=encoder_lstm_size,
input_size=cell_fw_1.output_size +
cell_bw_1.output_size,
use_peepholes=True)
initial_state_fw_2 = cell_fw_2.zero_state(
batch_size, tf.float32)
encoder_outputs, _ = brnn(
cell_fw=cell_fw_1,
cell_bw=cell_bw_1,
inputs=[
encoder_states_2d[:, utterance, :]
for utterance in range(history_length)
],
initial_state_fw=initial_state_fw_1,
initial_state_bw=initial_state_bw_1,
name='RNNHistoryBidirectionalLayer',
reuse=None)
_, encoder_states = rnn(cell=cell_fw_2,
inputs=encoder_outputs,
initial_state=initial_state_fw_2,
name='RNNHistoryForwardEncoder',
reuse=None)
with tf.name_scope("Decoder"):
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size +
cell_fw_2.state_size,
use_peepholes=True,
)
initial_state = cell.zero_state(batch_size, tf.float32)
# decode all histories along the utterance axis
final_encoder_state = encoder_states[-1]
decoder_states, decoder_outputs, decoder_outputs_softmax = rnn_decoder(
cell=cell,
inputs=[
actions[:, word]
for word in range(decoder_sequence_length)
],
static_input=final_encoder_state,
initial_state=initial_state, #final_encoder_state,
embedding_size=decoder_embedding_size,
embedding_length=decoder_vocabulary_length,
sequence_length=decoder_sequence_length,
name='RNNDecoder',
reuse=False,
use_inputs_prob=self.use_inputs_prob)
self.predictions = tf.concat(1, decoder_outputs_softmax)
# print(p_o_i)
if FLAGS.print_variables:
for v in tf.trainable_variables():
print(v.name)
with tf.name_scope('loss'):
one_hot_labels = dense_to_one_hot(actions,
decoder_vocabulary_length)
self.loss = tf.reduce_mean(
-one_hot_labels *
tf.log(tf.clip_by_value(self.predictions, 1e-10, 1.0)),
name='loss')
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2),
tf.argmax(self.predictions, 2))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction,
'float'))
tf.scalar_summary('accuracy', self.accuracy)
def __init__(self, data, FLAGS):
super(Model, self).__init__(data, FLAGS)
encoder_embedding_size = 32 * 4
encoder_vocabulary_length = len(data.idx2word_history)
decoder_lstm_size = 16 * 2
decoder_embedding_size = 16 * 2
decoder_sequence_length = data.batch_actions.shape[2]
decoder_vocabulary_length = len(data.idx2word_action)
with tf.name_scope('data'):
batch_histories = tf.Variable(data.batch_histories, name='histories', trainable=False)
batch_actions = tf.Variable(data.batch_actions, name='actions', trainable=False)
histories = tf.gather(batch_histories, self.batch_idx)
actions = tf.gather(batch_actions, self.batch_idx)
with tf.name_scope('model'):
batch_size = tf.shape(histories)[0]
encoder_embedding = embedding(
input=histories,
length=encoder_vocabulary_length,
size=encoder_embedding_size,
name='encoder_embedding'
)
with tf.name_scope("UtterancesEncoder"):
conv3 = encoder_embedding
# conv3 = conv2d(
# input=conv3,
# filter=[1, 3, encoder_embedding_size, encoder_embedding_size],
# name='conv_utt_size_3_layer_1'
# )
# conv_s3 = conv2d(
# input=conv_s3,
# filter=[1, 3, encoder_embedding_size, encoder_embedding_size],
# name='conv_utt_size_3_layer_2'
# )
# print(conv3)
# k = encoder_sequence_length
# mp_s3 = max_pool(conv_s3, ksize=[1, 1, k, 1], strides=[1, 1, k, 1])
# print(mp_s3)
# encoded_utterances = mp_s3
encoded_utterances = tf.reduce_max(conv3, [2], keep_dims=True)
with tf.name_scope("HistoryEncoder"):
conv3 = encoded_utterances
# conv3 = conv2d(
# input=conv3,
# filter=[3, 1, encoder_embedding_size, encoder_embedding_size],
# name='conv_hist_size_3_layer_1'
# )
# conv_s3 = conv2d(
# input=conv_s3,
# filter=[3, 1, encoder_embedding_size, encoder_embedding_size],
# name='conv_hist_size_3_layer_2'
# )
# print(conv3)
# k = encoder_sequence_length
# mp_s3 = max_pool(conv_s3, ksize=[1, 1, k, 1], strides=[1, 1, k, 1])
# print(mp_s3)
encoded_history = tf.reduce_max(conv3, [1, 2])
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=encoder_embedding_size,
# name='linear_projection_1'
# )
# encoded_history = tf.nn.relu(projection)
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=encoder_embedding_size,
# name='linear_projection_2'
# )
# encoded_history = tf.nn.relu(projection)
# projection = linear(
# input=encoded_history,
# input_size=encoder_embedding_size,
# output_size=decoder_lstm_size * 2,
# name='linear_projection_3'
# )
# encoded_history = tf.nn.relu(projection)
with tf.name_scope("Decoder"):
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size + encoder_embedding_size,
use_peepholes=True,
)
initial_state = cell.zero_state(batch_size, tf.float32)
# decode all histories along the utterance axis
final_encoder_state = encoded_history
decoder_states, decoder_outputs, decoder_outputs_softmax = rnn_decoder(
cell=cell,
inputs=[actions[:, word] for word in range(decoder_sequence_length)],
static_input=final_encoder_state,
initial_state=initial_state, # final_encoder_state,
embedding_size=decoder_embedding_size,
embedding_length=decoder_vocabulary_length,
sequence_length=decoder_sequence_length,
name='RNNDecoder',
reuse=False,
use_inputs_prob=self.use_inputs_prob
)
self.predictions = tf.concat(1, decoder_outputs_softmax)
if FLAGS.print_variables:
for v in tf.trainable_variables():
print(v.name)
with tf.name_scope('loss'):
one_hot_labels = dense_to_one_hot(actions, decoder_vocabulary_length)
self.loss = tf.reduce_mean(- one_hot_labels * tf.log(tf.clip_by_value(self.predictions, 1e-10, 1.0)),
name='loss')
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(self.predictions, 2))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
tf.scalar_summary('accuracy', self.accuracy)
def __init__(self, data, FLAGS):
super(Model, self).__init__(data, FLAGS)
encoder_embedding_size = 16 * 2
encoder_lstm_size = 16
encoder_vocabulary_length = len(data.idx2word_history)
history_length = data.train_set['histories'].shape[1]
encoder_sequence_length = data.train_set['histories'].shape[2]
decoder_lstm_size = 16
decoder_embedding_size = 16
decoder_sequence_length = data.batch_actions.shape[2]
decoder_vocabulary_length = len(data.idx2word_action)
with tf.name_scope('data'):
batch_histories = tf.Variable(data.batch_histories, name='histories', trainable=False)
batch_actions = tf.Variable(data.batch_actions, name='actions', trainable=False)
histories = tf.gather(batch_histories, self.batch_idx)
actions = tf.gather(batch_actions, self.batch_idx)
# inference model
with tf.name_scope('model'):
batch_size = tf.shape(histories)[0]
encoder_embedding = embedding(
input=histories,
length=encoder_vocabulary_length,
size=encoder_embedding_size,
name='encoder_embedding'
)
with tf.name_scope("UtterancesEncoder"):
with tf.name_scope("RNNForwardUtteranceEncoderCell_1"):
cell_fw_1 = LSTMCell(
num_units=encoder_lstm_size,
input_size=encoder_embedding_size,
use_peepholes=True
)
initial_state_fw_1 = cell_fw_1.zero_state(batch_size, tf.float32)
with tf.name_scope("RNNBackwardUtteranceEncoderCell_1"):
cell_bw_1 = LSTMCell(
num_units=encoder_lstm_size,
input_size=encoder_embedding_size,
use_peepholes=True
)
initial_state_bw_1 = cell_bw_1.zero_state(batch_size, tf.float32)
with tf.name_scope("RNNForwardUtteranceEncoderCell_2"):
cell_fw_2 = LSTMCell(
num_units=encoder_lstm_size,
input_size=cell_fw_1.output_size + cell_bw_1.output_size,
use_peepholes=True
)
initial_state_fw_2 = cell_fw_2.zero_state(batch_size, tf.float32)
# the input data has this dimensions
# [
# #batch,
# #utterance in a history (a dialogue),
# #word in an utterance (a sentence),
# embedding dimension
# ]
# encode all utterances along the word axis
encoder_states_2d = []
for utterance in range(history_length):
encoder_outputs, _ = brnn(
cell_fw=cell_fw_1,
cell_bw=cell_bw_1,
inputs=[encoder_embedding[:, utterance, word, :] for word in
range(encoder_sequence_length)],
initial_state_fw=initial_state_fw_1,
initial_state_bw=initial_state_bw_1,
name='RNNUtteranceBidirectionalLayer',
reuse=True if utterance > 0 else None
)
_, encoder_states = rnn(
cell=cell_fw_2,
inputs=encoder_outputs,
initial_state=initial_state_fw_2,
name='RNNUtteranceForwardEncoder',
reuse=True if utterance > 0 else None
)
# print(encoder_states[-1])
encoder_states = tf.concat(1, tf.expand_dims(encoder_states[-1], 1))
# print(encoder_states)
encoder_states_2d.append(encoder_states)
encoder_states_2d = tf.concat(1, encoder_states_2d)
# print('encoder_states_2d', encoder_states_2d)
with tf.name_scope("HistoryEncoder"):
# encode all histories along the utterance axis
with tf.name_scope("RNNForwardHistoryEncoderCell_1"):
cell_fw_1 = LSTMCell(
num_units=encoder_lstm_size,
input_size=cell_fw_2.state_size,
use_peepholes=True
)
initial_state_fw_1 = cell_fw_1.zero_state(batch_size, tf.float32)
with tf.name_scope("RNNBackwardHistoryEncoderCell_1"):
cell_bw_1 = LSTMCell(
num_units=encoder_lstm_size,
input_size=cell_fw_2.state_size,
use_peepholes=True
)
initial_state_bw_1 = cell_fw_2.zero_state(batch_size, tf.float32)
with tf.name_scope("RNNForwardHistoryEncoderCell_2"):
cell_fw_2 = LSTMCell(
num_units=encoder_lstm_size,
input_size=cell_fw_1.output_size + cell_bw_1.output_size,
use_peepholes=True
)
initial_state_fw_2 = cell_fw_2.zero_state(batch_size, tf.float32)
encoder_outputs, _ = brnn(
cell_fw=cell_fw_1,
cell_bw=cell_bw_1,
inputs=[encoder_states_2d[:, utterance, :] for utterance in range(history_length)],
initial_state_fw=initial_state_fw_1,
initial_state_bw=initial_state_bw_1,
name='RNNHistoryBidirectionalLayer',
reuse=None
)
_, encoder_states = rnn(
cell=cell_fw_2,
inputs=encoder_outputs,
initial_state=initial_state_fw_2,
name='RNNHistoryForwardEncoder',
reuse=None
)
with tf.name_scope("Decoder"):
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size+cell_fw_2.state_size,
use_peepholes=True,
)
initial_state = cell.zero_state(batch_size, tf.float32)
# decode all histories along the utterance axis
final_encoder_state = encoder_states[-1]
decoder_states, decoder_outputs, decoder_outputs_softmax = rnn_decoder(
cell=cell,
inputs=[actions[:, word] for word in range(decoder_sequence_length)],
static_input=final_encoder_state,
initial_state=initial_state, #final_encoder_state,
embedding_size=decoder_embedding_size,
embedding_length=decoder_vocabulary_length,
sequence_length=decoder_sequence_length,
name='RNNDecoder',
reuse=False,
use_inputs_prob=self.use_inputs_prob
)
self.predictions = tf.concat(1, decoder_outputs_softmax)
# print(p_o_i)
if FLAGS.print_variables:
for v in tf.trainable_variables():
print(v.name)
with tf.name_scope('loss'):
one_hot_labels = dense_to_one_hot(actions, decoder_vocabulary_length)
self.loss = tf.reduce_mean(- one_hot_labels * tf.log(tf.clip_by_value(self.predictions, 1e-10, 1.0)), name='loss')
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(self.predictions, 2))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
tf.scalar_summary('accuracy', self.accuracy)
