def train(train_set, test_set, idx2word_history, word2idx_history, idx2word_target, word2idx_target):
with tf.variable_scope("history_length"):
history_length = train_set['features'].shape[1]
encoder_lstm_size = 16*4
encoder_embedding_size = 16*8
encoder_vocabulary_length = len(idx2word_history)
with tf.variable_scope("encoder_sequence_length"):
encoder_sequence_length = train_set['features'].shape[2]
decoder_lstm_size = 16*4
decoder_embedding_size = 16*4
decoder_vocabulary_length = len(idx2word_target)
with tf.variable_scope("decoder_sequence_length"):
decoder_sequence_length = train_set['targets'].shape[1]
# inference model
with tf.name_scope('model'):
features = tf.placeholder("int32", name='features')
targets = tf.placeholder("int32", name='true_targets')
use_dropout_prob = tf.placeholder("float32", name='use_dropout_prob')
with tf.variable_scope("batch_size"):
batch_size = tf.shape(features)[0]
encoder_embedding = embedding(
input=features,
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("RNNFrowardHistoryEncoderCell_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("RNNFrowardHistoryEncoderCell_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"):
use_inputs_prob = tf.Variable(1.0, name='use_inputs_prob', trainable=False)
use_inputs_prob_decay_op = use_inputs_prob.assign(use_inputs_prob * FLAGS.use_inputs_prob_decay)
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size,
use_peepholes=True,
)
# 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=[targets[:, word] for word in range(decoder_sequence_length)],
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=use_inputs_prob
)
targets_give_features = 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(targets, decoder_vocabulary_length)
loss = tf.reduce_mean(- one_hot_labels * tf.log(targets_give_features), name='loss')
for v in tf.trainable_variables():
for n in ['/W_', '/W:', '/B:']:
if n in v.name:
print('Regularization using', v.name)
loss += FLAGS.regularization * tf.reduce_mean(tf.pow(v, 2))
tf.scalar_summary('loss', loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(targets_give_features, 2))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
tf.scalar_summary('accuracy', accuracy)
# with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
with tf.Session() as sess:
# Merge all the summaries and write them out to ./log
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('./log', sess.graph_def)
saver = tf.train.Saver()
# training
tvars = tf.trainable_variables()
# tvars = [v for v in tvars if 'embedding_table' not in v.name] # all variables except embeddings
learning_rate = tf.Variable(float(FLAGS.learning_rate), trainable=False)
# train_op = tf.train.GradientDescentOptimizer(
train_op = AdamPlusOptimizer(
learning_rate=learning_rate,
beta1=FLAGS.beta1,
beta2=FLAGS.beta2,
epsilon=FLAGS.epsilon,
pow=FLAGS.pow,
use_locking=False,
name='trainer')
learning_rate_decay_op = learning_rate.assign(learning_rate * FLAGS.decay)
global_step = tf.Variable(0, trainable=False)
gradients = tf.gradients(loss, tvars)
clipped_gradients, _ = tf.clip_by_global_norm(gradients, FLAGS.max_gradient_norm)
train_op = train_op.apply_gradients(zip(clipped_gradients, tvars), global_step=global_step)
tf.initialize_all_variables().run()
# prepare batch indexes
train_set_size = train_set['features'].shape[0]
print('Train set size:', train_set_size)
batch_size = FLAGS.batch_size
print('Batch size:', batch_size)
batch_indexes = [[i, i + batch_size] for i in range(0, train_set_size, batch_size)]
print('#Batches:', len(batch_indexes))
# print('Batch indexes', batch_indexes)
previous_accuracies = []
previous_losses = []
for epoch in range(FLAGS.max_epochs):
print('Batch: ', end=' ', flush=True)
for b, batch in enumerate(batch_indexes):
print(b, end=' ', flush=True)
sess.run(
train_op,
feed_dict={
features: train_set['features'][batch[0]:batch[1]],
targets: train_set['targets'][batch[0]:batch[1]],
}
)
print()
shuffle(batch_indexes)
if epoch % max(min(int(FLAGS.max_epochs / 100), 100), 1) == 0:
summary, lss, acc = sess.run([merged, loss, accuracy],
feed_dict={features: test_set['features'], targets: test_set['targets']})
writer.add_summary(summary, epoch)
print()
print('Epoch: {epoch}'.format(epoch=epoch))
print(' - accuracy = {acc:f}'.format(acc=acc))
print(' - loss = {lss:f}'.format(lss=lss))
print(' - learning rate = {lr:f}'.format(lr=learning_rate.eval()))
print(' - use inputs prob = {uip:f}'.format(uip=use_inputs_prob.eval()))
print()
# decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and lss > max(previous_losses[-3:]):
sess.run(learning_rate_decay_op)
previous_losses.append(lss)
# stop when reached a threshold maximum or when no improvement in the last 20 steps
previous_accuracies.append(acc)
if acc > 0.9999 or max(previous_accuracies) > max(previous_accuracies[-20:]):
break
sess.run(use_inputs_prob_decay_op)
save_path = saver.save(sess, ".rnn-model.ckpt")
print()
print("Model saved in file: %s" % save_path)
print()
# print('Test features')
# print(test_set['features'])
# print('Test targets')
print('Shape of targets:', test_set['targets'].shape)
# print(test_set['targets'])
print('Predictions')
targets_give_features = sess.run(targets_give_features,
feed_dict={features: test_set['features'], targets: test_set['targets']})
targets_given_features_argmax = np.argmax(targets_give_features, 2)
print('Shape of predictions:', targets_give_features.shape)
print('Argmax predictions')
# print(p_o_i_argmax)
print()
for features in range(0, targets_given_features_argmax.shape[0], max(int(targets_given_features_argmax.shape[0]/10), 1)):
print('History', features)
for j in range(test_set['features'].shape[1]):
utterance = []
for k in range(test_set['features'].shape[2]):
w = idx2word_history[test_set['features'][features, j, k]]
if w not in ['_SOS_', '_EOS_']:
utterance.append(w)
print('U {j}: {c:80}'.format(j=j, c=' '.join(utterance)))
prediction = []
for j in range(targets_given_features_argmax.shape[1]):
w = idx2word_target[targets_given_features_argmax[features, j]]
if w not in ['_SOS_', '_EOS_']:
prediction.append(w)
print('P : {t:80}'.format(t=' '.join(prediction)))
target = []
for j in range(test_set['targets'].shape[1]):
w = idx2word_target[test_set['targets'][features, j]]
if w not in ['_SOS_', '_EOS_']:
target.append(w)
print('T : {t:80}'.format(t=' '.join(target)))
print()
def __init__(self, data, FLAGS):
with tf.variable_scope("history_length"):
history_length = data.train_set['features'].shape[1]
encoder_embedding_size = 32 * 4
encoder_vocabulary_length = len(data.idx2word_history)
with tf.variable_scope("encoder_sequence_length"):
encoder_sequence_length = data.train_set['features'].shape[2]
decoder_lstm_size = 16 * 2
decoder_embedding_size = 16 * 2
decoder_vocabulary_length = len(data.idx2word_target)
with tf.variable_scope("decoder_sequence_length"):
decoder_sequence_length = data.train_set['targets'].shape[1]
# inference model
with tf.name_scope('model'):
features = tf.placeholder("int32", name='features')
targets = tf.placeholder("int32", name='true_targets')
use_dropout_prob = tf.placeholder("float32", name='use_dropout_prob')
with tf.variable_scope("batch_size"):
batch_size = tf.shape(features)[0]
encoder_embedding = embedding(
input=features,
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"):
use_inputs_prob = tf.placeholder("float32", name='use_inputs_prob')
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=[targets[:, 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=use_inputs_prob
)
targets_given_features = 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(targets, decoder_vocabulary_length)
loss = tf.reduce_mean(- one_hot_labels * tf.log(targets_given_features), name='loss')
for v in tf.trainable_variables():
for n in ['/W_', '/W:', '/B:']:
if n in v.name:
print('Regularization using', v.name)
loss += FLAGS.regularization * tf.reduce_mean(tf.pow(v, 2))
tf.scalar_summary('loss', loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(targets_given_features, 2))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
tf.scalar_summary('accuracy', accuracy)
self.data = data
self.train_set = data.train_set
self.test_set = data.test_set
self.idx2word_history = data.idx2word_history
self.word2idx_history = data.word2idx_history
self.idx2word_target = data.idx2word_target
self.word2idx_target = data.word2idx_target
self.history_length = history_length
self.encoder_sequence_length = encoder_sequence_length
self.features = features
self.targets = targets
self.batch_size = batch_size
self.use_inputs_prob = use_inputs_prob
self.targets_given_features = targets_given_features
self.loss = loss
self.accuracy = accuracy
def train(train_set, test_set, idx2word, word2idx):
encoder_lstm_size = 5
encoder_embedding_size = 5
encoder_vocabulary_length = len(idx2word)
encoder_sequence_length = train_set['features'].shape[1]
decoder_lstm_size = 5
decoder_embedding_size = 5
decoder_vocabulary_length = len(idx2word)
decoder_sequence_length = train_set['targets'].shape[1]
# inference model
with tf.name_scope('model'):
i = tf.placeholder("int32", name='input')
o = tf.placeholder("int32", name='true_output')
with tf.variable_scope("batch_size"):
batch_size = tf.shape(i)[0]
encoder_embedding = embedding(
input=i,
length=encoder_vocabulary_length,
size=encoder_embedding_size,
name='encoder_embedding'
)
with tf.name_scope("RNNEncoderCell"):
cell = LSTMCell(
num_units=encoder_lstm_size,
input_size=encoder_embedding_size,
use_peepholes=False
)
initial_state = cell.zero_state(batch_size, tf.float32)
encoder_outputs, encoder_states = rnn(
cell=cell,
inputs=[encoder_embedding[:, j, :] for j in range(encoder_sequence_length)],
initial_state=initial_state,
name='RNNForwardEncoder'
)
final_encoder_state = encoder_states[-1]
with tf.name_scope("RNNDecoderCell"):
cell = LSTMCell(
num_units=decoder_lstm_size,
input_size=decoder_embedding_size,
use_peepholes=False,
)
decoder_states, decoder_outputs, decoder_outputs_softmax = rnn_decoder(
cell=cell,
initial_state=final_encoder_state,
embedding_size=decoder_embedding_size,
embedding_length=decoder_vocabulary_length,
sequence_length=decoder_sequence_length,
name='RNNDecoder'
)
p_o_i = tf.concat(1, decoder_outputs_softmax)
with tf.name_scope('loss'):
one_hot_labels = dense_to_one_hot(o, decoder_vocabulary_length)
loss = tf.reduce_mean(-one_hot_labels * tf.log(p_o_i), name='loss')
# loss = tf.constant(0.0, dtype=tf.float32)
tf.scalar_summary('loss', loss)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(p_o_i, 2))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
# accuracy = tf.constant(0.0, dtype=tf.float32)
tf.scalar_summary('accuracy', accuracy)
with tf.Session() as sess:
# Merge all the summaries and write them out to ./log
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('./log', sess.graph_def)
saver = tf.train.Saver()
# training
train_op = tf.train.AdamOptimizer(FLAGS.learning_rate, name='trainer').minimize(loss)
tf.initialize_all_variables().run()
for epoch in range(FLAGS.max_epochs):
sess.run(train_op, feed_dict={i: train_set['features'], o: train_set['targets']})
if epoch % max(int(FLAGS.max_epochs / 100), 1) == 0:
summary, lss, acc = sess.run([merged, loss, accuracy],
feed_dict={i: test_set['features'], o: test_set['targets']})
writer.add_summary(summary, epoch)
print()
print('Epoch: {epoch}'.format(epoch=epoch))
print(' - accuracy = {acc}'.format(acc=acc))
print(' - loss = {lss}'.format(lss=lss))
save_path = saver.save(sess, "model.ckpt")
print()
print("Model saved in file: %s" % save_path)
print()
print('Test features')
print(test_set['features'])
print('Test targets')
print('Shape of targets:', test_set['targets'].shape)
print(test_set['targets'])
print('Predictions')
p_o_i = sess.run(p_o_i, feed_dict={i: test_set['features'], o: test_set['targets']})
p_o_i_argmax = np.argmax(p_o_i, 2)
print('Shape of predictions:', p_o_i.shape)
print('Argmax predictions')
print(p_o_i_argmax)
print()
for i in range(p_o_i_argmax.shape[0]):
for j in range(p_o_i_argmax.shape[1]):
w = idx2word[p_o_i_argmax[i, j]]
if w not in ['_SOS_', '_EOS_']:
print(w, end=' ')
print()
def __init__(self, data, FLAGS):
with tf.variable_scope("history_length"):
history_length = data.train_set["features"].shape[1]
encoder_lstm_size = 16
encoder_embedding_size = 16 * 2
encoder_vocabulary_length = len(data.idx2word_history)
with tf.variable_scope("encoder_sequence_length"):
encoder_sequence_length = data.train_set["features"].shape[2]
decoder_lstm_size = 16
decoder_embedding_size = 16
decoder_vocabulary_length = len(data.idx2word_target)
with tf.variable_scope("decoder_sequence_length"):
decoder_sequence_length = data.train_set["targets"].shape[1]
# inference model
with tf.name_scope("model"):
features = tf.placeholder("int32", name="features")
targets = tf.placeholder("int32", name="true_targets")
use_dropout_prob = tf.placeholder("float32", name="use_dropout_prob")
with tf.variable_scope("batch_size"):
batch_size = tf.shape(features)[0]
encoder_embedding = embedding(
input=features, 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"):
use_inputs_prob = tf.placeholder("float32", name="use_inputs_prob")
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=[targets[:, 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=use_inputs_prob,
)
targets_given_features = 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(targets, decoder_vocabulary_length)
loss = tf.reduce_mean(-one_hot_labels * tf.log(targets_given_features), name="loss")
for v in tf.trainable_variables():
for n in ["/W_", "/W:", "/B:"]:
if n in v.name:
print("Regularization using", v.name)
loss += FLAGS.regularization * tf.reduce_mean(tf.pow(v, 2))
tf.scalar_summary("loss", loss)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(one_hot_labels, 2), tf.argmax(targets_given_features, 2))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.scalar_summary("accuracy", accuracy)
self.data = data
self.train_set = data.train_set
self.test_set = data.test_set
self.idx2word_history = data.idx2word_history
self.word2idx_history = data.word2idx_history
self.idx2word_target = data.idx2word_target
self.word2idx_target = data.word2idx_target
self.history_length = history_length
self.encoder_sequence_length = encoder_sequence_length
self.features = features
self.targets = targets
self.batch_size = batch_size
self.use_inputs_prob = use_inputs_prob
self.targets_given_features = targets_given_features
self.loss = loss
self.accuracy = accuracy
