def main():
cnn = TextCNN(sequence_length=SEQUENCE_LENGTH,
num_classes=6,
filter_sizes=FILTER_SIZES,
num_filters=NUM_FILTERS,
neurons_hidden=NEURONS_HIDDEN,
learning_rate=LEARNING_RATE,
embedding_size=300,
reg_lambda=L2_REG)
logger = Logger(TITLE, COMMENT)
logger.set_source(cnn)
with tf.Session() as session:
session.run(tf.initialize_all_variables())
def collection_hook():
tf.add_to_collection('features', cnn.h_pool_flat)
tf.add_to_collection('input', cnn.input_vect)
tf.add_to_collection('dropout_keep_prop', cnn.dropout_keep_prob)
tf.add_to_collection('sequence_length', SEQUENCE_LENGTH)
tf.add_to_collection('scores', cnn.scores)
tf.add_to_collection('predictions', cnn.predictions)
def train_step(in_batch, target_batch):
feed_dict = {
cnn.input_vect: in_batch,
cnn.target_vect: target_batch,
cnn.dropout_keep_prob: 0.5,
cnn.class_weights: TrainingData().factors
}
_, acc, cost, summary = session.run(
[cnn.train_op, cnn.accuracy, cnn.loss, cnn.merged],
feed_dict=feed_dict)
return acc, cost, summary
def val_step(in_batch, target_batch):
feed_dict = {
cnn.input_vect: in_batch,
cnn.target_vect: target_batch,
cnn.dropout_keep_prob: 1.0
}
acc = session.run(cnn.accuracy, feed_dict)
return acc
train(training_step=train_step,
preprocess=preprocess,
num_batches=NUM_BATCHES,
batch_size=BATCH_SIZE,
collection_hook=collection_hook,
logger=logger,
name='CNN',
full=TRAIN_ON_FULL_DATA)
if not TRAIN_ON_FULL_DATA:
validate(validation_step=val_step,
preprocess=preprocess,
batch_size=BATCH_SIZE,
logger=logger)
def main():
with tf.Session() as session:
with tf.name_scope('Ensemble'):
ffn = NumericFFN(NUM_FEATURES, NEURONS_HIDDEN, 6, LEARNING_RATE,
L2_REG)
session.run(tf.initialize_all_variables())
rebuild_subnets()
logger = Logger(TITLE, COMMENT)
logger.set_source(ffn)
def collection_hook():
tf.add_to_collection(
'score',
ffn.scores,
)
tf.add_to_collection('input', ffn.in_vector)
tf.add_to_collection('dropout_keep_prop', ffn.dropout_keep_prob)
tf.add_to_collection('predictions', ffn.predictions)
tf.add_to_collection('category', ffn.category)
def train_step(in_batch, target_batch):
feed_dict = {
ffn.in_vector: get_subnet_votes(in_batch),
ffn.target_vect: target_batch,
ffn.dropout_keep_prob: 0.5,
ffn.class_weights: TrainingData().factors
}
_, acc, cost, summary = session.run(
[ffn.train_op, ffn.accuracy, ffn.loss, ffn.merged],
feed_dict=feed_dict)
return acc, cost, summary
def val_step(in_batch, target_batch):
feed_dict = {
ffn.in_vector: get_subnet_votes(in_batch),
ffn.target_vect: target_batch,
ffn.dropout_keep_prob: 1.0
}
acc = session.run(ffn.accuracy, feed_dict)
return acc
train(training_step=train_step,
preprocess=lambda x: x,
num_batches=NUM_BATCHES,
batch_size=BATCH_SIZE,
collection_hook=collection_hook,
logger=logger,
name=TITLE,
full=TRAIN_ON_FULL_DATA)
if not TRAIN_ON_FULL_DATA:
validate(validation_step=val_step,
preprocess=lambda x: x,
batch_size=BATCH_SIZE,
logger=logger)
def main():
rnn = LSTM(HIDDEN_UNITS, 6, SERIES_LENGTH, LEARNING_RATE)
logger = Logger(TITLE, COMMENT)
logger.set_source(rnn)
with tf.Session() as session:
session.run(tf.initialize_all_variables())
def collection_hook():
tf.add_to_collection('input', rnn.input_vect)
tf.add_to_collection('dropout_keep_prop', rnn.dropout_keep_prob)
tf.add_to_collection('batch_size', rnn.batch_size)
tf.add_to_collection('scores', rnn.scores)
tf.add_to_collection('predictions', rnn.predictions)
tf.add_to_collection('series_length', SERIES_LENGTH)
def train_step(in_batch, target_batch):
feed_dict = {
rnn.input_vect: in_batch,
rnn.target_vect: target_batch,
rnn.dropout_keep_prob: 0.5,
rnn.batch_size: len(in_batch),
rnn.class_weights: TrainingData().factors
}
_, acc, cost, summary = session.run(
[rnn.train_op, rnn.accuracy, rnn.loss, rnn.merged],
feed_dict=feed_dict)
return acc, cost, summary
def val_step(in_batch, target_batch):
feed_dict = {
rnn.input_vect: in_batch,
rnn.target_vect: target_batch,
rnn.dropout_keep_prob: 1.0,
rnn.batch_size: len(in_batch)
}
acc = session.run(rnn.accuracy, feed_dict)
return acc
train(training_step=train_step,
preprocess=preprocess,
num_batches=NUM_BATCHES,
batch_size=BATCH_SIZE,
collection_hook=collection_hook,
logger=logger,
name=TITLE,
full=TRAIN_ON_FULL_DATA,
log_interval=SAVE_INTERVAL)
if not TRAIN_ON_FULL_DATA:
validate(validation_step=val_step,
preprocess=preprocess,
batch_size=BATCH_SIZE,
logger=logger)
def main():
net = NumericFFN(parameters=NUM_EXTENSIONS,
neurons_hidden=NEURONS_HIDDEN,
categories=6,
learning_rate=LEARNING_RATE,
reg_lambda=L2_REG)
vectorizer = ExtensionVectorizer()
logger = Logger(TITLE, COMMENT)
logger.set_source(net)
with tf.Session() as session:
session.run(tf.initialize_all_variables())
# this is helpful should we later include this in an ensemble
def collection_hook():
tf.add_to_collection('dropout_keep_prop', net.dropout_keep_prob)
tf.add_to_collection('scores', net.scores)
tf.add_to_collection('predictions', net.predictions)
def train_step(in_batch, target_batch):
feed_dict = {
net.in_vector: in_batch,
net.target_vect: target_batch,
net.dropout_keep_prob: 0.5,
net.class_weights: TrainingData().factors
}
_, acc, cost, summary = session.run([net.train_op, net.accuracy, net.loss, net.merged],
feed_dict=feed_dict)
return acc, cost, summary
def val_step(in_batch, target_batch):
feed_dict = {
net.in_vector: in_batch,
net.target_vect: target_batch,
net.dropout_keep_prob: 1.0
}
acc = session.run(net.accuracy, feed_dict)
return acc
train(training_step=train_step,
preprocess=vectorizer.vectorize,
num_batches=NUM_BATCHES,
batch_size=BATCH_SIZE,
collection_hook=collection_hook,
logger=logger,
name=TITLE,
full=TRAIN_ON_FULL_DATA)
if not TRAIN_ON_FULL_DATA:
validate(validation_step=val_step,
preprocess=vectorizer.vectorize,
batch_size=BATCH_SIZE,
logger=logger)
def main():
ffn = NumericFFN(parameters=len(FEATURES),
neurons_hidden=NEURONS_HIDDEN,
categories=6,
learning_rate=LEARNING_RATE,
reg_lambda=0.01)
logger = Logger(TITLE, COMMENT)
logger.set_source(ffn)
with tf.Session() as session:
session.run(tf.initialize_all_variables())
def collection_hook():
tf.add_to_collection('score', ffn.scores)
tf.add_to_collection('input', ffn.in_vector)
tf.add_to_collection('dropout_keep_prop', ffn.dropout_keep_prob)
tf.add_to_collection('predictions', ffn.predictions)
tf.add_to_collection('category', ffn.category)
def train_step(in_batch, target_batch):
feed_dict = {
ffn.in_vector: in_batch,
ffn.target_vect: target_batch,
ffn.dropout_keep_prob: 0.5,
ffn.class_weights: TrainingData().factors
}
_, acc, cost, summary = session.run([ffn.train_op, ffn.accuracy, ffn.loss, ffn.merged],
feed_dict=feed_dict)
return acc, cost, summary
def val_step(in_batch, target_batch):
feed_dict = {
ffn.in_vector: in_batch,
ffn.target_vect: target_batch,
ffn.dropout_keep_prob: 1.0
}
acc = session.run(ffn.accuracy, feed_dict)
return acc
train(training_step=train_step,
preprocess=preprocess,
num_batches=NUM_BATCHES,
batch_size=BATCH_SIZE,
collection_hook=collection_hook,
logger=logger,
name=TITLE,
full=TRAIN_ON_FULL_DATA)
if not TRAIN_ON_FULL_DATA:
validate(
validation_step=val_step,
preprocess=preprocess,
batch_size=BATCH_SIZE,
logger=logger)