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Merged
Mxiim merged 4 commits into from
Apr 10, 2017
Merged

Refactoring/subreddit input #21

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961b30c
changed subreddit input to be a one hot vector
Apr 6, 2017
7a2e256
removed bias from l2_reg
Apr 6, 2017
7619051
main handels exceptions
Apr 6, 2017
fc8a0e2
chande to actual variable
Apr 6, 2017
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1 change: 1 addition & 0 deletions config.template.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ network:
rnn_unit: 'lstm' # Can be 'gru' or 'lstm', default: 'lstm'
hidden_layers: 0
hidden_neurons: 300
subreddit_input_neurons: 10 #Probebly not the best default value
use_concat_input: false
pre_train_subreddit: false
# Regularisation configs:
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1 change: 1 addition & 0 deletions definitions.py
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Expand Up @@ -44,6 +44,7 @@
RNN_UNIT = "rnn_unit"
HIDDEN_NEURONS = 'hidden_neurons'
HIDDEN_LAYERS = 'hidden_layers'
SUB_INPUT_NEURONS = 'subreddit_input_neurons'
USE_CONCAT_INPUT = 'use_concat_input'
BATCH_SIZE = 'batch_size'
TRAINING_EPOCHS = 'training_epochs'
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22 changes: 13 additions & 9 deletions main.py
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Expand Up @@ -36,16 +36,20 @@ def main():
args = parser.parse_args()

for conf in args.configs:
config_file = networkconfig[conf]
with tf.Session() as sess:
builder = ModelBuilder(config_file, sess)
try:
print("Starting config ", conf)
config_file = networkconfig[conf]
with tf.Session() as sess:
builder = ModelBuilder(config_file, sess)

network_model = builder.build()
if config_file[USE_PRETRAINED_NET]:
network_model.train(USE_PRETRAINED_NET)
network_model.train()
network_model.close_writers()
tf.reset_default_graph()
network_model = builder.build()
if config_file[USE_PRETRAINED_NET]:
network_model.train(USE_PRETRAINED_NET)
network_model.train()
network_model.close_writers()
tf.reset_default_graph()
except:
print("Config ", networkconfig[conf]["name"], "failed to complete")

if __name__ == "__main__":
main()
1 change: 1 addition & 0 deletions model/model.py
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Expand Up @@ -63,6 +63,7 @@ def __init__(self, config, session):
self.dropout_prob = config[DROPOUT_PROB] # Only used for train op
self.hidden_layers = config[HIDDEN_LAYERS]
self.hidden_neurons = config[HIDDEN_NEURONS]
self.subreddit_input_neurons = config[SUB_INPUT_NEURONS]
self.is_trainable_matrix = config[TRAINABLE_MATRIX]
self.use_pretrained = config[USE_PRETRAINED]
self.use_constant_limit = config[USE_CONSTANT_LIMIT]
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28 changes: 22 additions & 6 deletions model/model_builder.py
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Expand Up @@ -47,7 +47,7 @@ def add_input_layer(self):
name="input")
self._model.subreddit_input = \
tf.placeholder(tf.float64,
[None, 1],
[None, self._model.subreddit_count],
name="subreddit_input")
self._model.target = \
tf.placeholder(tf.float64,
Expand Down Expand Up @@ -94,7 +94,23 @@ def add_input_layer(self):
output = outputs[-1]
if self._model.use_concat_input:
# Add subreddit to end of input
output = tf.concat([output, self._model.subreddit_input], 1)
subreddit_weights = tf.Variable(tf.random_normal(
[self._model.subreddit_count,
self._model.subreddit_input_neurons],
stddev=0.35,
dtype=tf.float64),
name="sub_input_weights")

subreddit_bias = tf.Variable(tf.random_normal(
[self._model.subreddit_input_neurons],
stddev=0.35,
dtype=tf.float64),
name="sub_input_bias")

logit_subreddit = tf.add(
tf.matmul(self._model.subreddit_input, subreddit_weights),
subreddit_bias)
output = tf.concat([output, logit_subreddit], 1)

self._model.latest_layer = output

Expand All @@ -107,7 +123,9 @@ def add_layer(self, number_of_neurons):
self.added_layers = True
weights = tf.Variable(tf.random_normal(
[self._model.rnn_neurons +
(1 if self._model.use_concat_input else 0),
(self._model.subreddit_input_neurons
if self._model.use_concat_input
else 0),
number_of_neurons],
stddev=0.35,
dtype=tf.float64),
Expand All @@ -133,9 +151,7 @@ def add_layer(self, number_of_neurons):
logits, name="hidden_layer-" + str(self.number_of_layers))

if self._model.use_l2_loss:
self._model.l2_term = tf.add(
tf.add(self._model.l2_term, tf.nn.l2_loss(weights)),
tf.nn.l2_loss(bias))
self._model.l2_term = tf.add(self._model.l2_term, tf.nn.l2_loss(weights))
if self._model.use_dropout:
self._model.latest_layer = \
tf.nn.dropout(self._model.latest_layer,
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15 changes: 9 additions & 6 deletions model/util/data.py
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Expand Up @@ -123,8 +123,9 @@ def next_train_batch(self, batch_size=None):
self.train_present += present
self.train_absent += absent

subreddit_vec = helper.subreddit_index(subreddit,
self.subreddit_dict)
subreddit_vec = helper.label_vector(subreddit,
self.subreddit_dict,
self.subreddit_count)

label_vec = helper.label_vector(label.split(), self.users_dict,
self.user_count)
Expand Down Expand Up @@ -198,8 +199,9 @@ def next_valid_batch(self, batch_size=None):
self.valid_present += pres
self.valid_absent += absent

subreddit_vec = helper.subreddit_index(subreddit,
self.subreddit_dict)
subreddit_vec = helper.label_vector(subreddit,
self.subreddit_dict,
self.subreddit_count)

label_vec = helper.label_vector(label.split(), self.users_dict,
self.user_count)
Expand Down Expand Up @@ -238,8 +240,9 @@ def next_test_batch(self, batch_size=None):
self.word_dict,
self.max_title_length)

subreddit_vec = helper.subreddit_index(subreddit,
self.subreddit_dict)
subreddit_vec = helper.label_vector(subreddit,
self.subreddit_dict,
self.subreddit_count)

label_vec = helper.label_vector(label.split(), self.users_dict,
self.user_count)
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2 changes: 1 addition & 1 deletion model/util/writer.py
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Expand Up @@ -34,7 +34,7 @@ def log_samefile(config, f1_score_valid, f1_score_train, epoch_top, prec_valid,
raise FileNotFoundError('Can not write because no directory is created')

config_headers = [NET_TYPE, NET_NAME, VOC_SIZE, USER_COUNT, LEARN_RATE, EMBEDD_SIZE, MAX_TITLE_LENGTH, RNN_NEURONS, RNN_UNIT,
HIDDEN_NEURONS, HIDDEN_LAYERS, USE_CONCAT_INPUT, BATCH_SIZE, TRAINING_EPOCHS, USE_L2_LOSS, L2_FACTOR, USE_DROPOUT,
HIDDEN_NEURONS, HIDDEN_LAYERS, SUB_INPUT_NEURONS, USE_CONCAT_INPUT, BATCH_SIZE, TRAINING_EPOCHS, USE_L2_LOSS, L2_FACTOR, USE_DROPOUT,
DROPOUT_PROB, USE_CONSTANT_LIMIT, CONSTANT_PREDICTION_LIMIT, TRAINABLE_MATRIX,
PRE_TRAINED_MATRIX, USE_PRETRAINED, VALIDATION_DATA, TRAINING_DATA, TESTING_DATA]

Expand Down