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Merged
Mxiim merged 1 commit into from
Apr 13, 2017
Merged

Memory improvements #25

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2 changes: 1 addition & 1 deletion model/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ def __init__(self, config, session):
self.latest_layer = None
self.output_weights = None
self.output_bias = None
self.l2_term = tf.constant(0, dtype=tf.float64)
self.l2_term = tf.constant(0, dtype=tf.float32)

self.vocabulary_size = config[VOC_SIZE]
self.user_count = config[USER_COUNT]
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32 changes: 16 additions & 16 deletions model/model_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,19 +46,19 @@ def add_input_layer(self):
[None, self._model.max_title_length],
name="input")
self._model.subreddit_input = \
tf.placeholder(tf.float64,
tf.placeholder(tf.float32,
[None, self._model.subreddit_count],
name="subreddit_input")
self._model.target = \
tf.placeholder(tf.float64,
tf.placeholder(tf.float32,
[None, self._model.user_count],
name="target")
self._model.sec_target = \
tf.placeholder(tf.float64,
tf.placeholder(tf.float32,
[None, self._model.data.subreddit_count],
name="sec_target")

self._model.keep_prob = tf.placeholder(tf.float64, name="keep_prob")
self._model.keep_prob = tf.placeholder(tf.float32, name="keep_prob")

if self._model.rnn_unit == 'lstm':
rnn_layer = tf.contrib.rnn.LSTMCell(self._model.rnn_neurons)
Expand All @@ -73,13 +73,13 @@ def add_input_layer(self):
tf.random_uniform(
[self._model.vocabulary_size,
self._model.embedding_size],
-1.0, 1.0, dtype=tf.float64),
-1.0, 1.0, dtype=tf.float32),
trainable=self._model.is_trainable_matrix,
name="embedding_matrix",
dtype=tf.float64)
dtype=tf.float32)

self._model.embedding_placeholder = \
tf.placeholder(tf.float64,
tf.placeholder(tf.float32,
[self._model.vocabulary_size, self._model.embedding_size])
self._model.embedding_init = \
embedding_matrix.assign(self._model.embedding_placeholder)
Expand All @@ -88,7 +88,7 @@ def add_input_layer(self):
self._model.input)
# Run the LSTM layer with the embedded input
outputs, _ = tf.nn.dynamic_rnn(rnn_layer, embedded_input,
dtype=tf.float64)
dtype=tf.float32)

outputs = tf.transpose(outputs, [1, 0, 2])
output = outputs[-1]
Expand All @@ -98,13 +98,13 @@ def add_input_layer(self):
[self._model.subreddit_count,
self._model.subreddit_input_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="sub_input_weights")

subreddit_bias = tf.Variable(tf.random_normal(
[self._model.subreddit_input_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="sub_input_bias")

logit_subreddit = tf.add(
Expand All @@ -128,22 +128,22 @@ def add_layer(self, number_of_neurons):
else 0),
number_of_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="weights" + str(self.number_of_layers))
bias = tf.Variable(tf.random_normal([number_of_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="biases" + str(self.number_of_layers))

else:
weights = tf.Variable(tf.random_normal(
[self._model.latest_layer.get_shape()[1].value, number_of_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="weights" + str(self.number_of_layers))
bias = tf.Variable(tf.random_normal([number_of_neurons],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="biases" + str(self.number_of_layers))

logits = tf.add(tf.matmul(self._model.latest_layer, weights), bias)
Expand All @@ -169,12 +169,12 @@ def add_output_layer(self, output_size, secondary_output=False):
sigmoid_weights = tf.Variable(tf.random_normal(
[self._model.latest_layer.get_shape()[1].value, output_size],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="output_weights")

sigmoid_bias = tf.Variable(tf.random_normal([output_size],
stddev=0.35,
dtype=tf.float64),
dtype=tf.float32),
name="output_biases")

logits = tf.add(tf.matmul(self._model.latest_layer, sigmoid_weights), sigmoid_bias)
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2 changes: 1 addition & 1 deletion model/util/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,6 @@
for conf in cfg['network']:
for (key, value) in conf.items():
if type(value) is float:
conf[key] = np.float64(value)
conf[key] = np.float32(value)


30 changes: 14 additions & 16 deletions model/util/csv_reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,33 +74,31 @@ def get_data(self, datatype, data_column=[0], sub_column=1, label_column=2):
subreddit_full.append(subreddit)
return data_full, subreddit_full, label_full

def load_pretrained_embeddings(self, file_name, dimension_size=50):
file_path = os.path.join(DATASETS_PATH, file_name)
matrix = [np.random.rand(dimension_size).astype(np.float32)]
word_dict = dict()
word_dict['UNK'] = 0
count = 1

def test_load_pretrained_embeddings(self, fileName, dimension_size=50):
file_path = os.path.join(DATASETS_PATH, fileName)
with open(file_path, 'r', encoding='UTF-8') as csvfile:
reader = csv.reader(csvfile, delimiter=' ', quoting=csv.QUOTE_NONE)
word_dict = dict()
matrix = []

word_dict['UNK'] = len(matrix)
matrix.append(np.random.rand(1, dimension_size)[0].tolist())

for row in reader:
first_col = row[0]

if first_col in ['!', '?', '-', '_', '.', ',', '\'', '\"', ':', ';', '%', '(', ')']:
continue

if first_col[0] == '<': #some words are tokens for usernames like <user> or <caps> etc, ignore them.
# Some words are tokens for usernames like <user> or
# <caps> etc, ignore them.
if first_col[0] == '<':
continue
word_dict[first_col] = len(matrix)
matrix.append(row[1:])
embed_matrix = np.array(matrix)
embed_matrix = embed_matrix.astype(np.float64)
return word_dict, embed_matrix



word_dict[first_col] = count
row_array = np.array(row[1:], dtype=np.float32)
matrix.append(row_array)
count += 1
return word_dict, np.array(matrix)



2 changes: 1 addition & 1 deletion model/util/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def _build_dict(self):
helper.build_dataset(vocab, vocabulary_size=self.vocabulary_size)
else:
self.word_dict, self.embedding_matrix = \
self.reader.test_load_pretrained_embeddings(
self.reader.load_pretrained_embeddings(
self.pre_trained_matrix,
self.embedding_size)
users = " ".join(self.train_labels).split()
Expand Down