def _build(self, images):
embedder = Embedder()
embedded_grads_weights = embedder.embed_all_grads_weights(
self._placeholders)
# Fake batching
embedded_grads_weights = tf.expand_dims(embedded_grads_weights, 0)
encoder = Encoder(self._source_num_way, self._target_num_way)
encoded = encoder.encode(embedded_grads_weights)
decoded = encoder.decode(encoded)
# Fake batching
decoded = tf.squeeze(decoded, [0])
weight_updates = embedder.unembed_all_weights(decoded)
the_list = [tf.nn.moments(w, [0]) for w in weight_updates]
mean_means = tf.reduce_mean([tf.reduce_mean(v[0]) for v in the_list])
mean_vars = tf.reduce_mean([tf.reduce_mean(v[1]) for v in the_list])
tf.summary.scalar('weight_updates_mean', mean_means,
[META_TRAIN_COMBINED_SUMMARIES])
tf.summary.scalar('weight_updates_var', mean_vars,
[META_TRAIN_COMBINED_SUMMARIES])
# Get the updated model
new_weights = [
self._placeholders[0][1] + weight_updates[0],
self._placeholders[1][1] + weight_updates[1],
self._placeholders[2][1] + weight_updates[2],
self._placeholders[3][1] + weight_updates[3],
self._placeholders[4][1] + weight_updates[4]
]
self.outputs = self.new_model_forward(new_weights, images)
return self.outputs
class StepsCache:
def __init__(self, client, logger, username, password, url, cache_key_name):
self.client = client
self.logger = logger
self.username = username
self.password = password
self.url = url
self.cache_key_name = cache_key_name
self.encoder = Encoder()
def get_key_name(self):
return self.cache_key_name
def add_script_steps_data(self):
script_steps = self.get_steps_information()
for value in script_steps:
self.client.sadd(self.get_key_name(), self.encoder.encode(value))
def get_steps_information(self):
try:
response = self.get_authorized_response(self.url)
data = json.loads(response.read())
return data["steps"]
except Exception, e:
self.logger.error("Error when getting registration steps data %s", e)
return []
class BlacklistCache:
def __init__(self, client):
self.client = client
self.encoder = Encoder()
def add_to_blacklist(self, text):
encoded_text = self.encoder.encode(text)
self.client.sadd(self.key_name(), encoded_text)
def key_name(self):
return "ureport-high-priority-blacklist"
class StepsCache:
def __init__(self, client, username, password, url, cache_key_name):
self.client = client
self.username = username
self.password = password
self.url = url
self.cache_key_name = cache_key_name
self.encoder = Encoder()
def get_key_name(self):
return self.cache_key_name
def add_script_steps_data(self):
script_steps = self.get_steps_information()
for value in script_steps:
self.client.sadd(self.get_key_name(), self.encoder.encode(value))
def get_steps_information(self):
response = self.get_authorized_response(self.url)
data = json.loads(response.read())
return data["steps"]
def delete_script_steps_data(self):
self.client.delete(self.get_key_name())
def has_text(self, text):
return self.client.sismember(self.get_key_name(), self.encoder.encode(text))
def get_authorized_response(self, url):
request = urllib2.Request(url)
base64string = base64.encodestring('%s:%s' % (self.username, self.password)).replace('\n', '')
request.add_header("Authorization", "Basic %s" % base64string)
response = urllib2.urlopen(request)
return response
def test_that_encode_text_using_md5(self):
text = "important text"
encoder = Encoder()
encoded_text = encoder.encode(text)
self.assertEquals(encoded_text, 'encrypted_value')
def test_that_encode_text_using_md5(self):
text = u"why dòn't ìnsért sòme strànge chàrs? ù.ù"
encoder = Encoder()
encoded_text = encoder.encode(text)
self.assertEquals(encoded_text, 'encrypted_value')
