def run(self, session, primers, length, temperature, hp=None):
batch_size = len(primers)
# process in segments to avoid tensorflow eating all the memory
max_segment_length = min(10000, hp.segment_length)
print "conditioning..."
segment_length = min(max_segment_length,
max(len(primer[0]) for primer in primers))
state = NS(model=self.model.initial_state(batch_size))
for segment in util.segments(primers, segment_length,
overlap=LEFTOVER):
x, = util.examples_as_arrays(segment)
feed_dict = {self.tensors.x: x.T}
feed_dict.update(self.model.feed_dict(state.model))
values = tfutil.run(session,
tensors=self.tensors.cond.Extract(
"final_state.model final_xelt"),
feed_dict=feed_dict)
state.model = values.final_state.model
sys.stderr.write(".")
sys.stderr.write("\n")
cond_values = values
print "sampling..."
length_left = length + LEFTOVER
xhats = []
state = NS(model=cond_values.final_state.model,
initial_xelt=cond_values.final_xelt)
while length_left > 0:
segment_length = min(max_segment_length, length_left)
length_left -= segment_length
feed_dict = {
self.tensors.initial_xelt: state.initial_xelt,
self.tensors.length: segment_length,
self.tensors.temperature: temperature
}
feed_dict.update(self.model.feed_dict(state.model))
sample_values = tfutil.run(
session,
tensors=self.tensors.sample.Extract(
"final_state.model xhat final_xhatelt"),
feed_dict=feed_dict),
state.model = sample_values.final_state.model
state.initial_xelt = sample_values.final_xhatelt
xhats.append(sample_values.xhat)
sys.stderr.write(".")
sys.stderr.write("\n")
xhat = np.concatenate(xhats, axis=0)
return xhat.T
def _make(self, hp):
ts = NS()
ts.x = tf.placeholder(dtype=tf.int32, name="x")
# conditioning graph
ts.cond = self.model.make_evaluation_graph(x=ts.x)
# generation graph
tf.get_variable_scope().reuse_variables()
ts.initial_xelt = tf.placeholder(dtype=tf.int32,
name="initial_xelt",
shape=[None])
ts.length = tf.placeholder(dtype=tf.int32, name="length", shape=[])
ts.temperature = tf.placeholder(dtype=tf.float32,
name="temperature",
shape=[])
ts.sample = self.model.make_sampling_graph(
initial_xelt=ts.initial_xelt,
length=ts.length,
temperature=ts.temperature)
return ts