def primed_sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
primed_state = tf.nn.dynamic_rnn(inputs=self.x_prime,
cell=cell,
sequence_length=self.x_prime_len,
dtype=tf.float32,
initial_state=initial_state,
scope='rnn')[1]
return rnn_free_run(cell=cell,
sequence_length=self.sample_tsteps,
initial_state=primed_state,
scope='rnn')[1]
def sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
initial_input = tf.concat([
tf.zeros([self.num_samples, 2]),
tf.ones([self.num_samples, 1]),
],
axis=1)
return rnn_free_run(cell=cell,
sequence_length=self.sample_tsteps,
initial_state=initial_state,
initial_input=initial_input,
scope='rnn')[1]
def sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
initial_input = tf.concat([
tf.zeros([self.num_samples, 2]),
tf.ones([self.num_samples, 1]),
], axis=1)
return rnn_free_run(
cell=cell,
sequence_length=self.sample_tsteps,
initial_state=initial_state,
initial_input=initial_input,
scope='rnn'
)[1]
def primed_sample(self, cell):
initial_state = cell.zero_state(self.num_samples, dtype=tf.float32)
primed_state = tf.nn.dynamic_rnn(
inputs=self.x_prime,
cell=cell,
sequence_length=self.x_prime_len,
dtype=tf.float32,
initial_state=initial_state,
scope='rnn'
)[1]
return rnn_free_run(
cell=cell,
sequence_length=self.sample_tsteps,
initial_state=primed_state,
scope='rnn'
)[1]