def train(target_distribution: Distribution) -> None:
"""Training Normalizing Flow"""
target_distribution.save_distribution()
normalizing_flow = NormalizingFlow(K=NORMALIZING_FLOW_LAYER_NUM)
z_0, log_q_0 = normalizing_flow.get_placeholder()
z_k, log_q_k = normalizing_flow.forward(z_0, log_q_0)
loss = normalizing_flow.calc_loss(z_k, log_q_k, target_distribution)
trainer = normalizing_flow.get_trainer(loss)
logger.info('Calculation graph constructed')
loss_values = []
with tf.Session() as sess:
logger.info('Session Start')
sess.run(tf.global_variables_initializer())
logger.info('All variables initialized')
logger.info(f'Training Start (number of iterations: {ITERATION})')
for iteration in range(ITERATION + 1):
z_0_batch = NormalDistribution.sample(BATCH_SIZE)
log_q_0_batch = np.log(NormalDistribution.calc_prob(z_0_batch))
_, loss_value = sess.run([trainer, loss], {
z_0: z_0_batch,
log_q_0: log_q_0_batch
})
loss_values.append(loss_value)
if iteration % 100 == 0:
iteration_digits = len(str(ITERATION))
logger.info(
f'Iteration: {iteration:<{iteration_digits}} Loss: {loss_value}'
)
if iteration % SAVE_FIGURE_INTERVAL == 0:
z_0_batch_for_visualize = NormalDistribution.sample(
NUMBER_OF_SAMPLES_FOR_VISUALIZE)
log_q_0_batch_for_visualize = np.log(
NormalDistribution.calc_prob(z_0_batch_for_visualize))
z_k_value = sess.run(
z_k, {
z_0: z_0_batch_for_visualize,
log_q_0: log_q_0_batch_for_visualize
})
save_result(z_k_value, iteration, target_distribution.__name__)
save_loss_values(loss_values, target_distribution.__name__)
logger.info('Training Finished')
logger.info('Session Closed')
def test_save_distribution(self):
with self.assertRaises(NotImplementedError):
Distribution.save_distribution()