def main():
# load data
mnist = input_data.read_data_sets(args.dataset_path + 'MNIST_data',
one_hot=True)
# we can access to images like this:
# images = mnist.train.images; images.shape = []
# labels = mnist.train.labels; each label is a probability distribution.
print(mnist.train.num_examples)
max_epoch = args.max_epoch
max_loop_z = args.max_loop_z
with tf.Graph().as_default():
config = Config()
# model = MLP(config)
model = DCGN(config)
tf.get_default_graph().finalize()
progress = Progress()
n_batch_loop = int(mnist.train.num_examples / config.batch_size)
for epoch in range(max_epoch):
sum_cost = 0
progress.start_epoch(epoch, max_epoch)
for t in range(n_batch_loop):
# batch_X: batch_size x n_input
# batch_y: batch_size
batch_X, batch_y = mnist.train.next_batch(config.batch_size,
shuffle=False)
batch_indices = np.arange(
t * config.batch_size,
t * config.batch_size + config.batch_size)
for z_t in range(max_loop_z):
cost_z = model.forward_backprop_z(batch_X, batch_indices)
model.project_z_L2()
#print(cost_z)
cost_per_sample = model.forward_backprop_theta(
batch_X, batch_indices)
sum_cost += cost_per_sample
# model.increaseBatchID()
if t % 10 == 0:
progress.show(t, n_batch_loop, {})
print("cost: {}".format(sum_cost / n_batch_loop))
model.save(epoch, args.model_dir)
