recon_samples_list = self.vhv(vis_samples)
vis_p = self._one_rbm_compute_down(self.rbm_list[0], recon_samples_list[1])
vis_samples = recon_samples_list[0]
return x+1, vis_p, vis_samples
_, prob_imgs, sampled_imgs = tf.while_loop(cond, body, [0, vis, vis], back_prop=False)
return prob_imgs, sampled_imgs
if __name__ == '__main__':
if len(sys.argv) < 3 or len(sys.argv) > 4:
print 'usage: python drbn.py pcd/cd cd-k [output_dir]'
sys.exit()
else:
use_pcd = (sys.argv[1] == 'pcd')
cd_k = int(sys.argv[2])
output_dir = None if len(sys.argv) == 3 else sys.argv[3]
(train_xs, _), _, _ = cPickle.load(file('mnist.pkl', 'rb'))
train_xs = train_xs.reshape((-1, 28, 28, 1))
batch_size = 20
lr = 0.001 if use_pcd else 0.1
# drbn = DRBN([784])
drbn = DRBN([28, 28, 1])
drbn.add_conv_layer((5, 5, 1, 64), (2, 2), 'SAME', 'conv1')
drbn.add_conv_layer((5, 5, 64, 64), (2, 2), 'SAME', 'conv2')
drbn.add_fc_layer(500, 'fc1')
drbn.print_network()
train_rbm.train(drbn, train_xs, lr, 40, batch_size, use_pcd, cd_k, output_dir)
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
print "INFO: temporarily set 'image_dim_ordering' to 'tf'"
(train_xs, _), (_, _) = cifar10.load_data()
train_xs, mean, std = utils.preprocess_cifar10(train_xs)
batch_size = 20
pcd_chain_size = 100
lr = 0.0001 if use_pcd else 1e-5
train_xs = train_xs[:, :, :, :1] #.reshape(-1, 32*32)
rbm = GaussianCRBM((32, 32, 1), (12, 12, 1, 256), (2, 2), 'VALID',
output_dir, {})
# rbm = GaussianRBM(32*32, 1000, output_dir)
# drbn = DRBN([32*32], output_dir)
# drbn.add_fc_layer(500, 'fc1', use_gaussian=True)
# drbn.add_fc_layer(500, 'fc2')
# drbn.add_fc_layer(1000, 'fc3')
# drbn.print_network()
train_rbm.train(rbm,
train_xs,
lr,
40,
batch_size,
use_pcd,
cd_k,
output_dir,
pcd_chain_size=pcd_chain_size) # , mean, std)