def pretrain_autoencoder(net,
x,
x_val,
rbm_lr=0.001,
rbm_use_gauss_visible=False,
rbm_use_gauss_hidden=True,
rbm_mom=0.5,
rbm_weight_decay=0.0000,
rbm_lr_decay=0.0,
rbm_batch_size=100,
rbm_epochs=100,
rbm_patience=-1,
verbose=1):
final_arch = net.arch[1:math.ceil(len(net.arch) /
2.0)] # without input layer
n_dense_layers = len(final_arch)
rbm_list = []
#loop for training the RBMs
for i in range(n_dense_layers):
print("\nFine tuning layer number " + str(i))
if (i == 0):
x_new = x
x_val_new = x_val
else:
x_new = rbm_list[-1].get_h(x_new)
x_val_new = rbm_list[-1].get_h(x_val_new)
rbm = RBM(x_new.shape[1],
final_arch[i],
use_gaussian_visible_sampling=rbm_use_gauss_visible,
use_gaussian_hidden_sampling=rbm_use_gauss_hidden,
use_sample_vis_for_learning=False)
rbm.set_lr(
rbm_lr,
rbm_lr_decay,
momentum=rbm_mom,
weight_decay=rbm_weight_decay,
)
rbm.fit(x_new,
x_val_new,
batch_size=rbm_batch_size,
epochs=rbm_epochs,
patience=rbm_patience)
rbm_list.append(rbm)
rbm_iterator = 0
rbm_iterator_direction = 1
#loop to copy the weights from rbm to NN
for n_layer in range(len(net.layers)):
if (net.layers[n_layer].ID == "Dense"):
copy_dense_weights_from_rbm(rbm_list[rbm_iterator],
net.layers[n_layer],
rbm_iterator_direction)
if (rbm_iterator == len(rbm_list) - 1
and rbm_iterator_direction == 1):
rbm_iterator_direction = -1
else:
rbm_iterator += rbm_iterator_direction
print("Pre training finished!")
return rbm_list
