os.makedirs(init_w_path)
rnd = np.random.RandomState(1231)
nhid_l0 = 1025
nhid_l1 = 512
nhid_l2 = 64
# Create the AE in 1
nvis, nhid = w*h, nhid_l0
path_ini_params_l0 = init_w_path + "dae_w_l0_init_" + str(nvis) + '_' +\
str(nhid) + ".pkl"
dae_l0 = DenoisingAutoencoder(input,
nvis=nvis,
nhid=nhid,
L1_reg=0.,
L2_reg=1e-2,
rnd=rnd,
nonlinearity=NonLinearity.SIGMOID,
cost_type=CostType.MeanSquared,
reverse=False,
corruption_level=0.2)
if not os.path.isfile(path_ini_params_l0):
dae_l0.save_params(path_ini_params_l0)
else:
dae_l0.set_params_vals(path_ini_params_l0)
# Create the AE in 2
nvis, nhid = nhid_l0, nhid_l1
path_ini_params_l1 = init_w_path + "dae_w_l1_init_" + str(nvis) + '_' +\
str(nhid) + ".pkl"
dae_l1 = DenoisingAutoencoder(dae_l0.encode((input)),
print len(id_deep_conv_ae_in), id_deep_conv_ae_in
path_ini_params_deep_conv_ae_in = init_w_path +\
"deep_conv_ae_init_" + id_deep_conv_ae_in + ".pkl"
if not os.path.isfile(path_ini_params_deep_conv_ae_in):
deep_conv_ae_in.save_params(path_ini_params_deep_conv_ae_in)
else:
deep_conv_ae_in.set_params_vals(path_ini_params_deep_conv_ae_in)
# Create the AE in 1
nvis, nhid = deep_conv_ae_in.encoder.output_size_flatten, nhid_l0
path_ini_params_l0 = init_w_path + "dae_w_l0_init_" + str(nvis) + '_' +\
str(nhid) + ".pkl"
dae_l0 = DenoisingAutoencoder(deep_conv_ae_in.encoder.output.flatten(2),
nvis=nvis,
nhid=nhid,
L1_reg=0.,
L2_reg=0.,
rnd=rnd,
nonlinearity=NonLinearity.TANH,
cost_type=CostType.MeanSquared,
reverse=False,
corruption_level=0.2)
if not os.path.isfile(path_ini_params_l0):
dae_l0.save_params(path_ini_params_l0)
else:
dae_l0.set_params_vals(path_ini_params_l0)
# Create the AE in 2
# nvis, nhid = nhid_l0, nhid_l1
# path_ini_params_l1 = init_w_path + "dae_w_l1_init_" + str(nvis) + '_' +\
# str(nhid) + ".pkl"
# dae_l1 = DenoisingAutoencoder(dae_l0.encode(),