def make_GBRBM_kfold(train_data, valid_data, Image_type, k, save_path):
##############% K-fold based restricted boltzmann machine for training
# train the model
gbrbm = GBRBM(n_visible=train_data.shape[1], n_hidden=1000, learning_rate=0.01, momentum=0.9, use_tqdm=True)
train_data, valid_data, data_mean = data_demean(train_data,valid_data)
errs, errs_val = gbrbm.fit(train_data, valid_data, n_epoches=640, batch_size=20)
foldName = str(k)+'_fold/'+Image_type+'/'
createFolder(save_path+foldName)
plt.plot(errs)
plt.show()
plt.savefig(save_path+foldName+'train.png')
plt.plot(errs_val)
plt.savefig(save_path+foldName+'val.png')
plt.show()
np.save(save_path+foldName+'data_mean.npy', data_mean)
gbrbm.save_weights(filename = save_path+foldName, name = Image_type+'_model')
use_tqdm=False,
sample_visible=True,
sigma=sigma)
gb_n_epoches = 40
gb_batch_size = 128
errs = gbrbm.fit(data_x=input_data,
n_epoches=gb_n_epoches,
batch_size=gb_batch_size,
shuffle=True,
verbose=True)
gb_filename = 'pretrain_models/gbrbm.ckpt'
gb_name = 'rbm'
gbrbm.save_weights(filename=gb_filename, name=gb_name)
#begin pretraining the first Bernoulli-Bernoulli RBM
bb_input_data_1 = gbrbm.transform(input_data)
bb_input_data_1 = np.array(bb_input_data_1)
#print(bb_input_data_1.shape)
bb_n_visible_1 = bb_input_data_1.shape[1]
bb_n_hid_1 = 2048
bb_learning_rate_1 = 0.01
bb_momentum_1 = 0.95
bb_err_function_1 = 'mse'
bbrbm_1 = BBRBM(n_visible=bb_n_visible_1,
n_hidden=bb_n_hid_1,
learning_rate=bb_learning_rate_1,