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| n_input = images_train_norm.shape[1] # According to the paper, this must be equal to 32 * 32 = 1024 | |
| gbrbm_1 = GBRBM(n_input, 2000, learning_rate=0.001, use_tqdm=True, sigma=1) | |
| # Fit image data in gbrbm_1....... | |
| bbrbm_1 = BBRBM(2000, 1000, learning_rate=0.1, use_tqdm=True) | |
| # Fit image data in bbrbm_1....... | |
| bbrbm_2 = BBRBM(1000, 500, learning_rate=0.1, use_tqdm=True) | |
| # Fit image data in bbrbm_2....... | |
| bgrbm_1 = BGRBM(500, 50, learning_rate=0.001, use_tqdm=True, sigma=1) | |
| # Fit image data in bgrbm_1....... | |
| # Save all the learned parameters | |
| gbrbm_1.save_weights(args.save_model_dir+'gbrbm_1', 'gbrbm_1') | |
| bbrbm_1.save_weights(args.save_model_dir+'bbrbm_1', 'bbrbm_1') | |
| bbrbm_2.save_weights(args.save_model_dir+'bbrbm_2', 'bbrbm_2') | |
| bgrbm_1.save_weights(args.save_model_dir+'bgrbm_1', 'bgrbm_1') | |
| auto_enc = AutoEncoder(n_input, encoding_layer_sizes=[2000, 1000, 500, 50], | |
| layer_names=[['gbrbm_1_w', 'gbrbm_1_h'],['bbrbm_1_w', 'bbrbm_1_h'], | |
| ['bbrbm_2_w', 'bbrbm_2_h'],['bgrbm_1_w', 'bgrbm_1_h']]) | |
| auto_enc.load_rbm_weights(args.save_model_dir+'gbrbm_1', 0) | |
| auto_enc.load_rbm_weights(args.save_model_dir+'bbrbm_1', 1) | |
| auto_enc.load_rbm_weights(args.save_model_dir+'bbrbm_2', 2) | |
| auto_enc.load_rbm_weights(args.save_model_dir+'bgrbm_1', 3) |
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