# print(' '.join('%5s' % classes[labels[j]] for j in range(64)))
net = DBN()
print(net)
print(len(net.rbm_layers))
for i in range(len(net.rbm_layers)):
print("-------------No. {} layer's weights-------------".format(i+1))
print(net.rbm_layers[i].weights)
print(len(net.rbm_layers[i].weights))
print("-------------No. {} layer's visible_bias-------------".format(i+1))
print(net.rbm_layers[i].visible_bias)
print(len(net.rbm_layers[i].visible_bias))
print("-------------No. {} layer's hidden_bias-------------".format(i+1))
print(net.rbm_layers[i].hidden_bias)
print(len(net.rbm_layers[i].hidden_bias))
print("-------------No. {} layer's Learning rate-------------".format(i+1))
print(net.rbm_layers[i].learning_rate)
train_features, train_labels = net.train_static(train_loader, train_dataset)
print(train_features)
print(train_labels)
test_features, test_labels = net.Testing(test_loader, test_dataset)
print(test_features)
print(test_labels)
