# def __init__(self, W, input, data_size,input_size, output_size, isDropout):
rbm1 = RBM(None, cnn2.output(), file_num, rbm_size_list[0], rbm_size_list[1], False)
for i in xrange(pre_train_epoch):
print 'rbm1 pre_train:' + str(i)
rbm1.contrast_divergence()
reinput = rbm1.reconstruct_from_input(rbm1.input)
saveImage(reinput, node_shape[2], 'rbm1_after_train')
saveW(rbm1.getW(), 'rbm1_after_train')
rbm2 = RBM(None, rbm1.output(), file_num, rbm_size_list[1], rbm_size_list[2], False)
for i in xrange(pre_train_epoch):
print 'rbm2 pre_train:' + str(i)
rbm2.contrast_divergence()
reinput = rbm2.reconstruct_from_input(rbm2.input)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm2_after_train')
saveW(rbm2.getW(), 'rbm2_after_train')
rbm3 = RBM(None, rbm2.output(), file_num, rbm_size_list[2], rbm_size_list[3], False)
for i in xrange(pre_train_epoch):
print 'rbm3 pre_train:' + str(i)
rbm3.contrast_divergence()
reinput = rbm3.reconstruct_from_input(rbm3.input)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm3_after_train')
saveW(rbm3.getW(), 'rbm3_after_train')
rbm4 = RBM(None, rbm3.output(), file_num, rbm_size_list[3], rbm_size_list[4], False)
for i in xrange(pre_train_epoch):
