(test_data_sh, test_data_t_sh)]
rbm_stack = RBMStack(num_dims, [500])
dbn = DBN(rbm_stack, 2)
batch_size = 100
max_epoch = 10
train_params = {
'batch_size': batch_size,
'learning_rate': 0.01,
'cd_steps': 2,
'max_epoch': max_epoch,
'persistent': True,
'finetune_learning_rate': 0.1
}
pre_fn = dbn.pretrain_fun(train_data_sh, train_params)
num_batches = train_data_sh.get_value(borrow=True).shape[0] / batch_size
for f in pre_fn:
for ep in xrange(0, max_epoch):
for b in xrange(0, num_batches):
cost, cur_free_en, cur_gparam = f(b)
print "Epoch # %d:%d cost: %f free energy: %f grad: %f" % (
ep, b, cost, cur_free_en, cur_gparam)
train, valid, test = dbn.finetune_fun(datasets, train_params)
validation_frequency = 200
for ep in xrange(0, 30):
for b in xrange(0, num_batches):
cost = train(b)
print "Epoch # %d:%d cost: %f" % (ep, b, cost)
datasets = [(train_data_sh, train_data_t_sh), (valid_data_sh, valid_data_t_sh), (test_data_sh, test_data_t_sh)]
rbm_stack = RBMStack(num_dims, [500])
dbn = DBN(rbm_stack, 2)
batch_size = 100
max_epoch = 10
train_params = {
"batch_size": batch_size,
"learning_rate": 0.01,
"cd_steps": 2,
"max_epoch": max_epoch,
"persistent": True,
"finetune_learning_rate": 0.1,
}
pre_fn = dbn.pretrain_fun(train_data_sh, train_params)
num_batches = train_data_sh.get_value(borrow=True).shape[0] / batch_size
for f in pre_fn:
for ep in xrange(0, max_epoch):
for b in xrange(0, num_batches):
cost, cur_free_en, cur_gparam = f(b)
print "Epoch # %d:%d cost: %f free energy: %f grad: %f" % (ep, b, cost, cur_free_en, cur_gparam)
train, valid, test = dbn.finetune_fun(datasets, train_params)
validation_frequency = 200
for ep in xrange(0, 30):
for b in xrange(0, num_batches):
cost = train(b)
print "Epoch # %d:%d cost: %f" % (ep, b, cost)
iter = ep * num_batches + b
