本文整理匯總了Python中mlp.MLP.flatParam方法的典型用法代碼示例。如果您正苦於以下問題：Python MLP.flatParam方法的具體用法？Python MLP.flatParam怎麽用？Python MLP.flatParam使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mlp.MLP的用法示例。
在下文中一共展示了MLP.flatParam方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: xrange
# 需要導入模塊: from mlp import MLP [as 別名]
# 或者: from mlp.MLP import flatParam [as 別名]
grad,train_nll,train_error = mlp.get_gradient(train_gradient_X, train_gradient_Y, batch_size)
delta, next_init, after_cost = mlp.cg(-grad, train_cg_X_cur, train_cg_Y_cur, batch_size, next_init, 1)
Gv = mlp.get_Gv(train_cg_X_cur,train_cg_Y_cur,batch_size,delta)
delta_cost = numpy.dot(delta,grad+0.5*Gv)
before_cost = mlp.quick_cost(numpy.zeros((num_param,)), train_cg_X_cur, train_cg_Y_cur, batch_size)
l2norm = numpy.linalg.norm(Gv + mlp._lambda*delta + grad)
print "Residual Norm: ",l2norm
print 'Before cost: %f, After cost: %f'%(before_cost,after_cost)
param = mlp.flatParam() + delta
mlp.packParam(param)
tune_lambda = (after_cost - before_cost)/delta_cost
if tune_lambda < 0.25:
mlp._lambda = mlp._lambda*1.5
elif tune_lambda > 0.75:
mlp._lambda = mlp._lambda/1.5
print "Training NNL: %f, Error: %f"%(train_nll,train_error)
nll=[]
error=[]
for batch_index in xrange(n_valid_batches):
X=valid_X[batch_index*batch_size:(batch_index+1)*batch_size,:]