本文整理汇总了Python中mlp.MLP.forward_pass方法的典型用法代码示例。如果您正苦于以下问题：Python MLP.forward_pass方法的具体用法？Python MLP.forward_pass怎么用？Python MLP.forward_pass使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mlp.MLP的用法示例。
在下文中一共展示了MLP.forward_pass方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from mlp import MLP [as 别名]
# 或者: from mlp.MLP import forward_pass [as 别名]
class TrainerValidator:
def __init__(self, k, nb_epochs, H1, H2, nu, mu, batchsize, data):
self.k = k
self.data = data
self.H1 = H1
self.H2 = H2
self.mu = mu
self.nu = nu
self.batchsize = batchsize
self.mlp = MLP(H1,H2,576, nu, mu, batchsize, self.k)
self.error = Error()
self.NUM_EPOCH = nb_epochs
self.validation_error = sp.zeros(self.NUM_EPOCH+1)
self.misclassified_val = sp.zeros(self.NUM_EPOCH+1)
self.training_error = sp.zeros(self.NUM_EPOCH+1)
self.misclassified_train = sp.zeros(self.NUM_EPOCH+1)
def trainAndClassify(self):
converge = 0
a = 4
var_thresh = 0.005
early_stopping = 0
for i in range(self.NUM_EPOCH+1):
self.data.shuffleData()
self.mlp.train(self.data.train_left, self.data.train_right, self.data.train_cat)
_, _, _, _, _, results_train, _, _, _, _, _, _ = self.mlp.forward_pass(self.data.train_left, self.data.train_right)
results_val, results_classif = self.mlp.classify(self.data.val_left, self.data.val_right)
self.training_error[i], self.misclassified_train[i] = self.error.norm_total_error(results_train, self.data.train_cat, self.k)
self.validation_error[i], self.misclassified_val[i] = self.error.norm_total_error(results_val, self.data.val_cat, self.k)
print "Epoch #"+str(i)+" Ratio of misclassified: "+str(self.misclassified_val[i])+" - Error: "+str(self.validation_error[i])
# Early stopping
if early_stopping :
if i > 0 :
if (self.validation_error[i]>(self.validation_error[i-1]*(1-var_thresh))) :
converge += 1
else :
if converge > 0 :
converge -= 1/2
if converge>=a :
print "Triggering early stopping - Cause : increasing(overfitting) or convergence of the error has been detected"
break
#self.mlp.test_gradient(self.data.val_left, self.data.val_right, self.data.val_cat)
def plotResults(self):
error_fig = plt.figure()
ax1 = error_fig.add_subplot(111)
ax1.plot(self.validation_error, label='validation error')
ax1.plot(self.training_error, label='training error')
ax1.set_ylabel('error')
ax1.set_xlabel('epoch')
title = "k=%d H1=%d H2=%d mu=%f nu=%f batchsize=%d std(val)=%f std(err)=%f" % (self.k, self.H1, self.H2, self.mu, self.nu, self.batchsize, sp.std(self.validation_error), sp.std(self.training_error) )
error_fig.suptitle(title)
plt.legend()
filename = "k=%d-H1=%d-H2=%d-mu=%f-nu=%f-batchsize=%d-nb_epoch=%d" % (self.k,self.H1, self.H2, self.mu, self.nu, self.batchsize, self.NUM_EPOCH)
plt.savefig('results/'+filename+"-error.png")
mis_fig = plt.figure()
ax2 = mis_fig.add_subplot(111)
ax2.plot(self.misclassified_val, label='misclassified ratio (validation)')
ax2.plot(self.misclassified_train, label='misclassified ratio (training)')
title = "k=%d H1=%d H2=%d mu=%f nu=%f batchsize=%d std(val)=%f std(err)=%f" % (self.k, self.H1, self.H2, self.mu, self.nu, self.batchsize, sp.std(self.misclassified_val), sp.std(self.misclassified_train) )
mis_fig.suptitle(title)
#ax2.set_xlim([1,self.NUM_EPOCH])
ax2.set_ylabel('misclassified')
ax2.set_xlabel('epoch')
plt.legend()
plt.savefig('results/'+filename+"-misclassified.png")
#plt.show()
def getMLP(self) :
return self.mlp