本文整理汇总了Python中nolearn.lasagne.NeuralNet.train_history_[r]['train_loss']方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.train_history_[r]['train_loss']方法的具体用法？Python NeuralNet.train_history_[r]['train_loss']怎么用？Python NeuralNet.train_history_[r]['train_loss']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nolearn.lasagne.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.train_history_[r]['train_loss']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: create_stack
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import train_history_[r]['train_loss'] [as 别名]
normal_stack = create_stack(N)
print("Made stack!")
for k in range(0, 1000):
saved_accuracy = 10011.0
data = np.array(normal_stack + random.sample(coords, N))
val = np.append(np.zeros(N), np.ones(N))
data, val = shuffle(data, val)
for i in range(0, int(EPOCHS)):
nn.fit(data, val)
cur_accuracy = nn.train_history_[-1]['valid_loss']
if cur_accuracy - 0.004 > saved_accuracy:
print("Test Loss Jump! Loading previous network!")
with suppress_stdout():
nn.load_params_from("cachedgooglenn2.params")
else:
nn.save_params_to('cachedgooglenn2.params')
saved_accuracy = cur_accuracy
nn.update_learning_rate *= DECAY
normal_stack = update_stack(normal_stack, int(K*N), nn)
print("Data Report: K={3:.2f}, Prob Before={0}, Prob After={1}, Overlap={2}".format(proba_before, proba_after, overlap, K))
K += KGROWTH
EPOCHS *= EGROWTH
for r in range(len(nn.train_history_)):
nn.train_history_[r]['train_loss'] = 10011.0
nn.save_params_to('googlenn2.params')