本文整理匯總了Python中net.Net.getPrediction方法的典型用法代碼示例。如果您正苦於以下問題：Python Net.getPrediction方法的具體用法？Python Net.getPrediction怎麽用？Python Net.getPrediction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net.Net的用法示例。
在下文中一共展示了Net.getPrediction方法的2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: run_small_net
# 需要導入模塊: from net import Net [as 別名]
# 或者: from net.Net import getPrediction [as 別名]
def run_small_net():
global training_data2, n2, t2, testing_data
layers = []
layers.append({'type': 'input', 'out_sx': 24, 'out_sy': 24, 'out_depth': 1})
#layers.append({'type': 'fc', 'num_neurons': 50, 'activation': 'relu'})
layers.append({'type': 'softmax', 'num_classes': 10})
print 'Layers made...'
n2 = Net(layers)
print 'Smaller Net made...'
print n2
t2 = Trainer(n2, {'method': 'sgd', 'momentum': 0.0})
print 'Trainer made for smaller net...'
print 'In training of smaller net...'
print 'k', 'time\t\t ', 'loss\t ', 'training accuracy'
print '----------------------------------------------------'
try:
for x, y in training_data2:
stats = t2.train(x, y)
print stats['k'], stats['time'], stats['loss'], stats['accuracy']
except: #hit control-c or other
pass
print 'Testing smaller net: 5000 trials'
right = 0
count = 5000
for x, y in sample(testing_data, count):
n2.forward(x)
right += n2.getPrediction() == y
accuracy = float(right) / count * 100
print accuracy示例2: run_big_net
# 需要導入模塊: from net import Net [as 別名]
# 或者: from net.Net import getPrediction [as 別名]
def run_big_net():
global training_data, testing_data, n, t, training_data2
training_data = load_data()
testing_data = load_data(False)
training_data2 = []
print 'Data loaded...'
layers = []
layers.append({'type': 'input', 'out_sx': 24, 'out_sy': 24, 'out_depth': 1})
layers.append({'type': 'fc', 'num_neurons': 100, 'activation': 'relu', 'drop_prob': 0.5})
#layers.append({'type': 'fc', 'num_neurons': 800, 'activation': 'relu', 'drop_prob': 0.5})
layers.append({'type': 'softmax', 'num_classes': 10})
print 'Layers made...'
n = Net(layers)
print 'Net made...'
print n
t = Trainer(n, {'method': 'sgd', 'momentum': 0.0})
print 'Trainer made...'
print 'In training...'
print 'k', 'time\t\t ', 'loss\t ', 'training accuracy'
print '----------------------------------------------------'
try:
for x, y in training_data:
stats = t.train(x, y)
print stats['k'], stats['time'], stats['loss'], stats['accuracy']
training_data2.append((x, n.getPrediction()))
except: #hit control-c or other
pass
print 'In testing: 5000 trials'
right = 0
count = 5000
for x, y in sample(testing_data, count):
n.forward(x)
right += n.getPrediction() == y
accuracy = float(right) / count * 100
print accuracy