本文整理汇总了Python中mlp.MLP.test_patterns方法的典型用法代码示例。如果您正苦于以下问题：Python MLP.test_patterns方法的具体用法？Python MLP.test_patterns怎么用？Python MLP.test_patterns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.test_patterns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: main
# 需要导入模块: from mlp import MLP [as 别名]
# 或者: from mlp.MLP import test_patterns [as 别名]
def main():
imres = MLP()
num_points = 400
imres.add_layer(Layer(num_points))
imres.add_layer(Layer(20))
imres.add_layer(Layer(10))
imres.add_bias()
imres.init_network()
imres.step = 0.01
imres.moment = imres.step / 10
imres.verbose = True
target_error = 0.01
imres.patterns = []
imres._patterns = []
imres.test_patterns = []
imres._test_patterns = []
def norm(inp):
def fn(x):
return x/255
return map(fn, inp)
sample_dirs = set(os.listdir('font_samples'))
train = set(random.sample(sample_dirs, len(sample_dirs)-1))
test = sample_dirs - train
for j in train:
for i in range(0, 10):
gim = Image.open('font_samples/%s/%d.png' % (j, i)).convert('L')
imdata = norm(list(gim.getdata()))
outvect = [0]*10
outvect[i] = 1
imres.patterns.append((imdata, outvect))
imres._patterns.append((imdata, i, outvect))
for j in test:
for i in range(0, 10):
gim = Image.open('font_samples/%s/%d.png' % (j, i)).convert('L')
imdata = norm(list(gim.getdata()))
outvect = [0]*10
outvect[i] = 1
imres.test_patterns.append((imdata, outvect))
imres._test_patterns.append((imdata, i, outvect))
print 'Training samples: %d (%s)' % (len(imres.patterns),
' '.join(train))
print 'Testing samples: %d (%s)' % (len(imres.test_patterns),
' '.join(test))
print 'Target error: %.4f' % target_error
final_err, steps = imres.train_target(imres.patterns,
target_error=target_error)
print 'Training done in %d steps with final error of %.6f' % (steps,
final_err)
print '----- Detailed test output -----'
total_tests = len(imres._test_patterns)
total_fails = 0
for inp, num, target in imres._test_patterns:
computed = imres.run(inp)
error = abs(computed[0] - target[0])
computed = map(lambda x: round(x, 4), computed)
maxn = computed[0]
pos = 0
for i in range(len(computed)):
if computed[i] > maxn:
maxn = computed[i]
pos = i
if num != pos:
total_fails += 1
print 'in: %d, out: %d' % (num, pos)
print 'target: %s \noutput: %s' % (target, computed)
print '-----'
print 'Testing done - %d of %d samples classified incorrectly' % (
total_fails, total_tests)