print 'Loading training data...'
e0 = cv2.getTickCount()
# load training data
image_array = np.zeros((1, 38400))
label_array = np.zeros((1, 4), 'float')
training_data = glob.glob('training_data/*.npz')
for single_npz in training_data:
with np.load(single_npz) as data:
print data.files
train_temp = data['train']
train_labels_temp = data['train_labels']
print train_temp.shape
print train_labels_temp.shape
image_array = np.vstack((image_array, train_temp))
label_array = np.vstack((label_array, train_labels_temp))
train = image_array[1:, :]
train_labels = label_array[1:, :]
print train.shape
print train_labels.shape
e00 = cv2.getTickCount()
time0 = (e00 - e0)/ cv2.getTickFrequency()
print 'Loading image duration:', time0
# set start time
e1 = cv2.getTickCount()
# create MLP
layer_sizes = np.int32([38400, 32, 4])
model = cv2.ml.ANN_MLP_create()
model.create(layer_sizes)
criteria = (cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 500, 0.0001)
criteria2 = (cv2.TERM_CRITERIA_COUNT, 100, 0.001)
params = dict(term_crit = criteria,
train_method = cv2.ml.ANN_MLP_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
print 'Training MLP ...'
num_iter = model.train(train, train_labels, params = params )
# set end time
e2 = cv2.getTickCount()
time = (e2 - e1)/cv2.getTickFrequency()
print 'Training duration:', time
# save param
model.save('mlp_xml/mlp.xml')
print 'Ran for %d iterations' % num_iter
ret, resp = model.predict(train)
prediction = resp.argmax(-1)
print 'Prediction:', prediction
true_labels = train_labels.argmax(-1)
print 'True labels:', true_labels
print 'Testing...'
train_rate = np.mean(prediction == true_labels)
print 'Train rate: %f:' % (train_rate*100)
这是错误 AttributeError：'cv2.ml_ANN_MLP'对象没有属性'create'
答案 0 :(得分：0)
根据此code sample，我建议按如下方式设置图层数：
model.setLayerSizes(layer_sizes)
您还可以查看Learning OpenCV 3本书。
我似乎无法在一分钟内找到合适的完整文档，但是这里的记录是dir(model)给出的：