hamuchiwa
69 linhas
1.8 KiB
Python
69 linhas
1.8 KiB
Python
__author__ = 'zhengwang'
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import cv2
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import numpy as np
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import glob
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print 'Loading training data...'
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e0 = cv2.getTickCount()
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# load training data
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image_array = np.zeros((1, 38400))
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label_array = np.zeros((1, 4), 'float')
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training_data = glob.glob('training_data/*.npz')
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for single_npz in training_data:
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with np.load(single_npz) as data:
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print data.files
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train_temp = data['train']
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train_labels_temp = data['train_labels']
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print train_temp.shape
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print train_labels_temp.shape
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image_array = np.vstack((image_array, train_temp))
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label_array = np.vstack((label_array, train_labels_temp))
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train = image_array[1:, :]
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train_labels = label_array[1:, :]
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print train.shape
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print train_labels.shape
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e00 = cv2.getTickCount()
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time0 = (e00 - e0)/ cv2.getTickFrequency()
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print 'Loading image duration:', time0
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# set start time
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e1 = cv2.getTickCount()
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# create MLP
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layer_sizes = np.int32([38400, 32, 4])
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model = cv2.ANN_MLP()
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model.create(layer_sizes)
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criteria = (cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 500, 0.0001)
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criteria2 = (cv2.TERM_CRITERIA_COUNT, 100, 0.001)
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params = dict(term_crit = criteria,
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train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
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bp_dw_scale = 0.001,
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bp_moment_scale = 0.0 )
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print 'Training MLP ...'
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num_iter = model.train(train, train_labels, None, params = params)
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# set end time
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e2 = cv2.getTickCount()
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time = (e2 - e1)/cv2.getTickFrequency()
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print 'Training duration:', time
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# save param
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model.save('mlp_xml/mlp.xml')
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print 'Ran for %d iterations' % num_iter
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ret, resp = model.predict(train)
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prediction = resp.argmax(-1)
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print 'Prediction:', prediction
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true_labels = train_labels.argmax(-1)
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print 'True labels:', true_labels
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print 'Testing...'
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train_rate = np.mean(prediction == true_labels)
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print 'Train rate: %f:' % (train_rate*100) |