79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
'''
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Neural network digit recognition sample.
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Usage:
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digits.py
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Sample loads a dataset of handwritten digits from 'digits.png'.
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Then it trains a neural network classifier on it and evaluates
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its classification accuracy.
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'''
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import numpy as np
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import cv2
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from common import mosaic
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def unroll_responses(responses, class_n):
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'''[1, 0, 2, ...] -> [[0, 1, 0], [1, 0, 0], [0, 0, 1], ...]'''
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sample_n = len(responses)
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new_responses = np.zeros((sample_n, class_n), np.float32)
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new_responses[np.arange(sample_n), responses] = 1
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return new_responses
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SZ = 20 # size of each digit is SZ x SZ
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CLASS_N = 10
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digits_img = cv2.imread('digits.png', 0)
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# prepare dataset
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h, w = digits_img.shape
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digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
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digits = np.float32(digits).reshape(-1, SZ*SZ)
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N = len(digits)
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labels = np.repeat(np.arange(CLASS_N), N/CLASS_N)
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# split it onto train and test subsets
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shuffle = np.random.permutation(N)
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train_n = int(0.9*N)
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digits_train, digits_test = np.split(digits[shuffle], [train_n])
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labels_train, labels_test = np.split(labels[shuffle], [train_n])
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# train model
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model = cv2.ANN_MLP()
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layer_sizes = np.int32([SZ*SZ, 25, CLASS_N])
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model.create(layer_sizes)
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params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 100, 0.01),
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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...'
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labels_train_unrolled = unroll_responses(labels_train, CLASS_N)
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model.train(digits_train, labels_train_unrolled, None, params=params)
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model.save('dig_nn.dat')
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model.load('dig_nn.dat')
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def evaluate(model, samples, labels):
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'''Evaluates classifier preformance on a given labeled samples set.'''
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ret, resp = model.predict(samples)
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resp = resp.argmax(-1)
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error_mask = (resp == labels)
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accuracy = error_mask.mean()
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return accuracy, error_mask
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# evaluate model
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train_accuracy, _ = evaluate(model, digits_train, labels_train)
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print 'train accuracy: ', train_accuracy
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test_accuracy, test_error_mask = evaluate(model, digits_test, labels_test)
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print 'test accuracy: ', test_accuracy
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# visualize test results
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vis = []
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for img, flag in zip(digits_test, test_error_mask):
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img = np.uint8(img).reshape(SZ, SZ)
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if not flag:
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img[...,:2] = 0
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vis.append(img)
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vis = mosaic(25, vis)
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cv2.imshow('test', vis)
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cv2.waitKey()
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