Esempio n. 1
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def train(batchNum = 500, batchSize = 200000, learningRate = 0.001,
          ImagePatchWidth = 20, #layers = [500, 1000, 500],
          ImagePatchStep = 4, labelOptionNum = 100,
          labelMode = 'NUM'):
    trainDS = ds.read_data_sets(ImagePatchWidth, ImagePatchStep,
                                labelOptionNum, 'train', labelMode)

    classifier = skflow.TensorFlowEstimator(
        model_fn = conv_model,
        n_classes = labelOptionNum,
        batch_size = batchSize,
        steps = batchNum,
        learning_rate = learningRate)

    classifier.fit(trainDS.images, np.argmax(trainDS.labels, axis = 1),
                   logdir = gv.__DIR__ + gv.tensorflow_log_dir)
    return classifier
Esempio n. 2
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def test(classifier, ImagePatchWidth = 20, ImagePatchStep = 4,
         labelOptionNum = 100, labelMode = 'PRO'):
    image_files, bubble_num, bubble_regions = getinfo()

    result_filename   = gv.cnn__result_filename
    accuracy_filename = gv.cnn__accuracy_filename
    
    result   = np.zeros((len(image_files),1))
    accuracy = np.zeros((len(image_files),4))

    index = -1
    start_time = time.time()

    PROGRESS = progress.progress(0, len(image_files), prefix_info = 'Labeling ')

    for i, image_file in enumerate(image_files):
        testDS = ds.read_data_sets(ImagePatchWidth, ImagePatchStep,
                                   labelOptionNum, 'test', labelMode,
                                   imageName = image_file)
        y = classifier.predict(testDS.images)
        index = index + 1
        result[index] = np.sum(y)      
        # saving labeled result as image
        io.imsave(gv.__DIR__ + gv.cnn__image_dir + image_file,
                  np.reshape(y, (testDS.ylength, testDS.xlength)))
        _y = np.argmax(testDS.labels, axis = 1)
        # total accuracy
        accuracy[index, 0] = np.true_divide(np.sum(y == _y), _y.size)
        # accuracy of negative labeled instances
        accuracy[index, 1] = np.true_divide(np.sum(np.all(
            [y == _y, _y == 0], axis = 0)), np.sum(_y == 0))
        # accuracy of positive labeled instances
        accuracy[index, 2] = np.true_divide(np.sum(np.all(
            [y == _y, _y >  0], axis = 0)), np.sum(_y >  0))
        # average difference sum
        accuracy[index, 3] = np.true_divide(
            np.sum(np.absolute(np.subtract(y, _y))), _y.size)

        PROGRESS.setCurrentIteration(i+1)
        PROGRESS.setInfo(suffix_info = image_file)
        PROGRESS.printProgress()
    accuracy.tofile(accuracy_filename, sep =" ")
    return [result, accuracy]