Creating a dataset loader

Introduction

--Give a batch size from the learning program so that you can get the training image and training label, and you can get the test image and test label. --I referred to the implementation of TensorFlow below. - https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/learn/python/learn/datasets/mnist.py --The part that reads the learning image, learning label, test image, and test label that were previously pickle has been modified. --The complete source is here.

data set

――As mentioned at the beginning, we refer to the following. - https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/learn/python/learn/datasets/mnist.py

class DataSet():
    """Data set management."""

    def __init__(self, images, labels):
        self._num_examples = images.shape[0]
        images = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
        images = images.astype(numpy.float32)
        images = numpy.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

def dense_to_one_hot(labels_dense, num_classes):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = numpy.arange(num_labels) * num_classes
    labels_one_hot = numpy.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
    return labels_one_hot

Pickle data reading

--Load the image and label of pickle into the above dataset.

Data reading

--Read the original data or padded data based on the configuration file.

def load_data(one_hot=False, validation_size=0):
    """Configure dataset.Read according to py."""

    train_num = AUGMENT_NUM if USE_AUGMENT else 0
    datasets_file = os.path.join(DATASETS_PATH, ','.join(CLASSES), '{}x{}-{}.pickle'.format(IMG_ROWS, IMG_COLS, train_num))

    with open(datasets_file, 'rb') as fin:
        (train_images, train_labels), (test_images, test_labels) = pickle.load(fin)

Label one hot conversion

--This time, the read label data is converted to ʻone_hotand connected to the subsequent process. --For example, if the labels are Sunny: 1, Cloudy: 2, Rain: 3, Sunny: (1, 0, 0), Cloudy: (0, 1, 0), Rain: (0,, It is a conversion to a shape like 0, 1). --For more information, search formachine learning one hot`.

    if one_hot:
        num_classes = len(numpy.unique(train_labels))
        train_labels = dense_to_one_hot(train_labels, num_classes)
        test_labels = dense_to_one_hot(test_labels, num_classes)

Image and label dataset classification

--Every time you load the dataset, the position is shuffled. ――Validation may be required, so we are trying to cut it out from the training data. ――Finally, we are classifying the data set for learning, validation, and testing.

    perm = numpy.arange(train_images.shape[0])
    numpy.random.shuffle(perm)
    train_images = train_images[perm]
    train_labels = train_labels[perm]

    validation_images = train_images[:validation_size]
    validation_labels = train_labels[:validation_size]
    train_images = train_images[validation_size:]
    train_labels = train_labels[validation_size:]

    train = DataSet(train_images, train_labels)
    validation = DataSet(validation_images, validation_labels)
    test = DataSet(test_images, test_labels)

    return Datasets(train=train, validation=validation, test=test)

in conclusion

--Created a dataset loader. The part of the original data that reads the pickle data has been modified. ――However, these days, you can hide this area and program it, so there is little need to implement it. I think it's the first and the last. ――Next time, I would like to create a learning model.