utils.py

import tensorflow as tf
import numpy as np
import pickle
import json
import os

#from collections import defaultdict
#from scipy import ndimage

def flatten_tf_array(array):
    shape = array.get_shape().as_list()
    return tf.reshape(array, [shape[0], shape[1] * shape[2] * shape[3]])

def accuracy(predictions, labels):
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])

def randomize(dataset, labels):
    permutation = np.random.permutation(labels.shape[0])
    shuffled_dataset = dataset[permutation, :, :]
    shuffled_labels = labels[permutation]
    return shuffled_dataset, shuffled_labels

def one_hot_encode(np_array, num_labels):
    return (np.arange(num_labels) == np_array[:,None]).astype(np.float32)

def reformat_data(dataset, labels, image_width, image_height, image_depth):
    np_dataset_ = np.array([np.array(image_data).reshape(image_width, image_height, image_depth) for image_data in dataset])
    num_labels = len(np.unique(labels))
    np_labels_ = one_hot_encode(np.array(labels, dtype=np.float32), num_labels)
    np_dataset, np_labels = randomize(np_dataset_, np_labels_)
    return np_dataset, np_labels