code: from __future__ import absolute_import
  import numpy as np
  import scipy as sp
  from six.moves import range
  from six.moves import zip
  
  
  def to_categorical(y, nb_classes=None):
      '''Convert class vector (integers from 0 to nb_classes)
      to binary class matrix, for use with categorical_crossentropy.
      '''
      if not nb_classes:
          nb_classes = np.max(y)+1
      Y = np.zeros((len(y), nb_classes))
      for i in range(len(y)):
          Y[i, y[i]] = 1.
      return Y
  
  
  def normalize(a, axis=-1, order=2):
      l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
      l2[l2 == 0] = 1
      return a / np.expand_dims(l2, axis)
  
  
  def binary_logloss(p, y):
      epsilon = 1e-15
      p = sp.maximum(epsilon, p)
      p = sp.minimum(1-epsilon, p)
      res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
      res *= -1.0/len(y)
      return res
  
  
  def multiclass_logloss(P, Y):
      npreds = [P[i][Y[i]-1] for i in range(len(Y))]
      score = -(1. / len(Y)) * np.sum(np.log(npreds))
      return score
  
  
  def accuracy(p, y):
      return np.mean([a == b for a, b in zip(p, y)])
  
  
  def probas_to_classes(y_pred):
      if len(y_pred.shape) > 1 and y_pred.shape[1] > 1:
          return categorical_probas_to_classes(y_pred)
      return np.array([1 if p > 0.5 else 0 for p in y_pred])
  
  
  def categorical_probas_to_classes(p):
      return np.argmax(p, axis=1)
  
  
  def convert_kernel(kernel, dim_ordering='th'):
      '''Converts a kernel matrix (numpy array)
      from Theano format to TensorFlow format
      (or reciprocally, since the transformation
      is its own inverse).
      '''
      new_kernel = np.copy(kernel)
      if dim_ordering == 'th':
          w = kernel.shape[2]
          h = kernel.shape[3]
          for i in range(w):
              for j in range(h):
                  new_kernel[:, :, i, j] = kernel[:, :, w - i - 1, h - j - 1]
      elif dim_ordering == 'tf':
          w = kernel.shape[0]
          h = kernel.shape[1]
          for i in range(w):
              for j in range(h):
                  new_kernel[i, j, :, :] = kernel[w - i - 1, h - j - 1, :, :]
      else:
          raise Exception('Invalid dim_ordering: ' + str(dim_ordering))
      return new_kernel
  
apis: scipy.maximum scipy.subtract scipy.log numpy.argmax scipy.minimum numpy.copy numpy.expand_dims numpy.max numpy.log numpy.array numpy.linalg.norm