text: import numpy
  import random
  from keras.datasets import mnist
  from keras.models import Model
  from keras.layers import Input, Flatten, Dense, Dropout, Lambda
  from keras.optimizers import RMSprop
  from keras import backend as K
  
  from hyperopt import Trials, STATUS_OK, tpe
  from hyperas import optim
  from hyperas.distributions import choice, uniform
  
  def euclidean_distance(vects):
      x, y = vects
      return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
  
  def eucl_dist_output_shape(shapes):
      shape1, shape2 = shapes
      return (shape1[0], 1)
  
  def create_pairs(x, digit_indices):
      num_classes = 10
      pairs = []
      labels = []
      n = min([len(digit_indices[d]) for d in range(num_classes)]) - 1
      for d in range(num_classes):
          for i in range(n):
              z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]
              pairs += [[x[z1], x[z2]]]
              inc = random.randrange(1, num_classes)
              dn = (d + inc) % num_classes
              z1, z2 = digit_indices[d][i], digit_indices[dn][i]
              pairs += [[x[z1], x[z2]]]
              labels += [1, 0]
      return numpy.array(pairs), numpy.array(labels)
  
  def create_base_network(input_shape,dense_filter1,dense_filter2,dense_filter3,dropout1,dropout2):
      input = Input(shape=input_shape)
      x = Flatten()(input)
      x = Dense(dense_filter1, activation='relu')(x)
      x = Dropout(dropout1)(x)
      x = Dense(dense_filter2, activation='relu')(x)
      x = Dropout(dropout2)(x)
      x = Dense(dense_filter3, activation='relu')(x)
      return Model(input, x)
  
  def compute_accuracy(y_true, y_pred):
      pred = y_pred.ravel() < 0.5
      return numpy.mean(pred == y_true)
  
  def accuracy(y_true, y_pred):
      return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
  
  def process_data():
      num_classes = 10
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.astype('float32')
      x_test = x_test.astype('float32')
      x_train /= 255
      x_test /= 255
      input_shape = x_train.shape[1:]
  
      # create training+test positive and negative pairs
      digit_indices = [numpy.where(y_train == i)[0] for i in range(num_classes)]
      tr_pairs, tr_y = create_pairs(x_train, digit_indices)
  
      digit_indices = [numpy.where(y_test == i)[0] for i in range(num_classes)]
      te_pairs, te_y = create_pairs(x_test, digit_indices)
      return tr_pairs, tr_y, te_pairs, te_y,input_shape
  
  def data():
      tr_pairs, tr_y, te_pairs, te_y,input_shape = process_data()
      return tr_pairs, tr_y, te_pairs, te_y,input_shape
  
  def contrastive_loss(y_true, y_pred):
      margin = 1
      return K.mean(y_true * K.square(y_pred) +
                    (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
  
  def create_model(tr_pairs, tr_y, te_pairs, te_y,input_shape):
      epochs = 20
      dropout1 = {{uniform(0,1)}}
      dropout2 = {{uniform(0,1)}}
      dense_filter1 = {{choice([64,128,256])}}
      dense_filter2 = {{choice([64,128,256])}}
      dense_filter3 = {{choice([64,128,256])}}
      # network definition
      base_network = create_base_network(input_shape,dense_filter1,dense_filter2,dense_filter3,dropout1,dropout2)
  
      input_a = Input(shape=input_shape)
      input_b = Input(shape=input_shape)
  
      processed_a = base_network(input_a)
      processed_b = base_network(input_b)
  
      distance = Lambda(euclidean_distance,
                        output_shape=eucl_dist_output_shape)([processed_a, processed_b])
  
      model = Model([input_a, input_b], distance)
  
      rms = RMSprop()
      model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
      model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
                batch_size=128,
                epochs=epochs,
                verbose=1,
                validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))
  
      y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
      tr_acc = compute_accuracy(tr_y, y_pred)
      y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
      te_acc = compute_accuracy(te_y, y_pred)
      print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
      print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
  
      return {'loss': -te_acc, 'status': STATUS_OK, 'model': model}
  
  
  if __name__ == '__main__':
  
      tr_pairs, tr_y, te_pairs, te_y,input_shape = data()
  
      best_run, best_model = optim.minimize(model=create_model, data=data,
      functions = [process_data,create_base_network,euclidean_distance,contrastive_loss,eucl_dist_output_shape,create_pairs,accuracy,compute_accuracy],
      algo=tpe.suggest,max_evals=100,trials=Trials())
      print("best model",best_model)
      print("best run",best_run)
      print("Evalutation of best performing model:")
      loss,te_acc = best_model.evaluate([te_pairs[:, 0], te_pairs[:, 1]], te_y)
      print("best prediction accuracy on test data %0.2f%%" % (100 * te_acc))
  