'''Train a Siamese MLP on pairs of digits from the MNIST dataset.

It follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the
output of the shared network and by optimizing the contrastive loss (see paper
for mode details).

[1] "Dimensionality Reduction by Learning an Invariant Mapping"
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

Gets to 99.5% test accuracy after 20 epochs.
3 seconds per epoch on a Titan X GPU
'''
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Input, Lambda
from keras.optimizers import SGD, RMSprop
from keras import backend as K


def euclidean_distance(vects):
    x, y = vects
    return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))


def contrastive_loss(y_true, y_pred):
    '''Contrastive loss from Hadsell-et-al.'06
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    '''
    margin = 1
    return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))


def create_pairs(x, digit_indices):
    '''Positive and negative pair creation.
    Alternates between positive and negative pairs.
    '''
    pairs = []
    labels = []
    n = min([len(digit_indices[d]) for d in range(10)]) - 1
    for d in range(10):
        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, 10)
            dn = (d + inc) % 10
            z1, z2 = digit_indices[d][i], digit_indices[dn][i]
            pairs += [[x[z1], x[z2]]]
            labels += [1, 0]
    return np.array(pairs), np.array(labels)


def create_base_network(input_dim):
    '''Base network to be shared (eq. to feature extraction).
    '''
    seq = Sequential()
    seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))
    seq.add(Dropout(0.1))
    seq.add(Dense(128, activation='relu'))
    seq.add(Dropout(0.1))
    seq.add(Dense(128, activation='relu'))
    return seq


def compute_accuracy(predictions, labels):
    '''Compute classification accuracy with a fixed threshold on distances.
    '''
    return labels[predictions.ravel() < 0.5].mean()


# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
input_dim = 784
nb_epoch = 20

# create training+test positive and negative pairs
digit_indices = [np.where(y_train == i)[0] for i in range(10)]
tr_pairs, tr_y = create_pairs(X_train, digit_indices)

digit_indices = [np.where(y_test == i)[0] for i in range(10)]
te_pairs, te_y = create_pairs(X_test, digit_indices)

# network definition
base_network = create_base_network(input_dim)

input_a = Input(shape=(input_dim,))
input_b = Input(shape=(input_dim,))

# because we re-use the same instance `base_network`,
# the weights of the network
# will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)

distance = Lambda(euclidean_distance)([processed_a, processed_b])

model = Model(input=[input_a, input_b], output=distance)

# train
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms)
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
          batch_size=128,
          nb_epoch=nb_epoch)

# compute final accuracy on training and test sets
pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
tr_acc = compute_accuracy(pred, tr_y)
pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
te_acc = compute_accuracy(pred, te_y)

print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))

@@ -928,24 +928,108 @@
 ms=True))%0A%0A%0A
+def eucl_dist_output_shape(shapes):%0A    shape1, shape2 = shapes%0A    return shape1%0A%0A%0A
 def contrast
@@ -3446,16 +3446,53 @@
 distance
+, output_shape=eucl_dist_output_shape
 )(%5Bproce

Fix siamese example