'''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

Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_siamese_graph.py

Get 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, Graph
from keras.layers.core import *
from keras.optimizers import SGD, RMSprop
from keras import backend as K


def euclidean_distance(inputs):
    assert len(inputs) == 2, \
        'Euclidean distance needs 2 inputs, %d given' % len(inputs)
    u, v = inputs.values()
    return K.sqrt((K.square(u - v)).sum(axis=1, keepdims=True))


def contrastive_loss(y, d):
    """ 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 * K.square(d) + (1 - y) * K.square(K.maximum(margin - d, 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(in_dim):
    """ Base network to be shared (eq. to feature extraction).
    """
    seq = Sequential()
    seq.add(Dense(128, input_shape=(in_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 tran 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
in_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(in_dim)

g = Graph()
g.add_input(name='input_a', input_shape=(in_dim,))
g.add_input(name='input_b', input_shape=(in_dim,))
g.add_shared_node(base_network, name='shared', inputs=['input_a', 'input_b'],
                  merge_mode='join')
g.add_node(Lambda(euclidean_distance), name='d', input='shared')
g.add_output(name='output', input='d')

# train
rms = RMSprop()
g.compile(loss={'output': contrastive_loss}, optimizer=rms)
g.fit({'input_a': tr_pairs[:, 0], 'input_b': tr_pairs[:, 1], 'output': tr_y},
      validation_data={'input_a': te_pairs[:, 0], 'input_b': te_pairs[:, 1], 'output': te_y},
      batch_size=128, nb_epoch=nb_epoch)

# compute final accuracy on training and test sets
pred = g.predict({'input_a': tr_pairs[:, 0], 'input_b': tr_pairs[:, 1]})['output']
tr_acc = compute_accuracy(pred, tr_y)
pred = g.predict({'input_a': te_pairs[:, 0], 'input_b': te_pairs[:, 1]})['output']
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))