train on siamese graph - custom mini batches

siamese.py

'''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

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, Graph
from keras.layers.core import Dense, Dropout, Lambda
from keras.optimizers import SGD, RMSprop
from keras import backend as K

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 Dense, Dropout, Lambda
from keras.optimizers import SGD, RMSprop,Adagrad,Adadelta
from keras import backend as K
from numpy import linalg as LA
import utils
import numpy.testing as npt
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
import math 

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.sum(K.square(u - v), 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(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)

g = Graph()
g.add_input(name='input_a', input_shape=(input_dim,))
g.add_input(name='input_b', input_shape=(input_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)

batch_size=128
epochs = 10
nsamples = 60000
x = np.zeros((batch_size,784))
y = np.zeros((batch_size,784))
l = np.zeros((batch_size,1))

for epoch in range(epochs):
    shuf = np.random.permutation(nsamples)
    nbatches = int(nsamples/batch_size)
    for k in range(nbatches):
        sh = shuf[k*batch_size:k*batch_size+batch_size]
        for s in range(batch_size):
            x[s] = X_train[sh[s],0]
            y[s] = X_train[sh[s],1]
            l[s] = y_train[sh[s]]
        #train here
        tt = g.train_on_batch({'input_a': x, 'input_b': y, 'output': l})
        print(tt[0])