add siamese example

use graph model
take pairs of digits as input

examples/mnist_siamese_graph.py

@@ -0,0 +1,123 @@
+'''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))