131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
'''Train a Siamese MLP on pairs of digits from the MNIST dataset.
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It follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the
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output of the shared network and by optimizing the contrastive loss (see paper
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for mode details).
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[1] "Dimensionality Reduction by Learning an Invariant Mapping"
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http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
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Gets to 99.5% test accuracy after 20 epochs.
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3 seconds per epoch on a Titan X GPU
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'''
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from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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np.random.seed(1337) # for reproducibility
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import random
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from keras.datasets import mnist
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from keras.models import Sequential, Model
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from keras.layers import Dense, Dropout, Input, Lambda
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from keras.optimizers import SGD, RMSprop
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from keras import backend as K
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def euclidean_distance(vects):
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x, y = vects
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return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))
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def eucl_dist_output_shape(shapes):
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shape1, shape2 = shapes
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return (shape1[0], 1)
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def contrastive_loss(y_true, y_pred):
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'''Contrastive loss from Hadsell-et-al.'06
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http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
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'''
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margin = 1
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return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
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def create_pairs(x, digit_indices):
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'''Positive and negative pair creation.
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Alternates between positive and negative pairs.
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'''
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pairs = []
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labels = []
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n = min([len(digit_indices[d]) for d in range(10)]) - 1
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for d in range(10):
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for i in range(n):
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z1, z2 = digit_indices[d][i], digit_indices[d][i+1]
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pairs += [[x[z1], x[z2]]]
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inc = random.randrange(1, 10)
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dn = (d + inc) % 10
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z1, z2 = digit_indices[d][i], digit_indices[dn][i]
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pairs += [[x[z1], x[z2]]]
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labels += [1, 0]
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return np.array(pairs), np.array(labels)
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def create_base_network(input_dim):
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'''Base network to be shared (eq. to feature extraction).
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'''
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seq = Sequential()
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seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))
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seq.add(Dropout(0.1))
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seq.add(Dense(128, activation='relu'))
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seq.add(Dropout(0.1))
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seq.add(Dense(128, activation='relu'))
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return seq
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def compute_accuracy(predictions, labels):
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'''Compute classification accuracy with a fixed threshold on distances.
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'''
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return labels[predictions.ravel() < 0.5].mean()
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# the data, shuffled and split between train and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(60000, 784)
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X_test = X_test.reshape(10000, 784)
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X_train = X_train.astype('float32')
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X_test = X_test.astype('float32')
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X_train /= 255
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X_test /= 255
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input_dim = 784
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nb_epoch = 20
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# create training+test positive and negative pairs
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digit_indices = [np.where(y_train == i)[0] for i in range(10)]
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tr_pairs, tr_y = create_pairs(X_train, digit_indices)
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digit_indices = [np.where(y_test == i)[0] for i in range(10)]
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te_pairs, te_y = create_pairs(X_test, digit_indices)
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# network definition
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base_network = create_base_network(input_dim)
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input_a = Input(shape=(input_dim,))
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input_b = Input(shape=(input_dim,))
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# because we re-use the same instance `base_network`,
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# the weights of the network
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# will be shared across the two branches
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processed_a = base_network(input_a)
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processed_b = base_network(input_b)
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distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])
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model = Model(input=[input_a, input_b], output=distance)
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# train
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rms = RMSprop()
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model.compile(loss=contrastive_loss, optimizer=rms)
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model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
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validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
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batch_size=128,
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nb_epoch=nb_epoch)
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# compute final accuracy on training and test sets
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pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
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tr_acc = compute_accuracy(pred, tr_y)
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pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
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te_acc = compute_accuracy(pred, te_y)
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print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
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print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
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