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代码位于keras的官方样例,并做了微量修改和大量学习
最终效果:
import keras import numpy as np import matplotlib.pyplot as plt import random from keras.callbacks import TensorBoard from keras.datasets import mnist from keras.models import Model from keras.layers import Input, Flatten, Dense, Dropout, Lambda from keras.optimizers import RMSprop from keras import backend as K num_classes = 10 epochs = 20 def euclidean_distance(vects): x, y = vects sum_square = K.sum(K.square(x - y), axis=1, keepdims=True) return K.sqrt(K.maximum(sum_square, K.epsilon())) def eucl_dist_output_shape(shapes): shape1, shape2 = shapes return (shape1[0], 1) 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 sqaure_pred = K.square(y_pred) margin_square = K.square(K.maximum(margin - y_pred, 0)) return K.mean(y_true * sqaure_pred + (1 - y_true) * margin_square) 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(num_classes)]) - 1 for d in range(num_classes): 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, num_classes) dn = (d + inc) % num_classes 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_shape): '''Base network to be shared (eq. to feature extraction). ''' input = Input(shape=input_shape) x = Flatten()(input) x = Dense(128, activation='relu')(x) x = Dropout(0.1)(x) x = Dense(128, activation='relu')(x) x = Dropout(0.1)(x) x = Dense(128, activation='relu')(x) return Model(input, x) def compute_accuracy(y_true, y_pred): # numpy上的操作 '''Compute classification accuracy with a fixed threshold on distances. ''' pred = y_pred.ravel() < 0.5 return np.mean(pred == y_true) def accuracy(y_true, y_pred): # Tensor上的操作 '''Compute classification accuracy with a fixed threshold on distances. ''' return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype))) def plot_train_history(history, train_metrics, val_metrics): plt.plot(history.history.get(train_metrics), '-o') plt.plot(history.history.get(val_metrics), '-o') plt.ylabel(train_metrics) plt.xlabel('Epochs') plt.legend(['train', 'validation']) # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 input_shape = x_train.shape[1:] # create training+test positive and negative pairs digit_indices = [np.where(y_train == i)[0] for i in range(num_classes)] tr_pairs, tr_y = create_pairs(x_train, digit_indices) digit_indices = [np.where(y_test == i)[0] for i in range(num_classes)] te_pairs, te_y = create_pairs(x_test, digit_indices) # network definition base_network = create_base_network(input_shape) input_a = Input(shape=input_shape) input_b = Input(shape=input_shape) # 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, output_shape=eucl_dist_output_shape)([processed_a, processed_b]) model = Model([input_a, input_b], distance) keras.utils.plot_model(model,"siamModel.png",show_shapes=True) model.summary() # train rms = RMSprop() model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy]) history=model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y, batch_size=128, epochs=epochs,verbose=2, validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)) plt.figure(figsize=(8, 4)) plt.subplot(1, 2, 1) plot_train_history(history, 'loss', 'val_loss') plt.subplot(1, 2, 2) plot_train_history(history, 'accuracy', 'val_accuracy') plt.show() # compute final accuracy on training and test sets y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]]) tr_acc = compute_accuracy(tr_y, y_pred) y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]]) te_acc = compute_accuracy(te_y, y_pred) print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc)) print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
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