如何使用keras ImageDataGenerator与Siamese或Tripple网络

我正在尝试 在自定义大型数据集上建立一个连体神经网络和三重神经网络

Keras具有ImageDataGenerator这使得输入数据的生成到一个regular神经网络非常容易.

我很有兴趣使用ImageDataGenerator或类似的方式来训练具有2(siamese)和3(triple)输入的网络.

在mniset keras siamese示例中,由预处理阶段生成的输入由create_pairs方法完成.我不认为这种方式适合大型数据集.

ImageDataGenerator在这种情况下可以使用吗？假设数据集非常大,我的其他选择是什么？

DataGenerators的想法是fit_generator批量提供数据流..因此,您可以控制如何生成数据,即是从文件加载还是像执行的那样进行数据扩充ImageDataGenerator.

在这里,我发布了自定义DataGenerator的mniset siamese示例的修改版本,您可以从这里开始工作.

import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Input, Lambda
from keras.optimizers import SGD, RMSprop
from keras import backend as K

class DataGenerator(object):
    """docstring for DataGenerator"""
    def __init__(self, batch_sz):
        # 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

        # create training+test positive and negative pairs
        digit_indices = [np.where(y_train == i)[0] for i in range(10)]
        self.tr_pairs, self.tr_y = self.create_pairs(X_train, digit_indices)

        digit_indices = [np.where(y_test == i)[0] for i in range(10)]
        self.te_pairs, self.te_y = self.create_pairs(X_test, digit_indices)

        self.tr_pairs_0 = self.tr_pairs[:, 0]
        self.tr_pairs_1 = self.tr_pairs[:, 1]
        self.te_pairs_0 = self.te_pairs[:, 0]
        self.te_pairs_1 = self.te_pairs[:, 1]

        self.batch_sz = batch_sz
        self.samples_per_train  = (self.tr_pairs.shape[0]/self.batch_sz)*self.batch_sz
        self.samples_per_val    = (self.te_pairs.shape[0]/self.batch_sz)*self.batch_sz


        self.cur_train_index=0
        self.cur_val_index=0

    def create_pairs(self, 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 next_train(self):
        while 1:
            self.cur_train_index += self.batch_sz
            if self.cur_train_index >= self.samples_per_train:
                self.cur_train_index=0
            yield ([    self.tr_pairs_0[self.cur_train_index:self.cur_train_index+self.batch_sz], 
                        self.tr_pairs_1[self.cur_train_index:self.cur_train_index+self.batch_sz]
                    ],
                    self.tr_y[self.cur_train_index:self.cur_train_index+self.batch_sz]
                )

    def next_val(self):
        while 1:
            self.cur_val_index += self.batch_sz
            if self.cur_val_index >= self.samples_per_val:
                self.cur_val_index=0
            yield ([    self.te_pairs_0[self.cur_val_index:self.cur_val_index+self.batch_sz], 
                        self.te_pairs_1[self.cur_val_index:self.cur_val_index+self.batch_sz]
                    ],
                    self.te_y[self.cur_val_index:self.cur_val_index+self.batch_sz]
                )

def euclidean_distance(vects):
    x, y = vects
    return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))


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
    return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))


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()


input_dim = 784
nb_epoch = 20
batch_size=128

datagen = DataGenerator(batch_size)

# network definition
base_network = create_base_network(input_dim)

input_a = Input(shape=(input_dim,))
input_b = Input(shape=(input_dim,))

# 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=[input_a, input_b], output=distance)

# train
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms)
model.fit_generator(generator=datagen.next_train(), samples_per_epoch=datagen.samples_per_train, nb_epoch=nb_epoch, validation_data=datagen.next_val(), nb_val_samples=datagen.samples_per_val)