官网实例详解4.30（mnist_siamese.py）-keras学习笔记四

基于MNIST数据集上从一对数字中训练一个 Siamese MLP。

Siamese ,连体的，相似的。

Siamese Net,孪生网络、连体网络

MLP，多层感知机，（多个隐藏层的全连接的神经网络）

详解

Keras实例目录

代码注释

'''Trains a Siamese MLP on pairs of digits from the MNIST dataset.
基于MNIST数据集上从一对数字中训练一个 Siamese MLP。
Siamese网络是一种相似性度量方法,当类别数多,但每个类别的样本数量少的情况下可用于类别的识别、分类等。

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).
通过计算共享网络的输出上的欧几里德距离并通过优化对比损耗（见模式细节论文）来跟踪Ha售货等。'06（1）。
# References
参考
- Dimensionality Reduction by Learning an Invariant Mapping
基于不变映射学习的降维
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

Gets to 97.2% test accuracy after 20 epochs.
20周期后97.2%测试准确率
2 seconds per epoch on a Titan X Maxwell GPU
2秒/周期，基于Titan X Maxwell GPU （运行硬件）
'''
from __future__ import absolute_import
from __future__ import print_function
import numpy as np

import random
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
    return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), 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
    Hadsell-et-al.'06 的对比损失
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

    人工智能专家：
    Raia Hadsell
    http://raiahadsell.com/index.html

    Sumit Chopra
    https://in.linkedin.com/in/schoprasumit

    Yann LeCun
    http://yann.lecun.com/

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


def create_pairs(x, digit_indices):
    '''Positive and negative pair creation.
    正面和负面的创作。
    Alternates between positive and negative pairs.
    在正和负对之间交替。
    '''
    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).
    共享的基本网络（相当于特征提取）。
    '''
    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):
    '''Compute classification accuracy with a fixed threshold on distances.
    用固定的阈值计算距离的分类精度。
    '''
    pred = y_pred.ravel() < 0.5
    return np.mean(pred == y_true)


def accuracy(y_true, y_pred):
    '''Compute classification accuracy with a fixed threshold on distances.
    用固定的阈值计算距离的分类精度。
    '''
    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))


# the data, shuffled and split between train and test sets
# 用于训练和测试的数据集，经过了筛选（清洗、数据样本顺序打乱）和划分（划分为训练和测试集）
(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
# 创建训练+测试正负两对
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
# 网络定义
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
# 因为我们重新使用同一个实例“base_network”，网络的权重将在两个分支之间共享。
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)

# train
# 训练
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
          batch_size=128,
          epochs=epochs,
          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))

# compute final accuracy on training and test sets
# 计算训练和测试集的最终准确率
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))

代码执行

C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/mnist_siamese.py
Using TensorFlow backend.
Train on 108400 samples, validate on 17820 samples
Epoch 1/20

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108400/108400 [==============================] - 15s 138us/step - loss: 0.0948 - accuracy: 0.8871 - val_loss: 0.0449 - val_accuracy: 0.9506
Epoch 2/20

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108400/108400 [==============================] - 13s 121us/step - loss: 0.0412 - accuracy: 0.9589 - val_loss: 0.0320 - val_accuracy: 0.9685
Epoch 3/20

   128/108400 [..............................] - ETA: 13s - loss: 0.0147 - accuracy: 1.0000
   768/108400 [..............................] - ETA: 9s - loss: 0.0338 - accuracy: 0.9688 
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108400/108400 [==============================] - 11s 98us/step - loss: 0.0290 - accuracy: 0.9716 - val_loss: 0.0288 - val_accuracy: 0.9693
Epoch 4/20

   128/108400 [..............................] - ETA: 13s - loss: 0.0247 - accuracy: 0.9688
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108400/108400 [==============================] - 11s 98us/step - loss: 0.0227 - accuracy: 0.9774 - val_loss: 0.0239 - val_accuracy: 0.9727
Epoch 5/20

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108400/108400 [==============================] - 10s 95us/step - loss: 0.0196 - accuracy: 0.9801 - val_loss: 0.0253 - val_accuracy: 0.9717
Epoch 6/20

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108400/108400 [==============================] - 11s 98us/step - loss: 0.0168 - accuracy: 0.9831 - val_loss: 0.0233 - val_accuracy: 0.9749
Epoch 7/20

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108400/108400 [==============================] - 11s 101us/step - loss: 0.0156 - accuracy: 0.9839 - val_loss: 0.0224 - val_accuracy: 0.9741
Epoch 8/20

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108400/108400 [==============================] - 11s 102us/step - loss: 0.0140 - accuracy: 0.9858 - val_loss: 0.0233 - val_accuracy: 0.9721
Epoch 9/20

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108400/108400 [==============================] - 13s 124us/step - loss: 0.0131 - accuracy: 0.9868 - val_loss: 0.0226 - val_accuracy: 0.9727
Epoch 10/20

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108400/108400 [==============================] - 13s 118us/step - loss: 0.0123 - accuracy: 0.9872 - val_loss: 0.0230 - val_accuracy: 0.9735
Epoch 11/20

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108400/108400 [==============================] - 13s 120us/step - loss: 0.0120 - accuracy: 0.9874 - val_loss: 0.0233 - val_accuracy: 0.9731
Epoch 12/20

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108400/108400 [==============================] - 13s 122us/step - loss: 0.0116 - accuracy: 0.9881 - val_loss: 0.0226 - val_accuracy: 0.9730
Epoch 13/20

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108400/108400 [==============================] - 11s 102us/step - loss: 0.0111 - accuracy: 0.9885 - val_loss: 0.0226 - val_accuracy: 0.9730
Epoch 14/20

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108400/108400 [==============================] - 14s 125us/step - loss: 0.0104 - accuracy: 0.9895 - val_loss: 0.0235 - val_accuracy: 0.9738
Epoch 15/20

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108400/108400 [==============================] - 11s 98us/step - loss: 0.0101 - accuracy: 0.9897 - val_loss: 0.0246 - val_accuracy: 0.9717
Epoch 16/20

   128/108400 [..............................] - ETA: 15s - loss: 0.0023 - accuracy: 1.0000
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108400/108400 [==============================] - 13s 122us/step - loss: 0.0101 - accuracy: 0.9897 - val_loss: 0.0241 - val_accuracy: 0.9723
Epoch 17/20

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108400/108400 [==============================] - 14s 125us/step - loss: 0.0098 - accuracy: 0.9900 - val_loss: 0.0253 - val_accuracy: 0.9714
Epoch 18/20

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108400/108400 [==============================] - 14s 132us/step - loss: 0.0095 - accuracy: 0.9902 - val_loss: 0.0227 - val_accuracy: 0.9737
Epoch 19/20

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108400/108400 [==============================] - 13s 119us/step - loss: 0.0092 - accuracy: 0.9906 - val_loss: 0.0245 - val_accuracy: 0.9721
Epoch 20/20

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108400/108400 [==============================] - 13s 123us/step - loss: 0.0086 - accuracy: 0.9913 - val_loss: 0.0224 - val_accuracy: 0.9743
* Accuracy on training set: 99.58%
* Accuracy on test set: 97.43%

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Keras详细介绍

英文：https://keras.io/

中文：http://keras-cn.readthedocs.io/en/latest/

实例下载

https://github.com/keras-team/keras

https://github.com/keras-team/keras/tree/master/examples

完整项目下载

方便没积分童鞋，请加企鹅452205574，共享文件夹。

包括：代码、数据集合（图片）、已生成model、安装库文件等。