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
128/108400 [..............................] - ETA: 15s - loss: 0.0733 - accuracy: 0.9219
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96128/108400 [=========================>....] - ETA: 1s - loss: 0.0421 - accuracy: 0.9579
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99200/108400 [==========================>...] - ETA: 1s - loss: 0.0419 - accuracy: 0.9582
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100480/108400 [==========================>...] - ETA: 0s - loss: 0.0418 - accuracy: 0.9582
101120/108400 [==========================>...] - ETA: 0s - loss: 0.0418 - accuracy: 0.9582
101760/108400 [===========================>..] - ETA: 0s - loss: 0.0417 - accuracy: 0.9583
102400/108400 [===========================>..] - ETA: 0s - loss: 0.0417 - accuracy: 0.9584
103040/108400 [===========================>..] - ETA: 0s - loss: 0.0416 - accuracy: 0.9584
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104320/108400 [===========================>..] - ETA: 0s - loss: 0.0415 - accuracy: 0.9586
104960/108400 [============================>.] - ETA: 0s - loss: 0.0414 - accuracy: 0.9586
105600/108400 [============================>.] - ETA: 0s - loss: 0.0414 - accuracy: 0.9587
106112/108400 [============================>.] - ETA: 0s - loss: 0.0413 - accuracy: 0.9588
106752/108400 [============================>.] - ETA: 0s - loss: 0.0413 - accuracy: 0.9588
107392/108400 [============================>.] - ETA: 0s - loss: 0.0412 - accuracy: 0.9589
107904/108400 [============================>.] - ETA: 0s - loss: 0.0412 - accuracy: 0.9589
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
1408/108400 [..............................] - ETA: 9s - loss: 0.0344 - accuracy: 0.9666
2048/108400 [..............................] - ETA: 9s - loss: 0.0327 - accuracy: 0.9678
2688/108400 [..............................] - ETA: 9s - loss: 0.0328 - accuracy: 0.9684
3328/108400 [..............................] - ETA: 9s - loss: 0.0329 - accuracy: 0.9691
3968/108400 [>.............................] - ETA: 9s - loss: 0.0325 - accuracy: 0.9698
4608/108400 [>.............................] - ETA: 9s - loss: 0.0330 - accuracy: 0.9685
5248/108400 [>.............................] - ETA: 9s - loss: 0.0328 - accuracy: 0.9688
5888/108400 [>.............................] - ETA: 8s - loss: 0.0325 - accuracy: 0.9689
6528/108400 [>.............................] - ETA: 8s - loss: 0.0329 - accuracy: 0.9681
7168/108400 [>.............................] - ETA: 8s - loss: 0.0331 - accuracy: 0.9682
7808/108400 [=>............................] - ETA: 8s - loss: 0.0327 - accuracy: 0.9682
8448/108400 [=>............................] - ETA: 8s - loss: 0.0326 - accuracy: 0.9684
9088/108400 [=>............................] - ETA: 8s - loss: 0.0326 - accuracy: 0.9683
9728/108400 [=>............................] - ETA: 8s - loss: 0.0325 - accuracy: 0.9685
10368/108400 [=>............................] - ETA: 8s - loss: 0.0323 - accuracy: 0.9686
10880/108400 [==>...........................] - ETA: 8s - loss: 0.0323 - accuracy: 0.9689
11392/108400 [==>...........................] - ETA: 8s - loss: 0.0324 - accuracy: 0.9686
11904/108400 [==>...........................] - ETA: 8s - loss: 0.0325 - accuracy: 0.9683
12416/108400 [==>...........................] - ETA: 8s - loss: 0.0326 - accuracy: 0.9686
12800/108400 [==>...........................] - ETA: 8s - loss: 0.0324 - accuracy: 0.9688
13312/108400 [==>...........................] - ETA: 8s - loss: 0.0323 - accuracy: 0.9689
13824/108400 [==>...........................] - ETA: 9s - loss: 0.0323 - accuracy: 0.9690
14336/108400 [==>...........................] - ETA: 9s - loss: 0.0321 - accuracy: 0.9692
14848/108400 [===>..........................] - ETA: 9s - loss: 0.0324 - accuracy: 0.9689
15232/108400 [===>..........................] - ETA: 9s - loss: 0.0325 - accuracy: 0.9686
15616/108400 [===>..........................] - ETA: 9s - loss: 0.0325 - accuracy: 0.9684
16128/108400 [===>..........................] - ETA: 9s - loss: 0.0326 - accuracy: 0.9684
16640/108400 [===>..........................] - ETA: 9s - loss: 0.0327 - accuracy: 0.9681
17152/108400 [===>..........................] - ETA: 9s - loss: 0.0327 - accuracy: 0.9682
17664/108400 [===>..........................] - ETA: 9s - loss: 0.0328 - accuracy: 0.9680
18176/108400 [====>.........................] - ETA: 8s - loss: 0.0326 - accuracy: 0.9681
18688/108400 [====>.........................] - ETA: 8s - loss: 0.0326 - accuracy: 0.9682
19328/108400 [====>.........................] - ETA: 8s - loss: 0.0324 - accuracy: 0.9685
19968/108400 [====>.........................] - ETA: 8s - loss: 0.0325 - accuracy: 0.9682
20352/108400 [====>.........................] - ETA: 8s - loss: 0.0327 - accuracy: 0.9680
20992/108400 [====>.........................] - ETA: 8s - loss: 0.0329 - accuracy: 0.9677
21632/108400 [====>.........................] - ETA: 8s - loss: 0.0329 - accuracy: 0.9676
22144/108400 [=====>........................] - ETA: 8s - loss: 0.0327 - accuracy: 0.9676
22656/108400 [=====>........................] - ETA: 8s - loss: 0.0327 - accuracy: 0.9676
23296/108400 [=====>........................] - ETA: 8s - loss: 0.0327 - accuracy: 0.9677
23936/108400 [=====>........................] - ETA: 8s - loss: 0.0325 - accuracy: 0.9681
24576/108400 [=====>........................] - ETA: 8s - loss: 0.0322 - accuracy: 0.9685
25216/108400 [=====>........................] - ETA: 8s - loss: 0.0322 - accuracy: 0.9686
25856/108400 [======>.......................] - ETA: 8s - loss: 0.0320 - accuracy: 0.9689
26496/108400 [======>.......................] - ETA: 8s - loss: 0.0320 - accuracy: 0.9689
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27520/108400 [======>.......................] - ETA: 7s - loss: 0.0319 - accuracy: 0.9690
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28800/108400 [======>.......................] - ETA: 7s - loss: 0.0318 - accuracy: 0.9690
29440/108400 [=======>......................] - ETA: 7s - loss: 0.0317 - accuracy: 0.9691
30080/108400 [=======>......................] - ETA: 7s - loss: 0.0316 - accuracy: 0.9692
30720/108400 [=======>......................] - ETA: 7s - loss: 0.0316 - accuracy: 0.9693
31360/108400 [=======>......................] - ETA: 7s - loss: 0.0316 - accuracy: 0.9695
32000/108400 [=======>......................] - ETA: 7s - loss: 0.0316 - accuracy: 0.9693
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33280/108400 [========>.....................] - ETA: 7s - loss: 0.0316 - accuracy: 0.9693
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34560/108400 [========>.....................] - ETA: 7s - loss: 0.0315 - accuracy: 0.9694
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35712/108400 [========>.....................] - ETA: 7s - loss: 0.0315 - accuracy: 0.9695
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36736/108400 [=========>....................] - ETA: 6s - loss: 0.0315 - accuracy: 0.9695
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38912/108400 [=========>....................] - ETA: 6s - loss: 0.0315 - accuracy: 0.9695
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42624/108400 [==========>...................] - ETA: 6s - loss: 0.0314 - accuracy: 0.9695
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45824/108400 [===========>..................] - ETA: 6s - loss: 0.0313 - accuracy: 0.9694
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48384/108400 [============>.................] - ETA: 5s - loss: 0.0312 - accuracy: 0.9695
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50304/108400 [============>.................] - ETA: 5s - loss: 0.0310 - accuracy: 0.9697
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52096/108400 [=============>................] - ETA: 5s - loss: 0.0309 - accuracy: 0.9699
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102144/108400 [===========================>..] - ETA: 0s - loss: 0.0291 - accuracy: 0.9714
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103424/108400 [===========================>..] - ETA: 0s - loss: 0.0291 - accuracy: 0.9715
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104704/108400 [===========================>..] - ETA: 0s - loss: 0.0290 - accuracy: 0.9715
105344/108400 [============================>.] - ETA: 0s - loss: 0.0290 - accuracy: 0.9715
105984/108400 [============================>.] - ETA: 0s - loss: 0.0290 - accuracy: 0.9716
106624/108400 [============================>.] - ETA: 0s - loss: 0.0289 - accuracy: 0.9716
107264/108400 [============================>.] - ETA: 0s - loss: 0.0290 - accuracy: 0.9716
107904/108400 [============================>.] - ETA: 0s - loss: 0.0290 - accuracy: 0.9715
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
768/108400 [..............................] - ETA: 10s - loss: 0.0217 - accuracy: 0.9805
1408/108400 [..............................] - ETA: 9s - loss: 0.0215 - accuracy: 0.9815
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4736/108400 [>.............................] - ETA: 11s - loss: 0.0238 - accuracy: 0.9772
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6528/108400 [>.............................] - ETA: 11s - loss: 0.0239 - accuracy: 0.9767
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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
128/108400 [..............................] - ETA: 13s - loss: 0.0435 - accuracy: 0.9531
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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
128/108400 [..............................] - ETA: 16s - loss: 0.0097 - accuracy: 0.9922
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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
128/108400 [..............................] - ETA: 15s - loss: 0.0163 - accuracy: 0.9766
640/108400 [..............................] - ETA: 12s - loss: 0.0181 - accuracy: 0.9781
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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
128/108400 [..............................] - ETA: 15s - loss: 0.0073 - accuracy: 1.0000
768/108400 [..............................] - ETA: 11s - loss: 0.0111 - accuracy: 0.9883
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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
128/108400 [..............................] - ETA: 14s - loss: 0.0073 - accuracy: 0.9922
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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
128/108400 [..............................] - ETA: 17s - loss: 0.0104 - accuracy: 0.9922
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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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106240/108400 [============================>.] - ETA: 0s - loss: 0.0115 - accuracy: 0.9881
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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
128/108400 [..............................] - ETA: 17s - loss: 0.0053 - accuracy: 1.0000
640/108400 [..............................] - ETA: 13s - loss: 0.0106 - accuracy: 0.9906
1152/108400 [..............................] - ETA: 13s - loss: 0.0101 - accuracy: 0.9896
1664/108400 [..............................] - ETA: 13s - loss: 0.0102 - accuracy: 0.9892
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3456/108400 [..............................] - ETA: 11s - loss: 0.0091 - accuracy: 0.9899
4096/108400 [>.............................] - ETA: 11s - loss: 0.0100 - accuracy: 0.9893
4736/108400 [>.............................] - ETA: 10s - loss: 0.0094 - accuracy: 0.9901
5376/108400 [>.............................] - ETA: 10s - loss: 0.0096 - accuracy: 0.9898
6016/108400 [>.............................] - ETA: 10s - loss: 0.0095 - accuracy: 0.9900
6656/108400 [>.............................] - ETA: 10s - loss: 0.0098 - accuracy: 0.9899
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106368/108400 [============================>.] - ETA: 0s - loss: 0.0111 - accuracy: 0.9885
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107392/108400 [============================>.] - ETA: 0s - loss: 0.0111 - accuracy: 0.9885
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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
128/108400 [..............................] - ETA: 18s - loss: 0.0037 - accuracy: 1.0000
640/108400 [..............................] - ETA: 13s - loss: 0.0071 - accuracy: 0.9938
1152/108400 [..............................] - ETA: 14s - loss: 0.0067 - accuracy: 0.9931
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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
128/108400 [..............................] - ETA: 14s - loss: 0.0054 - accuracy: 1.0000
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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
128/108400 [..............................] - ETA: 16s - loss: 0.0033 - accuracy: 1.0000
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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%
Process finished with exit code 0
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。
