论文地址：Large-scale Bisample Learning on ID vs. Spot Face Recognition
工程地址：keras-lbl-IvS
keras实现人证比对论文《Large-scale Bisample Learning on ID vs. Spot Face Recognition》的核心思想; 当类别数非常大时(超过百万)，GPU显存可能无法装载权重参数；可以使用支配原型概念，每个step将相关的类别一块训练，step开始时将当前mini-batch的类别原型权重加载到GPU中，而不用加载所有的类别权重参数; step结束时更新并保存当前训练类别的原型权重。
由于目前没有公开证件数据集;这里使用cifar10和cifar100数据集做简单的模拟测试；先说结果，直接使用cifar10数据集训练，使用imprint生成初始的cifar100数据集的类别权重；在初始的权重中测试精度为20.8%,可以说是一个不错的初始化状态；每次使用30个类别做支配原型训练，训练的精度约为80%；使用训练后的原型权重再整体测试cifar100类，最终精度为57.4%; 效果任然不太理想。一方面支配原型训练优化应该更加困难；另一方面也没有测试更多的超参数选择(支配训练类别数、支配队列大小、候选队列大小、margin、scale、batch-size、lr等等)。
但以上测试结果仍然能够说明此方法是可行的；至少在类别数特别大时(GPU无法装下),使用用支配原型权重概念，在训练时动态加载当前训练类别的权重的是一个不错的方法。
欢迎关注、反馈问题和沟通交流... ...
下图为支配原型训练的acc,loss和lr信息：
以下是测试过程，目录如下：
[TOC]
- python 3.6
- tensorflow-gpu 1.9.0
- keras 2.2.0
- faiss 1.4.0
- h5py 2.8.0
import tensorflow as tf
import numpy as np
import h5py
import keras
from keras.layers import Input
from keras.models import Model
from keras.optimizers import SGD
from keras.datasets import cifar10,cifar100
import keras.backend as K
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, TensorBoard
import sys
import os
import time
import argparse
if __package__ is None:
sys.path
sys.path.insert(0, '/opt/github/py_data_mining/face')
__package__ = "keras-lbl-IvS"
from .config import Config
from .utils import random_select, get_weights, update_weights, update_queue
from .faiss_utils import get_index, update_multi/root/anaconda3/envs/keras/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)
/root/anaconda3/envs/keras/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)
Using TensorFlow backend.
class CifarConfig(Config):
# 输入信息
input_shape = (32, 32, 3)
num_classes = 100 # cifar100
train_num_classes = 30 # 支配原型训练的类别数
# 训练参数
batch_size = 256
# 原型权重保存
prototype_weights_hdf5 = '/tmp/cifar_prototype_weights.hdf5'
prototype_weights_dataset = 'cifar_prototype_weights_set'
pw_h5_file = None # hdf5 File文件
index = None # 保存原型类别的faiss索引
dominant_queue = None # 支配队列
candidate_queue = None # 候选队列
dominant_queue_num = 20 # 支配队列大小
candidate_queue_num = 50 # 候选队列大小
config = CifarConfig()def set_gpu_growth():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
session = tf.Session(config=cfg)
K.set_session(session)
set_gpu_growth()import matplotlib.pyplot as plt
def img_show(X, rows, cols):
plt.figure(figsize=(cols * 1.5, rows))
for i, img in enumerate(X[:rows * cols]):
plt.subplot(rows, cols, 1 + i)
plt.imshow(img)
plt.show()使用cifar10 作为源数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()img_show(x_train,2,10)
骨干网络，提取特征
# 骨干网络，提取特征
import keras.layers as layers
def cifar_base_net(inputs):
"""
骨干网络、用于提取特征
:param inputs: keras Input对象
:return:
"""
x = layers.Conv2D(32, (3, 3), padding='same', name='conv_1')(inputs)
x = layers.BatchNormalization(axis=3, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(32, (3, 3), name='conv_2')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.25)(x)
x = layers.Conv2D(64, (3, 3), padding='same', name='conv_3')(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(64, (3, 3), name='conv_4')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.25)(x)
x = layers.Conv2D(512, (3, 3), name='conv_5')(x)
x = layers.Activation('relu')(x)
x = layers.GlobalAvgPool2D()(x)
return x关于AMSoftmax的详细信息见论文:Additive Margin Softmax for Face Verification
from keras.layers import Layer
class DenseWithAMSoftmaxLoss(Layer):
def __init__(self, num_classes, m=0.35, scale=30, **kwargs):
"""
根据Additive Margin Softmaxfor Face Verification论文设置m和scale
:param num_classes: 类别数
:param m: margin间隔
:param scale: 尺度
:param kwargs:
"""
self.output_dim = num_classes
self.m = m
self.scale = scale
super(DenseWithAMSoftmaxLoss, self).__init__(**kwargs)
def build(self, input_shape):
# 添加可训练参数
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='glorot_normal',
trainable=True)
def call(self, inputs):
self.inputs = inputs
# L2归一化
self.w_norm = K.tf.nn.l2_normalize(self.kernel, 0, 1e-10)
self.x_norm = K.tf.nn.l2_normalize(self.inputs, 1, 1e-10)
self.logits = K.dot(self.x_norm, self.w_norm)
return self.logits
def compute_output_shape(self, input_shape):
return (input_shape[0], # batch_size
self.output_dim)
def loss_dense(self, y_true, y_pred):
"""
AmSoftmax Loss
:param y_true: 真实类别 [N,num_classes]
:param y_pred: 预测类别[N,num_classes]
:return:
"""
y_true = K.cast(y_true, 'float32')
thelta_m = y_pred - y_true * self.m # cosine(thelta)-m ; y_true 就相当于mask
return K.categorical_crossentropy(y_true, self.scale * thelta_m, from_logits=True)from keras.models import Model
from keras.layers import Dense
from keras import Input
def source_model(img_inputs,num_class):
features=cifar_base_net(img_inputs)
dense=DenseWithAMSoftmaxLoss(num_class,m=0.2,scale=24.,name='amsoftmax') # 使用自定义的AMSoftMax分类层
output=dense(features)
m = Model(img_inputs,output)
m.compile(optimizer=keras.optimizers.Adam(lr=0.001,decay=1e-6),
loss=dense.loss_dense,
metrics=['accuracy'])
return m
img_input=Input(shape=(32,32,3))
m = source_model(img_input,10) # cifar10 一共10类
m.summary()WARNING:tensorflow:From /root/anaconda3/envs/keras/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3216: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See @{tf.nn.softmax_cross_entropy_with_logits_v2}.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
_________________________________________________________________
conv_1 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
bn_conv1 (BatchNormalization (None, 32, 32, 32) 128
_________________________________________________________________
activation_1 (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
conv_2 (Conv2D) (None, 30, 30, 32) 9248
_________________________________________________________________
activation_2 (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 15, 15, 32) 0
_________________________________________________________________
conv_3 (Conv2D) (None, 15, 15, 64) 18496
_________________________________________________________________
activation_3 (Activation) (None, 15, 15, 64) 0
_________________________________________________________________
conv_4 (Conv2D) (None, 13, 13, 64) 36928
_________________________________________________________________
activation_4 (Activation) (None, 13, 13, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 64) 0
_________________________________________________________________
conv_5 (Conv2D) (None, 4, 4, 512) 295424
_________________________________________________________________
activation_5 (Activation) (None, 4, 4, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
amsoftmax (DenseWithAMSoftma (None, 10) 5120
=================================================================
Total params: 366,240
Trainable params: 366,176
Non-trainable params: 64
_________________________________________________________________
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
m.fit(x_train, keras.utils.to_categorical(y_train,10),
batch_size=64,
epochs=10,
validation_data=(x_test, keras.utils.to_categorical(y_test,10)),
shuffle=True)Train on 50000 samples, validate on 10000 samples
Epoch 1/10
50000/50000 [==============================] - 8s 153us/step - loss: 5.6485 - acc: 0.4251 - val_loss: 4.8240 - val_acc: 0.5428
Epoch 2/10
50000/50000 [==============================] - 6s 129us/step - loss: 4.1562 - acc: 0.6204 - val_loss: 3.5912 - val_acc: 0.6834
Epoch 3/10
50000/50000 [==============================] - 7s 136us/step - loss: 3.4796 - acc: 0.6930 - val_loss: 3.1658 - val_acc: 0.7277
Epoch 4/10
50000/50000 [==============================] - 7s 137us/step - loss: 3.0975 - acc: 0.7322 - val_loss: 2.9639 - val_acc: 0.7396
Epoch 5/10
50000/50000 [==============================] - 7s 135us/step - loss: 2.8483 - acc: 0.7540 - val_loss: 3.1497 - val_acc: 0.7251
Epoch 6/10
50000/50000 [==============================] - 7s 131us/step - loss: 2.6558 - acc: 0.7757 - val_loss: 3.0899 - val_acc: 0.7297
Epoch 7/10
50000/50000 [==============================] - 6s 128us/step - loss: 2.5169 - acc: 0.7885 - val_loss: 2.6846 - val_acc: 0.7680
Epoch 8/10
50000/50000 [==============================] - 7s 136us/step - loss: 2.4056 - acc: 0.7990 - val_loss: 2.8013 - val_acc: 0.7623
Epoch 9/10
50000/50000 [==============================] - 7s 136us/step - loss: 2.3017 - acc: 0.8082 - val_loss: 2.4712 - val_acc: 0.7900
Epoch 10/10
50000/50000 [==============================] - 7s 131us/step - loss: 2.2125 - acc: 0.8167 - val_loss: 2.4762 - val_acc: 0.7867
<keras.callbacks.History at 0x7f15b406be80>
# 保存模型
m.save('/tmp/source.weights.h5')使用在源数据集cifar10上训练的模型，来评估在cifar100上的精度;评估方式是通过imprint构建初始的分类权重; imprint详见Low-Shot Learning with Imprinted Weights论文，相关代码见keras-imprinting.
(x_train, y_train), (x_test, y_test) = cifar100.load_data()img_show(x_train,2,10)
使用imprint方式将cifar100的类别原型权重保持到hdf5中(虽然这里只有100类，但是也按照类别上百万的方式来实现)
# 类别分组
def label_id_map(labels, num_class):
"""
将图像按照类别分组
"""
# 初始化
label_id_dict = dict()
for i in range(num_class):
label_id_dict[i] = []
# 逐个图像归类
for img_id in range(len(labels)):
label_id_dict[labels[img_id]].append(img_id)
return label_id_dict
label_id_dict = label_id_map(y_train[:,0], config.num_classes) # y_train (5000,1)# 初始化原型权重
def init_prototype(pretrained_weights, X, label_id_dict, num_class):
inputs = Input(batch_shape=(config.batch_size,) + config.input_shape)
features = cifar_base_net(inputs)
model = Model(inputs, features)
model.load_weights(pretrained_weights, by_name=True)
# 原型权重一份放到hdf5，一份存放到faiss中(faiss中保留的不是精准的)
if os.path.exists(config.prototype_weights_hdf5):
os.remove(config.prototype_weights_hdf5)
f_label = h5py.File(config.prototype_weights_hdf5, 'w')
label_feature = f_label.create_dataset(config.prototype_weights_dataset,
shape=(num_class, 512), dtype='f')
# 逐个类别处理
for label in range(num_class):
# 获取某个label的所有图像，并使用模型预测图像的特征，最后求均值作为label的原型权重
image_ids = label_id_dict[label] # 图像id
images = X[image_ids] # 图像数据
features = model.predict(np.asarray(images)) # 输出特征
features = keras.utils.np_utils.normalize(features) # 归一化
features = np.mean(features, axis=0) # 求均值
features = keras.utils.np_utils.normalize(features) # 再次归一化; 是二维的
# 赋值给hdf5
label_feature[label] = features[0] # (1,d) 转为 (d,)
# 每1w次，刷写到磁盘
if label % 1000 == 0:
f_label.flush()
print("{} init_prototype 完成：{}".format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
label))
f_label.flush()
# 最后关闭文件
f_label.close()init_prototype('/tmp/source.weights.h5',x_train,
label_id_dict,
config.num_classes)2019-01-14 23:11:57 init_prototype 完成：0
# 定义评估函数
from keras.layers import Dense
def evaluate(pretrained_weights, X, y, num_class, use_amsoftmax=False):
if use_amsoftmax:
dense = DenseWithAMSoftmaxLoss(num_class, m=0.2, scale=24.,name='other') # name='other'
else:
dense = layers.Dense(num_class, use_bias=False, activation='softmax')
# 构建模型
inputs = Input(shape=config.input_shape)
features = cifar_base_net(inputs)
outputs = dense(features)
m = Model(inputs, outputs)
m.load_weights(pretrained_weights, by_name=True)
# 设置分类层的权重
f_label = h5py.File(config.prototype_weights_hdf5, 'r')
weights = f_label[config.prototype_weights_dataset] # HDF5 dataset object
m.layers[-1].set_weights([np.transpose(weights[:])]) # 使用weights[:]转为numpy
# 最后关闭文件
f_label.close()
#m.summary()
# 评估预测
y_pred = m.predict(X)
y_pred = np.argmax(y_pred, axis=-1)
print("acc:{}".format(np.mean(np.equal(y,y_pred))))evaluate('/tmp/source.weights.h5', x_test,y_test[:,0],config.num_classes,True)acc:0.2081
可以看到直接使用imprint方式初始化类别权重参数，在cifar100的精度超过20%；是随机精度1%的20倍；
获取保持在hdf5中的初始原型权重;并将所有的权重加载到faiss的索引中，用于候选构造支配队列和候选队列
def get_prototype(deal_batch=1000):
f_label = h5py.File(config.prototype_weights_hdf5, 'r+')
dset = f_label[config.prototype_weights_dataset]
length = len(dset)
index = get_index(512)
# 逐个类别处理,更新faiss index
for batch_no in range(length // deal_batch):
start = batch_no * deal_batch
end = (batch_no + 1) * deal_batch
features = dset[start:end]
update_multi(index, features, np.arange(start, end))
# 处理不能整除的情况
if not length % deal_batch == 0:
start = length - length % deal_batch
end = length
features = dset[start:end]
update_multi(index, features, np.arange(start, end))
return f_label, indexconfig.pw_h5_file, config.index = get_prototype(1000)根据原型权重构造支配队列和候选队列;
def init_queue(index, weights_set, num_class, dq_num, cq_num):
"""
初始化候选队列和支配队列
:param index:
:param weights_set: h5py dataset对象
:param num_class:
:param dq_num:
:param cq_num:
:return:
"""
data, candidate_label_idx = index.search(weights_set[:num_class], cq_num + 1)
candidate_label_idx = candidate_label_idx[:, 1:] #
dominant_label_idx = candidate_label_idx[:, :dq_num] # 候选队列包含支配队列
# 转为字典类型
dominant_queue = dict(enumerate(dominant_label_idx))
candidate_queue = dict(enumerate(candidate_label_idx))
return dominant_queue, candidate_queueconfig.dominant_queue, config.candidate_queue = init_queue(config.index,
config.pw_h5_file[config.prototype_weights_dataset],
config.num_classes,
config.dominant_queue_num,
config.candidate_queue_num)关键点： a) 将需要输出的tensor保存在权重参数中 b) 使用K.tf.control_dependencies保证修动态加载原型权重参数和输出预测结果到不训练的权重中会加入到计算图中
class DenseWithDPSoftmaxLoss(Layer):
def __init__(self, num_class, bacth_size, m=0.35, scale=30, **kwargs):
self.output_dim = num_class
self.m = m
self.scale = scale
self.batch_size = bacth_size
super(DenseWithDPSoftmaxLoss, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[0][1], self.output_dim), # (嵌入维度,num_class)
dtype=K.floatx(),
initializer='glorot_normal',
constraint=keras.constraints.unit_norm(),
trainable=True)
self.current_selected_labels = self.add_weight(name='labels',
shape=(self.batch_size, 1),
initializer='glorot_normal',
trainable=False)
self.y_pred = self.add_weight(name='pred',
shape=(self.batch_size, self.output_dim),
initializer='glorot_normal',
trainable=False)
def call(self, inputs, **kwargs):
# 将当前step类别的权重，赋值给tensor
weights_assign_op = K.tf.assign(self.kernel,
K.transpose(inputs[1][:self.output_dim]),
name='assign_weights')
label_assign_op = K.tf.assign(self.current_selected_labels, inputs[2], name='assign_labels')
with K.tf.control_dependencies([weights_assign_op, label_assign_op]):
# L2归一化
self.w_norm = K.tf.nn.l2_normalize(self.kernel, 0, 1e-10)
self.x_norm = K.tf.nn.l2_normalize(inputs[0], 1, 1e-10)
self.logits = K.dot(self.x_norm, self.w_norm)
return self.logits
def compute_output_shape(self, input_shape):
return (input_shape[0][0],
self.output_dim)
# return [(input_shape[0][0], self.output_dim),
# (self.output_dim, input_shape[0][1])]
def loss(self, y_true, y_pred):
"""
DPAmSoftmax Loss
:param y_true: 真实类别 [N,num_classes]
:param y_pred: 预测类别[N,num_classes]
:return:
"""
# 首先将预测值保持到权重中
pred_assign_op = K.tf.assign(self.y_pred,
y_pred,
name='assign_pred')
with K.tf.control_dependencies([pred_assign_op]):
y_true = K.cast(y_true, 'float32')
thelta_m = y_pred - y_true * self.m # cosine(thelta)-m ; y_true 就相当于mask
losses = K.categorical_crossentropy(y_true, self.scale * thelta_m, from_logits=True)
return lossesdef target_model(pretrained_weights, batch_size,train_num_class, input_shape):
"""
目标数据集训练模型
:param pretrained_weights: 源数据集上训练的特征提取层的权重
:param batch_size:
:param input_shape:
:return:
"""
# 构建模型
inputs = Input(batch_shape=(batch_size,) + input_shape)
weights_inputs = Input(batch_shape=(train_num_class, 512))
label_inputs = Input(batch_shape=(batch_size, 1)) # 至少是二维的,
features = cifar_base_net(inputs)
dense = DenseWithDPSoftmaxLoss(train_num_class,batch_size,m=0.2,scale=24.)
outputs = dense([features, weights_inputs, label_inputs])
m = Model([inputs, weights_inputs, label_inputs], outputs)
m.load_weights(pretrained_weights, by_name=True)
# 固定参数
#for l in m.layers[:-1]:
# l.trainable = False
m.compile(loss=dense.loss,
optimizer=SGD(lr=0.2, momentum=0, decay=1e-4),
metrics=['accuracy'])
m.summary()
return mm=target_model('/tmp/source.weights.h5',
config.batch_size,config.train_num_classes, config.input_shape)__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) (64, 32, 32, 3) 0
__________________________________________________________________________________________________
conv_1 (Conv2D) (64, 32, 32, 32) 896 input_4[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (64, 32, 32, 32) 128 conv_1[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (64, 32, 32, 32) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
conv_2 (Conv2D) (64, 30, 30, 32) 9248 activation_16[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (64, 30, 30, 32) 0 conv_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D) (64, 15, 15, 32) 0 activation_17[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (64, 15, 15, 32) 0 max_pooling2d_7[0][0]
__________________________________________________________________________________________________
conv_3 (Conv2D) (64, 15, 15, 64) 18496 dropout_7[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (64, 15, 15, 64) 0 conv_3[0][0]
__________________________________________________________________________________________________
conv_4 (Conv2D) (64, 13, 13, 64) 36928 activation_18[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (64, 13, 13, 64) 0 conv_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_8 (MaxPooling2D) (64, 6, 6, 64) 0 activation_19[0][0]
__________________________________________________________________________________________________
dropout_8 (Dropout) (64, 6, 6, 64) 0 max_pooling2d_8[0][0]
__________________________________________________________________________________________________
conv_5 (Conv2D) (64, 4, 4, 512) 295424 dropout_8[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (64, 4, 4, 512) 0 conv_5[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_4 (Glo (64, 512) 0 activation_20[0][0]
__________________________________________________________________________________________________
input_5 (InputLayer) (30, 512) 0
__________________________________________________________________________________________________
input_6 (InputLayer) (64, 1) 0
__________________________________________________________________________________________________
dense_with_dp_softmax_loss_1 (D (64, 30) 17344 global_average_pooling2d_4[0][0]
input_5[0][0]
input_6[0][0]
==================================================================================================
Total params: 378,464
Trainable params: 376,416
Non-trainable params: 2,048
__________________________________________________________________________________________________
可以看到支配原型训练模型的最后的分类层输出是30个类别
def generator(X, label_id_dict, dominant_queue, num_class, train_num_class, batch_size):
"""
训练样本生成器
:param X: 图像
:param label_id_dict: 类别和对应的图像id字典
:param dominant_queue: 支配队列; 类别和对应的支配类别
:param num_class: 类别数, 这里是100
:param train_num_class: 训练类别数, 每次加载多少个类别训练
:param batch_size: batch_size
:return:
"""
while True:
# 两级采样，首先采样个train_num_class // 3正类别;然后随机采样支配类别
sample_labels = np.random.choice(num_class, train_num_class // 3, replace=False) # 无放回抽样
selected_labels = set(sample_labels) # 保存当前选中类别set
# 采样支配类别
all_current_dominant_labels = set()
for label in selected_labels:
all_current_dominant_labels = all_current_dominant_labels.union(dominant_queue[label])
all_current_dominant_labels = all_current_dominant_labels - selected_labels
selected_dominant_labels = np.random.choice(list(all_current_dominant_labels),
train_num_class - len(selected_labels),
replace=False)
all_selected_labels = selected_labels | set(selected_dominant_labels)
all_selected_labels = list(all_selected_labels) # 转为list
list.sort(all_selected_labels)
# print(all_selected_labels)
# 以下采样图像
selected_image_labels = [] # 保存当前step选中的图像和对应的类别标签
# 首先每个类别采样一次
for label in all_selected_labels:
selected_image_id = random_select(label_id_dict[label])
selected_image_labels.append([selected_image_id, label])
# 再随机采样，直到mini-batch大小
while len(selected_image_labels) < batch_size:
# 随机采样类别
label = random_select(all_selected_labels)
# 随机采样图像
selected_image_id = random_select(label_id_dict[label])
selected_image_labels.append([selected_image_id, label])
# 真实选中的标签
selected_image_labels = np.asarray(selected_image_labels) # 转为numpy数组
real_selected_labels = np.array(selected_image_labels[:, 1]) # 新建一个数组
# 将真实标签映射为当前训练的标签
for i in range(batch_size):
selected_image_labels[i][1] = all_selected_labels.index(selected_image_labels[i][1])
# 当前选中图像和标签
current_selected_labels = np.asarray(all_selected_labels)
current_weights = get_weights(config.pw_h5_file[config.prototype_weights_dataset],
current_selected_labels)
# 增加padding到batch-size大小
current_weights = np.pad(current_weights,
((0, batch_size - train_num_class), (0, 0)),
'constant')
# 加载图像
images = X[selected_image_labels[:, 0]]
# 处理mini-batch的真实标签
real_selected_labels = np.expand_dims(real_selected_labels, axis=1)
# 返回当前mini-batch
yield [images,
current_weights,
real_selected_labels], keras.utils.to_categorical(selected_image_labels[:, 1],
train_num_class) # 标签类别永远是0~batch_size-1在训练的每个step后，将此次训练的类别原型权重导出保存到hdf5中，同时更新根据预测结果更新支配队列
class ExportWeights(keras.callbacks.Callback):
def on_batch_end(self, batch, logs=None):
layer = self.model.layers[-1]
# 获取当前step训练后的权重、当前训练实际的标签类别，预测结果
trained_weights, current_trained_labels, y_pred = layer.get_weights()[:3]
trained_weights = np.transpose(trained_weights)
# l2 norm
trained_weights = trained_weights/np.linalg.norm(trained_weights, ord=2, axis=1, keepdims=True)
current_trained_labels = np.asarray(current_trained_labels[:, 0], dtype=np.int) # 二维转一维
# print("trained_weights:{}".format(trained_weights[:2][:5]))
# print("current_trained_labels:{}".format(current_trained_labels))
# 更新原型权重
update_weights(config.pw_h5_file,
config.pw_h5_file[config.prototype_weights_dataset],
trained_weights,
np.unique(current_trained_labels))
# 以下更新支配队列,根据预测结果更新支配队列;
update_queue(config.dominant_queue,
config.candidate_queue,
y_pred,
current_trained_labels)#定义callback
def get_call_back():
# 精度没有提升
lr_reducer = ReduceLROnPlateau(monitor='acc',
factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
log = TensorBoard(log_dir='log')
export_weights = ExportWeights()
return [lr_reducer, export_weights, log]print("开始训练模型... ...")
gen = generator(x_train,
label_id_dict,
config.dominant_queue,
config.num_classes,
config.train_num_classes, # 每次训练的类别数
config.batch_size)
evaluate('/tmp/source.weights.h5',
x_test,y_test[:,0],config.num_classes,True)开始训练模型... ...
m.fit_generator(gen,
callbacks=get_call_back(),
steps_per_epoch=len(x_train) // config.batch_size,
epochs=200,
use_multiprocessing=True,
# validation_data=next(gen),
verbose=1
)Epoch 1/200
195/195 [==============================] - 5s 23ms/step - loss: 8.6228 - acc: 0.0701
Epoch 2/200
195/195 [==============================] - 4s 21ms/step - loss: 6.9006 - acc: 0.3078
Epoch 3/200
195/195 [==============================] - 4s 21ms/step - loss: 6.2487 - acc: 0.4090
Epoch 4/200
195/195 [==============================] - 4s 21ms/step - loss: 5.9130 - acc: 0.4527
Epoch 5/200
195/195 [==============================] - 4s 21ms/step - loss: 5.7382 - acc: 0.4725
Epoch 6/200
195/195 [==============================] - 4s 21ms/step - loss: 5.5785 - acc: 0.4917
Epoch 7/200
195/195 [==============================] - 4s 21ms/step - loss: 5.3747 - acc: 0.5178
Epoch 8/200
195/195 [==============================] - 4s 21ms/step - loss: 5.2205 - acc: 0.5345
Epoch 9/200
195/195 [==============================] - 4s 21ms/step - loss: 5.0547 - acc: 0.5533
Epoch 10/200
195/195 [==============================] - 4s 21ms/step - loss: 4.9496 - acc: 0.5629
Epoch 11/200
195/195 [==============================] - 4s 21ms/step - loss: 4.9175 - acc: 0.5664
Epoch 12/200
195/195 [==============================] - 4s 21ms/step - loss: 4.7262 - acc: 0.5885
Epoch 13/200
195/195 [==============================] - 4s 21ms/step - loss: 4.6691 - acc: 0.5940
Epoch 14/200
195/195 [==============================] - 4s 21ms/step - loss: 4.5670 - acc: 0.6050
Epoch 15/200
195/195 [==============================] - 4s 21ms/step - loss: 4.4895 - acc: 0.6137
Epoch 16/200
195/195 [==============================] - 4s 21ms/step - loss: 4.4187 - acc: 0.6185
Epoch 17/200
195/195 [==============================] - 4s 21ms/step - loss: 4.3984 - acc: 0.6248
Epoch 18/200
195/195 [==============================] - 4s 21ms/step - loss: 4.3396 - acc: 0.6281
Epoch 19/200
195/195 [==============================] - 4s 21ms/step - loss: 4.2386 - acc: 0.6382
Epoch 20/200
195/195 [==============================] - 4s 21ms/step - loss: 4.2609 - acc: 0.6366
Epoch 21/200
195/195 [==============================] - 4s 21ms/step - loss: 4.2040 - acc: 0.6425
Epoch 22/200
195/195 [==============================] - 4s 21ms/step - loss: 4.1078 - acc: 0.6521
Epoch 23/200
195/195 [==============================] - 4s 21ms/step - loss: 4.1030 - acc: 0.6492
Epoch 24/200
195/195 [==============================] - 4s 21ms/step - loss: 4.0578 - acc: 0.6584
Epoch 25/200
195/195 [==============================] - 4s 21ms/step - loss: 3.9726 - acc: 0.6668
Epoch 26/200
195/195 [==============================] - 4s 21ms/step - loss: 3.9568 - acc: 0.6678
Epoch 27/200
195/195 [==============================] - 4s 21ms/step - loss: 3.8982 - acc: 0.6756
Epoch 28/200
195/195 [==============================] - 4s 21ms/step - loss: 3.8731 - acc: 0.6754
Epoch 29/200
195/195 [==============================] - 4s 21ms/step - loss: 3.8896 - acc: 0.6719
Epoch 30/200
195/195 [==============================] - 4s 21ms/step - loss: 3.8002 - acc: 0.6818
Epoch 31/200
195/195 [==============================] - 4s 21ms/step - loss: 3.7603 - acc: 0.6842
Epoch 32/200
195/195 [==============================] - 4s 21ms/step - loss: 3.7401 - acc: 0.6894
Epoch 33/200
195/195 [==============================] - 4s 21ms/step - loss: 3.7214 - acc: 0.6924
Epoch 34/200
195/195 [==============================] - 4s 21ms/step - loss: 3.6610 - acc: 0.6975
Epoch 35/200
195/195 [==============================] - 4s 21ms/step - loss: 3.6877 - acc: 0.6937
Epoch 36/200
195/195 [==============================] - 4s 21ms/step - loss: 3.6174 - acc: 0.7023
Epoch 37/200
195/195 [==============================] - 4s 21ms/step - loss: 3.6541 - acc: 0.6981
Epoch 38/200
195/195 [==============================] - 4s 21ms/step - loss: 3.5960 - acc: 0.7030
Epoch 39/200
195/195 [==============================] - 4s 21ms/step - loss: 3.5589 - acc: 0.7082
Epoch 40/200
195/195 [==============================] - 4s 21ms/step - loss: 3.5304 - acc: 0.7080
Epoch 41/200
195/195 [==============================] - 4s 21ms/step - loss: 3.5466 - acc: 0.7083
Epoch 42/200
195/195 [==============================] - 4s 21ms/step - loss: 3.4622 - acc: 0.7191
Epoch 43/200
195/195 [==============================] - 4s 21ms/step - loss: 3.4591 - acc: 0.7161
Epoch 44/200
195/195 [==============================] - 4s 21ms/step - loss: 3.4526 - acc: 0.7163
Epoch 45/200
195/195 [==============================] - 4s 21ms/step - loss: 3.4540 - acc: 0.7162
Epoch 46/200
195/195 [==============================] - 4s 21ms/step - loss: 3.4770 - acc: 0.7142
Epoch 47/200
195/195 [==============================] - 4s 21ms/step - loss: 3.3777 - acc: 0.7260
Epoch 48/200
195/195 [==============================] - 4s 21ms/step - loss: 3.3948 - acc: 0.7224
Epoch 49/200
195/195 [==============================] - 4s 21ms/step - loss: 3.3268 - acc: 0.7286
Epoch 50/200
195/195 [==============================] - 4s 21ms/step - loss: 3.3755 - acc: 0.7261
Epoch 51/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2830 - acc: 0.7326
Epoch 52/200
195/195 [==============================] - 4s 21ms/step - loss: 3.3017 - acc: 0.7325
Epoch 53/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2776 - acc: 0.7352
Epoch 54/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2682 - acc: 0.7354
Epoch 55/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2782 - acc: 0.7355
Epoch 56/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2301 - acc: 0.7388
Epoch 57/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2260 - acc: 0.7384
Epoch 58/200
195/195 [==============================] - 4s 21ms/step - loss: 3.2353 - acc: 0.7393
Epoch 59/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1699 - acc: 0.7463
Epoch 60/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1867 - acc: 0.7443
Epoch 61/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1513 - acc: 0.7471
Epoch 62/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1651 - acc: 0.7463
Epoch 63/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1378 - acc: 0.7481
Epoch 64/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1572 - acc: 0.7446
Epoch 65/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1101 - acc: 0.7506
Epoch 66/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1224 - acc: 0.7497
Epoch 67/200
195/195 [==============================] - 4s 21ms/step - loss: 3.1320 - acc: 0.7495
Epoch 68/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0740 - acc: 0.7520
Epoch 69/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0902 - acc: 0.7509
Epoch 70/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0828 - acc: 0.7523
Epoch 71/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0511 - acc: 0.7560
Epoch 72/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0450 - acc: 0.7584
Epoch 73/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0774 - acc: 0.7532
Epoch 74/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0108 - acc: 0.7608
Epoch 75/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0656 - acc: 0.7544
Epoch 76/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0506 - acc: 0.7548
Epoch 77/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0210 - acc: 0.7609
Epoch 78/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9919 - acc: 0.7619
Epoch 79/200
195/195 [==============================] - 4s 21ms/step - loss: 3.0089 - acc: 0.7601
Epoch 80/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9677 - acc: 0.7643
Epoch 81/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9317 - acc: 0.7677
Epoch 82/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9385 - acc: 0.7662
Epoch 83/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9403 - acc: 0.7667
Epoch 84/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9502 - acc: 0.7661
Epoch 85/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9566 - acc: 0.7648
Epoch 86/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9040 - acc: 0.7686
Epoch 87/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9097 - acc: 0.7696
Epoch 88/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9770 - acc: 0.7648
Epoch 89/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8816 - acc: 0.7713
Epoch 90/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9009 - acc: 0.7690
Epoch 91/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8859 - acc: 0.7689
Epoch 92/200
195/195 [==============================] - 4s 21ms/step - loss: 2.9151 - acc: 0.7679
Epoch 93/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8468 - acc: 0.7735
Epoch 94/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8806 - acc: 0.7716
Epoch 95/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8690 - acc: 0.7727
Epoch 96/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8607 - acc: 0.7728
Epoch 97/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8648 - acc: 0.7743
Epoch 98/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8162 - acc: 0.7769
Epoch 99/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8316 - acc: 0.7759
Epoch 100/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8379 - acc: 0.7771
Epoch 101/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7826 - acc: 0.7825
Epoch 102/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8175 - acc: 0.7772
Epoch 103/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8053 - acc: 0.7807
Epoch 104/200
195/195 [==============================] - 4s 21ms/step - loss: 2.8092 - acc: 0.7777
Epoch 105/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7802 - acc: 0.7807
Epoch 106/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7878 - acc: 0.7812
Epoch 107/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6700 - acc: 0.7913
Epoch 108/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6969 - acc: 0.7908
Epoch 109/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6961 - acc: 0.7897
Epoch 110/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7121 - acc: 0.7865
Epoch 111/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6620 - acc: 0.7920
Epoch 112/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7139 - acc: 0.7876
Epoch 113/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6802 - acc: 0.7916
Epoch 114/200
195/195 [==============================] - 4s 21ms/step - loss: 2.7220 - acc: 0.7861
Epoch 115/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6849 - acc: 0.7881
Epoch 116/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6786 - acc: 0.7906
Epoch 117/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6823 - acc: 0.7901
Epoch 118/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6086 - acc: 0.7976
Epoch 119/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6388 - acc: 0.7937
Epoch 120/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6250 - acc: 0.7950
Epoch 121/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6180 - acc: 0.7965
Epoch 122/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6573 - acc: 0.7924
Epoch 123/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6354 - acc: 0.7954
Epoch 124/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6108 - acc: 0.8001
Epoch 125/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6199 - acc: 0.7964
Epoch 126/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6237 - acc: 0.7963
Epoch 127/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5821 - acc: 0.8002
Epoch 128/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6052 - acc: 0.7995
Epoch 129/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5955 - acc: 0.7979
Epoch 130/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6088 - acc: 0.7991
Epoch 131/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5897 - acc: 0.8001
Epoch 132/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6078 - acc: 0.7975
Epoch 133/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6425 - acc: 0.7951
Epoch 134/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6338 - acc: 0.7943
Epoch 135/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6003 - acc: 0.7984
Epoch 136/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5735 - acc: 0.8002
Epoch 137/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6034 - acc: 0.7975
Epoch 138/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6422 - acc: 0.7954
Epoch 139/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6546 - acc: 0.7932
Epoch 140/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6455 - acc: 0.7960
Epoch 141/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6205 - acc: 0.7969
Epoch 142/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6054 - acc: 0.7963
Epoch 143/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5764 - acc: 0.8005
Epoch 144/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5725 - acc: 0.8014
Epoch 145/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5743 - acc: 0.8000
Epoch 146/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6077 - acc: 0.7979
Epoch 147/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6226 - acc: 0.7988
Epoch 148/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5762 - acc: 0.8017
Epoch 149/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5979 - acc: 0.7970
Epoch 150/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6239 - acc: 0.7968
Epoch 151/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6141 - acc: 0.7958
Epoch 152/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5842 - acc: 0.8003
Epoch 153/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5660 - acc: 0.8022
Epoch 154/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5796 - acc: 0.8012
Epoch 155/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6097 - acc: 0.7964
Epoch 156/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6196 - acc: 0.7968
Epoch 157/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6238 - acc: 0.7960
Epoch 158/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5960 - acc: 0.7987
Epoch 159/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6156 - acc: 0.7973
Epoch 160/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6116 - acc: 0.7976
Epoch 161/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6379 - acc: 0.7962
Epoch 162/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6249 - acc: 0.7946
Epoch 163/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5953 - acc: 0.7966
Epoch 164/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6325 - acc: 0.7950
Epoch 165/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6523 - acc: 0.7930
Epoch 166/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5807 - acc: 0.8010
Epoch 167/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6433 - acc: 0.7930
Epoch 168/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5426 - acc: 0.8030
Epoch 169/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6110 - acc: 0.7972
Epoch 170/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6340 - acc: 0.7957
Epoch 171/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6350 - acc: 0.7963
Epoch 172/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5893 - acc: 0.7995
Epoch 173/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5882 - acc: 0.7984
Epoch 174/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6131 - acc: 0.7979
Epoch 175/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5740 - acc: 0.8005
Epoch 176/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6203 - acc: 0.7960
Epoch 177/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6040 - acc: 0.7974
Epoch 178/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6296 - acc: 0.7949
Epoch 179/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6019 - acc: 0.7982
Epoch 180/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6176 - acc: 0.7980
Epoch 181/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6353 - acc: 0.7956
Epoch 182/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6288 - acc: 0.7968
Epoch 183/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6437 - acc: 0.7945
Epoch 184/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6047 - acc: 0.7967
Epoch 185/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6041 - acc: 0.7991
Epoch 186/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6201 - acc: 0.7970
Epoch 187/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6390 - acc: 0.7957
Epoch 188/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6322 - acc: 0.7966
Epoch 189/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6007 - acc: 0.7981
Epoch 190/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5960 - acc: 0.7989
Epoch 191/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5726 - acc: 0.8006
Epoch 192/200
195/195 [==============================] - 4s 21ms/step - loss: 2.5944 - acc: 0.7993
Epoch 193/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6384 - acc: 0.7954
Epoch 194/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6452 - acc: 0.7952
Epoch 195/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6178 - acc: 0.7959
Epoch 196/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6292 - acc: 0.7968
Epoch 197/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6361 - acc: 0.7950
Epoch 198/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6028 - acc: 0.7972
Epoch 199/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6190 - acc: 0.7978
Epoch 200/200
195/195 [==============================] - 4s 21ms/step - loss: 2.6297 - acc: 0.7947
<keras.callbacks.History at 0x7f14bc0b4b38>
# 保存模型
m.save('/tmp/target.weights.h5')# 最后关闭hdf5
config.pw_h5_file.close()evaluate('/tmp/target.weights.h5',
x_test,y_test[:,0],config.num_classes,True)acc:0.5741
虽然精度还不是很高(需要测试更多的超参数选择),但是可以看到从来都没使用100个类别一块训练;最终在100个类别的评估精度为0.5741