以前训练的网络被自己搞乱了还要重新训练，代码太多，又不会管理，不如上传云端吧
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 14 08:27:55 2018
@author: w
"""
import tqdm
from keras.models import Model
from keras.layers.convolutional import ZeroPadding2D,Conv2D
from keras.layers import Input,concatenate,Activation
from keras.layers.core import Dense, Dropout
from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import keras.backend as K
from keras.utils import plot_model,np_utils
import matplotlib.pyplot as plt
from keras.optimizers import SGD
from custom_layers import Scale
import os
import numpy as np
from PIL import Image
def DenseNet(nb_dense_block=2, growth_rate=32, nb_filter=64, reduction=0.0, dropout_rate=0.0, weight_decay=1e-4, classes=61583, weights_path=None):
eps = 1.1e-5
compression = 1.0 - reduction
global concat_axis
if K.image_dim_ordering() == 'tf':
concat_axis = 3
img_input = Input(shape=(45, 32, 1), name='data')
else:
concat_axis = 1
img_input = Input(shape=(1, 45, 32), name='data')
nb_filter = 64
nb_layers = [24,16]
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
x = Conv2D(nb_filter,(7, 7), name='conv1',strides=(2, 2))(x)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)
x = Scale(axis=concat_axis, name='conv1_scale')(x)
x = Activation('relu', name='relu1')(x)
x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)
for block_idx in range(nb_dense_block - 1):
stage = block_idx+2
x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate, weight_decay=weight_decay)
nb_filter = int(nb_filter * compression)
final_stage = stage + 1
x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv'+str(final_stage)+'_blk_bn')(x)
x = Scale(axis=concat_axis, name='conv'+str(final_stage)+'_blk_scale')(x)
x = Activation('relu', name='relu'+str(final_stage)+'_blk')(x)
x = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
x = Dense(61583, name='fc6')(x)
x = Activation('softmax', name='prob')(x)
model = Model(img_input, x, name='densenet')
if weights_path is not None:
model.load_weights(weights_path)
return model
def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_' + str(branch)
relu_name_base = 'relu' + str(stage) + '_' + str(branch)
inter_channel = nb_filter * 4
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)
x = Activation('relu', name=relu_name_base+'_x1')(x)
x = Conv2D(inter_channel, (1, 1), name=conv_name_base+'_x1',use_bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)
x = Activation('relu', name=relu_name_base+'_x2')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)
x = Conv2D(nb_filter,(3, 3), name=conv_name_base+'_x2', use_bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_blk'
relu_name_base = 'relu' + str(stage) + '_blk'
pool_name_base = 'pool' + str(stage)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)
x = Activation('relu', name=relu_name_base)(x)
x = Conv2D(int(nb_filter * compression),( 1, 1), name=conv_name_base, use_bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)
return x
def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = concatenate([concat_feat, x],axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
if __name__ == '__main__':
model = DenseNet( )
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
model.load_weights('D:/zhy/python_work/files/saved_models/fingerprintclass_mode.h5')
feature=Model(inputs=model.input,outputs=model.get_layer('pool3').output)
path_test='D:/zhy/python_work/data/Distence_sample/'
pics=os.listdir(path_test)
a_all=[]
for pic in tqdm.tqdm(pics):
srcs=os.listdir('D:/zhy/python_work/data/Distence_sample/'+pic)
featu=[]
for i in range(2):
picture=Image.open('D:/zhy/python_work/data/Distence_sample/'+pic+'/'+srcs[i])
arr = np.asarray(picture,dtype='float32')
arr /= 8.
arr = arr.reshape(1,45,32,1)
feature_out=feature.predict(arr)
featu.append(feature_out)
a=featu[0]-featu[1]
a=np.square(a)
a=np.sum(a)
a=np.sqrt(a)
a_all.append(a)
n_part=len(a_all)
plt.scatter(n_part, a_all)
plt.show()