# -*- coding: utf-8 -*-
import os
from keras.layers import Flatten, Input
from keras.layers.convolutional import Conv2D, Conv2DTranspose, ZeroPadding2D
from keras.layers.core import Activation, Dense, Dropout, Permute, Reshape
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D
from keras.layers.wrappers import TimeDistributed
from keras.models import Model
from keras.regularizers import l2
from keras.utils import plot_model
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def conv_block(input, growth_rate, dropout_rate=None, weight_decay=1e-4):
x = BatchNormalization(axis=-1, epsilon=1.1e-5)(input)
x = Activation('relu')(x)
x = Conv2D(growth_rate, (3, 3),kernel_initializer='he_normal', padding='same')(x)
if(dropout_rate):
x = Dropout(dropout_rate)(x)
return x
def dense_block(x,nb_layers,nb_filter,growth_rate,droput_rate=0.2,weight_decay=1e-4):
for i in range(nb_layers):
cb = conv_block(x,growth_rate,droput_rate,weight_decay)
x = concatenate([x,cb],axis=-1)
nb_filter +=growth_rate
return x ,nb_filter
def transition_block(input,nb_filter,dropout_rate=None,pooltype=1,weight_decay=1e-4):
x = BatchNormalization(axis=-1,epsilon=1.1e-5)(input)
x = Activation('relu')(x)
x = Conv2D(nb_filter,(1,1),kernel_initializer='he_normal', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
if(dropout_rate):
x = Dropout(dropout_rate)(x)
if(pooltype==2):
x = AveragePooling2D((2,2),strides=(2,2))(x)
elif(pooltype==1):
x = ZeroPadding2D(padding=(0,1))(x)
x = AveragePooling2D((2,2),strides=(2,1))(x)
elif(pooltype==3):
x = AveragePooling2D((2,2),strides=(2,1))(x)
return x,nb_filter
def dense_cnn(input, nclass):
_dropout_rate = 0.2
_weight_decay = 1e-4
_nb_filter = 64
# conv 64 5*5 s=2
x = Conv2D(_nb_filter, (5, 5), strides=(2, 2), kernel_initializer='he_normal', padding='same',
use_bias=False, kernel_regularizer=l2(_weight_decay))(input)
# 64 + 8 * 8 = 128
x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)
#128
x, _nb_filter = transition_block(x, 128, _dropout_rate, 2, _weight_decay)
#128 + 8 * 8 = 192
x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)
#192->128
x, _nb_filter = transition_block(x, 128, _dropout_rate, 2, _weight_decay)
#128 + 8 * 8 = 192
x, _nb_filter = dense_block(x, 8, _nb_filter, 8, None, _weight_decay)
x = BatchNormalization(axis=-1, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
x = Permute((2, 1, 3), name='permute')(x)
x = TimeDistributed(Flatten(), name='flatten')(x)
y_pred = Dense(nclass, name='out', activation='softmax')(x)
basemodel = Model(inputs=input,outputs=y_pred)
basemodel.summary()
return basemodel
if __name__ == '__main__':
input = Input(shape=(32, 153, 1), name='the_input')
cnn = dense_cnn(input, 1000)
cnn.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])
plot_model(cnn, to_file='./models/densenet.png')
cnn.summary()
