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Unet.py
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Unet.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Apr 21 13:49:32 2019
@author: Winham
Unet.py: Unet模型定义
"""
from keras.models import Model
from keras.layers import Input, core, Dropout, concatenate
from keras.layers.convolutional import Conv1D, MaxPooling1D, UpSampling1D
def Unet(nClasses, optimizer=None, input_length=1800, nChannels=1):
inputs = Input((input_length, nChannels))
conv1 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling1D(pool_size=2)(conv1)
conv2 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Dropout(0.2)(conv2)
conv2 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling1D(pool_size=2)(conv2)
conv3 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling1D(pool_size=2)(conv3)
conv4 = Conv1D(128, 32, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Dropout(0.5)(conv4)
conv4 = Conv1D(128, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
up1 = Conv1D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling1D(size=2)(conv4))
merge1 = concatenate([up1, conv3], axis=-1)
conv5 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(merge1)
conv5 = Conv1D(64, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
up2 = Conv1D(32, 2, activation='relu', padding='same', kernel_initializer = 'he_normal')(UpSampling1D(size=2)(conv5))
merge2 = concatenate([up2, conv2], axis=-1)
conv6 = Conv1D(32, 32, activation='relu', padding='same', kernel_initializer = 'he_normal')(merge2)
conv6 = Dropout(0.2)(conv6)
conv6 = Conv1D(32, 32, activation='relu', padding='same')(conv6)
up3 = Conv1D(16, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling1D(size=2)(conv6))
merge3 = concatenate([up3, conv1], axis=-1)
conv7 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(merge3)
conv7 = Conv1D(16, 32, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv8 = Conv1D(nClasses, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv8 = core.Reshape((nClasses, input_length))(conv8)
conv8 = core.Permute((2, 1))(conv8)
conv9 = core.Activation('softmax')(conv8)
model = Model(inputs=inputs, outputs=conv9)
if not optimizer is None:
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=['accuracy'])
return model
if __name__ == '__main__':
print('\nSummarize the model:\n')
model = Unet(3)
model.summary()
print('\nEnd for summary.\n')