--- a
+++ b/Unet.py
@@ -0,0 +1,69 @@
+# -*- 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')