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人工智能系统实战第三期/实战代码/计算机视觉/FCN&UNet/model.py

+import numpy as np 
+import numpy as np 
+import os
+import skimage.io as io
+import skimage.transform as trans
+import numpy as np
+from keras.models import *
+from keras.layers import *
+from keras.optimizers import *
+from keras.callbacks import ModelCheckpoint, LearningRateScheduler
+from keras import backend as keras
+
+
+def unet(pretrained_weights = None,input_size = (256,256,1)):
+    inputs = Input(input_size)
+    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
+    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
+    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+
+
+
+    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
+    merge6 = concatenate([drop4,up6], axis = 3)
+    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
+    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
+
+    
+    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
+
+    model = Model(inputs=inputs, outputs=conv10)
+
+    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
+    
+    #model.summary()
+
+    if(pretrained_weights):
+    	model.load_weights(pretrained_weights)
+
+    return model
+
+