Create Network.py

Network.py

@@ -0,0 +1,33 @@
+from keras.layers import Input, Conv2D, Conv2DTranspose, Concatenate
+from keras.applications.vgg19 import VGG19
+from keras.models import Model
+
+def build_vgg():
+    vgg_model = VGG19(include_top=False, weights='imagenet')
+    vgg_model.trainable = False
+    return Model(inputs=vgg_model.input, outputs=vgg_model.get_layer('block3_conv4').output)
+
+def build_mbllen(input_shape):
+
+    def EM(input, kernal_size, channel):
+        conv_1 = Conv2D(channel, (3, 3), activation='relu', padding='same', data_format='channels_last')(input)
+        conv_2 = Conv2D(channel, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_1)
+        conv_3 = Conv2D(channel*2, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_2)
+        conv_4 = Conv2D(channel*4, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_3)
+        conv_5 = Conv2DTranspose(channel*2, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_4)
+        conv_6 = Conv2DTranspose(channel, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_5)
+        res = Conv2DTranspose(3, (kernal_size, kernal_size), activation='relu', padding='valid', data_format='channels_last')(conv_6)
+        return res
+
+    inputs = Input(shape=input_shape)
+    FEM = Conv2D(32, (3, 3), activation='relu', padding='same', data_format='channels_last')(inputs)
+    EM_com = EM(FEM, 5, 8)
+
+    for j in range(3):
+        for i in range(0, 3):
+            FEM = Conv2D(32, (3, 3), activation='relu', padding='same', data_format='channels_last')(FEM)
+            EM1 = EM(FEM, 5, 8)
+            EM_com = Concatenate(axis=3)([EM_com, EM1])
+
+    outputs = Conv2D(3, (1, 1), activation='relu', padding='same', data_format='channels_last')(EM_com)
+    return Model(inputs, outputs)