Upload simple_unet_model.py

simple_unet_model.py

@@ -0,0 +1,68 @@
+from keras.models import Model
+from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
+
+################################################################
+def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS):
+#Build the model
+    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
+    #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand
+    s = inputs
+
+    #Contraction path
+    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)
+    c1 = Dropout(0.1)(c1)
+    c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
+    p1 = MaxPooling2D((2, 2))(c1)
+    
+    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
+    c2 = Dropout(0.1)(c2)
+    c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
+    p2 = MaxPooling2D((2, 2))(c2)
+     
+    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
+    c3 = Dropout(0.2)(c3)
+    c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
+    p3 = MaxPooling2D((2, 2))(c3)
+     
+    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
+    c4 = Dropout(0.2)(c4)
+    c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
+    p4 = MaxPooling2D(pool_size=(2, 2))(c4)
+     
+    c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
+    c5 = Dropout(0.3)(c5)
+    c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)
+    
+    #Expansive path 
+    u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
+    u6 = concatenate([u6, c4])
+    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
+    c6 = Dropout(0.2)(c6)
+    c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
+     
+    u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
+    u7 = concatenate([u7, c3])
+    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
+    c7 = Dropout(0.2)(c7)
+    c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
+     
+    u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
+    u8 = concatenate([u8, c2])
+    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
+    c8 = Dropout(0.1)(c8)
+    c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
+     
+    u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
+    u9 = concatenate([u9, c1], axis=3)
+    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
+    c9 = Dropout(0.1)(c9)
+    c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
+     
+    outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
+     
+    model = Model(inputs=[inputs], outputs=[outputs])
+    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+    model.summary()
+    
+    return model
+