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architecture.py

@@ -4,11 +4,11 @@
 #a basic convolution block having two 3x3 convolution where each conv.
 #is followed by batch normalizaton and ReLU then a max pooling operation
 def conv_block(input, filters, pool=True):
-    x = conv2D(filters, 3, padding="same")(input)
+    x = Conv2D(filters, 3, padding="same")(input)
     x = BatchNormalization()(x)
     x = Activation("relu")(x)
 
-    x = conv2D(filters, 3, padding="same")(x)
+    x = Conv2D(filters, 3, padding="same")(x)
     x = BatchNormalization()(x)
     x = Activation("relu")(x)
 
@@ -38,6 +38,25 @@ def build_unet(shape, num_classes):
     u1 = UpSampling2D((2, 2), interpolation="bilinear")(b1)
     #contatenation with the feature man from encoder
     c1 = Concatenate()([u1, x4])
+    x5 = conv_block(c1, 64, pool=False)
 
-    if __name__ == "__main__":
-        build_unet((256, 256, 3), 3)
+    u2 = UpSampling2D((2, 2), interpolation="bilinear")(x5)
+    c2 = Concatenate()([u2, x3])
+    x6 = conv_block(c2, 48, pool=False)
+
+    u3 = UpSampling2D((2, 2), interpolation="bilinear")(x6)
+    c3 = Concatenate()([u3, x2])
+    x7 = conv_block(c3, 32, pool=False)
+
+    u4 = UpSampling2D((2, 2), interpolation="bilinear")(x7)
+    c4 = Concatenate()([u4, x1])
+    x8 = conv_block(c4, 16, pool=False)
+
+    """Output Layer"""
+    output = Conv2D(num_classes, 1, padding ="same", activation="softmax")(x8)
+
+    return Model(inputs, output)
+
+if __name__ == "__main__":
+    model = build_unet((256, 256, 3), 10)
+    model.summary()