update UNet to TF2

mlair/model_modules/model_class.py

@@ -660,16 +660,16 @@ class MyUnet(AbstractModelClass):
         p3 = keras.layers.MaxPooling2D(self.pool_size)(c3)
 
         ### own LSTM Block ###
-        ls1 = keras.layers.Reshape((p3.shape[1].value, p3.shape[-1].value))(p3)
+        ls1 = keras.layers.Reshape((p3.shape[1], p3.shape[-1]))(p3)
         ls1 = keras.layers.LSTM(64*2, return_sequences=True)(ls1)
         ls1 = keras.layers.LSTM(64*2, return_sequences=True)(ls1)
-        c4 = keras.layers.Reshape((p3.shape[1].value, 1, -1))(ls1)
+        c4 = keras.layers.Reshape((p3.shape[1], 1, -1))(ls1)
 
         ### own 2nd LSTM Block ###
-        ls2 = keras.layers.Reshape((c3.shape[1].value, c3.shape[-1].value))(c3)
+        ls2 = keras.layers.Reshape((c3.shape[1], c3.shape[-1]))(c3)
         ls2 = keras.layers.LSTM(64 * 2, return_sequences=True)(ls2)
         ls2 = keras.layers.LSTM(64 * 2, return_sequences=True)(ls2)
-        c4_2 = keras.layers.Reshape((c3.shape[1].value, 1, -1))(ls2)
+        c4_2 = keras.layers.Reshape((c3.shape[1], 1, -1))(ls2)
 
         # c4 = Padding2D("SymPad2D")(padding=pad_size)(p3)
         # c4 = keras.layers.Conv2D(128, self.kernel_size, activation=self.activation, kernel_initializer=self.kernel_initializer)(c4)