diff --git a/mlair/model_modules/model_class.py b/mlair/model_modules/model_class.py index 0604c777ad56d7bb3fbda4723e39e0bfb607b5bb..4b507db3b2590dcf043f9aa8ed23935115adceef 100644 --- a/mlair/model_modules/model_class.py +++ b/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)