@@ -16,7 +16,7 @@ class CharRNNModel {
1616 readonly Random random = new Random ( ) ;
1717 readonly CharRNNModelParameters parameters ;
1818 readonly Func < int , RNNCell > cellFactory ;
19- readonly PythonList < RNNCell > cells = new PythonList < RNNCell > ( ) ;
19+ readonly List < RNNCell > cells = new List < RNNCell > ( ) ;
2020 readonly RNNCell rnn ;
2121 internal readonly dynamic inputData ;
2222 internal readonly seq2seqState initialState ;
@@ -65,8 +65,8 @@ public CharRNNModel(CharRNNModelParameters parameters, bool training = true) {
6565 if ( training && parameters . KeepOutputProbability < 1 )
6666 input = tf . nn . dropout ( input , parameters . KeepOutputProbability ) ;
6767
68- PythonList < Tensor > inputs = tf . split ( input , parameters . SeqLength , axis : 1 ) ;
69- inputs = inputs . Select ( i => ( Tensor ) tf . squeeze ( i , axis : 1 ) ) . ToPythonList ( ) ;
68+ IList < Tensor > inputs = tf . split ( input , parameters . SeqLength , axis : 1 ) ;
69+ inputs = inputs . Select ( i => ( Tensor ) tf . squeeze ( i , axis : 1 ) ) . ToList ( ) ;
7070
7171 dynamic Loop ( dynamic prev , dynamic _ ) {
7272 prev = tf . matmul ( prev , softmax_W ) + softmax_b ;
@@ -78,7 +78,7 @@ dynamic Loop(dynamic prev, dynamic _) {
7878 initial_state : this . initialState . Items ( ) ,
7979 cell : this . rnn ,
8080 loop_function : training ? null : PythonFunctionContainer . Of ( new Func < dynamic , dynamic , dynamic > ( Loop ) ) , scope : "rnnlm" ) ;
81- var outputs = decoder . Item1 ;
81+ IList < Tensor > outputs = decoder . Item1 ;
8282 var lastState = ( seq2seqState ) decoder . Item2 ;
8383 dynamic contatenatedOutputs = tf . concat ( outputs , 1 ) ;
8484 var output = tensorflow . tf . reshape ( contatenatedOutputs , new [ ] { - 1 , parameters . RNNSize } ) ;
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