Change model.build parameter to input_shape.

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -191,7 +191,7 @@ namespace Tensorflow.Keras.Engine
                 tf.Context.eager_mode(isFunc: tf.Context.is_build_function());
             }
                
-            build(inputs);
+            build(inputs.shape);
 
             if (need_restore_mode)
                 tf.Context.restore_mode();
@@ -199,7 +199,7 @@ namespace Tensorflow.Keras.Engine
             built = true;
         }
 
-        protected virtual void build(Tensors inputs)
+        public virtual void build(Shape input_shape)
         {
             built = true;
         }

src/TensorFlowNET.Keras/Layers/Activation/ELU.cs

@@ -6,30 +6,38 @@ using Tensorflow.Keras.Engine;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers {
-      /// <summary>
-      /// ELU Layer:
-      /// x = 0 when x > 0, x = alpha( e^x-1 ) elsewhere
-      /// </summary>
-      public class ELU : Layer {
-            ELUArgs args;
-            float alpha => args.Alpha;
-            public ELU ( ELUArgs args ) : base(args) {
-                  this.args = args;
-            }
-            protected override void build ( Tensors inputs ) {
-                  if ( alpha < 0f ) {
-                        throw new ValueError("Alpha must be a number greater than 0.");
-                  }
-                  built = true;
-            }
-            protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
-                  Tensor output = inputs;
-                  output = tf.where(output > 0f, output,
-                        tf.multiply(alpha, tf.sub(tf.exp(output), 1f)));
-                  return output;
-            }
-            public override Shape ComputeOutputShape ( Shape input_shape ) {
-                  return input_shape;
+    /// <summary>
+    /// ELU Layer:
+    /// x = 0 when x > 0, x = alpha( e^x-1 ) elsewhere
+    /// </summary>
+    public class ELU : Layer
+    {
+        ELUArgs args;
+        float alpha => args.Alpha;
+        public ELU(ELUArgs args) : base(args)
+        {
+            this.args = args;
+        }
+
+        public override void build(Shape input_shape)
+        {
+            if (alpha < 0f)
+            {
+                throw new ValueError("Alpha must be a number greater than 0.");
             }
-      }
+            built = true;
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            Tensor output = inputs;
+            output = tf.where(output > 0f, output,
+                  tf.multiply(alpha, tf.sub(tf.exp(output), 1f)));
+            return output;
+        }
+        public override Shape ComputeOutputShape(Shape input_shape)
+        {
+            return input_shape;
+        }
+    }
 }

src/TensorFlowNET.Keras/Layers/Activation/Exponential.cs

@@ -6,19 +6,24 @@ using Tensorflow.Keras.Engine;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers {
-      public class Exponential : Layer {
-            public Exponential ( LayerArgs args ) : base(args) {
-                  // Exponential has no args
-            }
-            protected override void build ( Tensors inputs ) {
-                  built = true;
-            }
-            protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
-                  Tensor output = inputs;
-                  return tf.exp(output);
-            }
-            public override Shape ComputeOutputShape ( Shape input_shape ) {
-                  return input_shape;
-            }
-      }
+    public class Exponential : Layer
+    {
+        public Exponential(LayerArgs args) : base(args)
+        {
+            // Exponential has no args
+        }
+        public override void build(Shape input_shape)
+        {
+            built = true;
+        }
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            Tensor output = inputs;
+            return tf.exp(output);
+        }
+        public override Shape ComputeOutputShape(Shape input_shape)
+        {
+            return input_shape;
+        }
+    }
 }

src/TensorFlowNET.Keras/Layers/Activation/SELU.cs

@@ -15,7 +15,7 @@ namespace Tensorflow.Keras.Layers {
             public SELU ( LayerArgs args ) : base(args) {
                   // SELU has no arguments
             }
-            protected override void build ( Tensors inputs ) {
+            public override void build(Shape input_shape) {
                   if ( alpha < 0f ) {
                         throw new ValueError("Alpha must be a number greater than 0.");
                   }

src/TensorFlowNET.Keras/Layers/Attention/Attention.cs

@@ -90,9 +90,10 @@ namespace Tensorflow.Keras.Layers
             }.Contains(this.score_mode))
                 throw new ValueError("Received: score_mode={score_mode}. Acceptable values are: [\"dot\", \"concat\"]");
         }
-        
+
         // Creates variable when `use_scale` is True or `score_mode` is `concat`.
-        protected override void build(Tensors inputs) {
+        public override void build(Shape input_shape)
+        {
             if (this.use_scale)
                 this.scale = this.add_weight(name: "scale",
                                              shape: 1,
@@ -110,7 +111,7 @@ namespace Tensorflow.Keras.Layers
                                                            trainable: true);
             else
                 this.concat_score_weight = null;
-            base.build(inputs);
+            base.build(input_shape);
         }
 
         /// <summary>

src/TensorFlowNET.Keras/Layers/Convolution/Conv2DTranspose.cs

@@ -29,9 +29,8 @@ namespace Tensorflow.Keras.Layers
 
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            var input_shape = inputs.shape;
             if (len(input_shape) != 4)
                 throw new ValueError($"Inputs should have rank 4. Received input shape: {input_shape}");
 
@@ -43,14 +42,12 @@ namespace Tensorflow.Keras.Layers
                 shape: kernel_shape,
                 initializer: kernel_initializer,
                 regularizer: kernel_regularizer,
-                trainable: true,
-                dtype: inputs.dtype);
+                trainable: true);
             if (use_bias)
                 bias = add_weight(name: "bias",
                 shape: filters,
                 initializer: bias_initializer,
-                trainable: true,
-                dtype: inputs.dtype);
+                trainable: true);
             built = true;
         }
 

src/TensorFlowNET.Keras/Layers/Convolution/Convolutional.cs

@@ -57,9 +57,8 @@ namespace Tensorflow.Keras.Layers
             _tf_data_format = conv_utils.convert_data_format(data_format, rank + 2);
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            Shape input_shape = inputs.shape;
             int channel_axis = data_format == "channels_first" ? 1 : -1;
             var input_channel = channel_axis < 0 ?
                 input_shape.dims[input_shape.ndim + channel_axis] :

src/TensorFlowNET.Keras/Layers/Core/Dense.cs

@@ -41,9 +41,8 @@ namespace Tensorflow.Keras.Layers
             this.inputSpec = new InputSpec(min_ndim: 2);
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            Shape input_shape = inputs.shape;
             var last_dim = input_shape.dims.Last();
             var axes = new Dictionary<int, int>();
             axes[-1] = (int)last_dim;

src/TensorFlowNET.Keras/Layers/Core/EinsumDense.cs

@@ -119,9 +119,8 @@ namespace Tensorflow.Keras.Layers
             this.bias_constraint = args.BiasConstraint;
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            var input_shape = inputs.shape;
             var shape_data = _analyze_einsum_string(this.equation, this.bias_axes, input_shape, this.partial_output_shape);
             var kernel_shape = shape_data.Item1;
             var bias_shape = shape_data.Item2;
@@ -141,7 +140,7 @@ namespace Tensorflow.Keras.Layers
                                             trainable: true);
             else
                 this.bias = null;
-            base.build(inputs);
+            base.build(input_shape);
         }
 
         public override Shape ComputeOutputShape(Shape input_shape)

src/TensorFlowNET.Keras/Layers/Core/Embedding.cs

@@ -54,7 +54,7 @@ namespace Tensorflow.Keras.Layers
             SupportsMasking = mask_zero;
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
             tf.Context.eager_mode();
             embeddings = add_weight(shape: (input_dim, output_dim),

src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs

@@ -2,49 +2,61 @@
 using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras.Layers {
-      public class Cropping1D : Layer {
-            CroppingArgs args;
-            public Cropping1D ( CroppingArgs args ) : base(args) {
-                  this.args = args;
-            }
+    public class Cropping1D : Layer
+    {
+        CroppingArgs args;
+        public Cropping1D(CroppingArgs args) : base(args)
+        {
+            this.args = args;
+        }
 
-            protected override void build ( Tensors inputs ) {
-                  if ( args.cropping.rank != 1 ) {
-                        // throw an ValueError exception
-                        throw new ValueError("");
-                  }
-                  else if ( args.cropping.shape[0] > 2 || args.cropping.shape[0] < 1 ) {
-                        throw new ValueError("The `cropping` argument must be a tuple of 2 integers.");
-                  }
-                  built = true;
+        public override void build(Shape input_shape)
+        {
+            if (args.cropping.rank != 1)
+            {
+                // throw an ValueError exception
+                throw new ValueError("");
+            }
+            else if (args.cropping.shape[0] > 2 || args.cropping.shape[0] < 1)
+            {
+                throw new ValueError("The `cropping` argument must be a tuple of 2 integers.");
             }
+            built = true;
+        }
 
-            protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
-                  Tensor output = inputs;
-                  if ( output.rank != 3 ) {
-                        // throw an ValueError exception
-                        throw new ValueError("Expected dim=3, found dim=" + output.rank);
-                  }
-                  if ( args.cropping.shape[0] == 1 ) {
-                        int crop_start = args.cropping[0];
-                        output = output[new Slice(), new Slice(crop_start, ( int ) output.shape[1] - crop_start), new Slice()];
-                  }
-                  else {
-                        int crop_start = args.cropping[0], crop_end = args.cropping[1];
-                        output = output[new Slice(), new Slice(crop_start, ( int ) (output.shape[1]) - crop_end), new Slice()];
-                  }
-                  return output;
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            Tensor output = inputs;
+            if (output.rank != 3)
+            {
+                // throw an ValueError exception
+                throw new ValueError("Expected dim=3, found dim=" + output.rank);
+            }
+            if (args.cropping.shape[0] == 1)
+            {
+                int crop_start = args.cropping[0];
+                output = output[new Slice(), new Slice(crop_start, (int)output.shape[1] - crop_start), new Slice()];
             }
+            else
+            {
+                int crop_start = args.cropping[0], crop_end = args.cropping[1];
+                output = output[new Slice(), new Slice(crop_start, (int)(output.shape[1]) - crop_end), new Slice()];
+            }
+            return output;
+        }
 
-            public override Shape ComputeOutputShape ( Shape input_shape ) {
-                  if ( args.cropping.shape[0] == 1 ) {
-                        int crop = args.cropping[0];
-                        return new Shape(( int ) (input_shape[0]), ( int ) (input_shape[1] - crop * 2), ( int ) (input_shape[2]));
-                  }
-                  else {
-                        int crop_start = args.cropping[0], crop_end = args.cropping[1];
-                        return new Shape(( int ) (input_shape[0]), ( int ) (input_shape[1] - crop_start - crop_end), ( int ) (input_shape[2]));
-                  }
+        public override Shape ComputeOutputShape(Shape input_shape)
+        {
+            if (args.cropping.shape[0] == 1)
+            {
+                int crop = args.cropping[0];
+                return new Shape((int)(input_shape[0]), (int)(input_shape[1] - crop * 2), (int)(input_shape[2]));
+            }
+            else
+            {
+                int crop_start = args.cropping[0], crop_end = args.cropping[1];
+                return new Shape((int)(input_shape[0]), (int)(input_shape[1] - crop_start - crop_end), (int)(input_shape[2]));
             }
-      }
+        }
+    }
 }

src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs

@@ -12,7 +12,7 @@ namespace Tensorflow.Keras.Layers {
             public Cropping2D ( Cropping2DArgs args ) : base(args) {
                   this.args = args;
             }
-            protected override void build ( Tensors inputs ) {
+            public override void build(Shape input_shape) {
                   built = true;
             }
             protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {

src/TensorFlowNET.Keras/Layers/Cropping/Cropping3D.cs

@@ -11,7 +11,7 @@ namespace Tensorflow.Keras.Layers {
                   this.args = args;
             }
 
-            protected override void build ( Tensors inputs ) {
+            public override void build(Shape input_shape) {
                   built = true;
             }
 

src/TensorFlowNET.Keras/Layers/Merging/Concatenate.cs

@@ -23,7 +23,7 @@ namespace Tensorflow.Keras.Layers
             this.args = args;
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
             /*var shape_set = new HashSet<Shape>();
             var reduced_inputs_shapes = inputs.Select(x => x.shape).ToArray();

src/TensorFlowNET.Keras/Layers/Merging/Merge.cs

@@ -14,7 +14,7 @@ namespace Tensorflow.Keras.Layers
 
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
             // output_shape = input_shape.dims[1^];
         }

src/TensorFlowNET.Keras/Layers/Normalization/BatchNormalization.cs

@@ -53,9 +53,8 @@ namespace Tensorflow.Keras.Layers
             axis = args.Axis.dims.Select(x => (int)x).ToArray();
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            Shape input_shape = inputs.shape;
             var ndims = input_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)

src/TensorFlowNET.Keras/Layers/Normalization/LayerNormalization.cs

@@ -49,9 +49,8 @@ namespace Tensorflow.Keras.Layers
             axis = args.Axis.axis;
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            Shape input_shape = inputs.shape;
             var ndims = input_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)

src/TensorFlowNET.Keras/Layers/Preprocessing/TextVectorization.cs

@@ -35,14 +35,14 @@ namespace Tensorflow.Keras.Layers
             var shape = data.output_shapes[0];
             if (shape.ndim == 1)
                 data = data.map(tensor => array_ops.expand_dims(tensor, -1));
-            build(data.variant_tensor);
+            build(data.variant_tensor.shape);
             var preprocessed_inputs = data.map(_preprocess);
             _index_lookup_layer.adapt(preprocessed_inputs);
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            base.build(inputs);
+            base.build(input_shape);
         }
 
         Tensors _preprocess(Tensors inputs)

src/TensorFlowNET.Keras/Layers/Reshaping/Permute.cs

@@ -7,32 +7,39 @@ using static Tensorflow.Binding;
 using Tensorflow.Keras.ArgsDefinition;
 
 namespace Tensorflow.Keras.Layers {
-      public class Permute : Layer {
-            int[] dims, permute;
-            public Permute ( PermuteArgs args ) : base(args) {
-                  this.dims = args.dims;
+    public class Permute : Layer
+    {
+        int[] dims, permute;
+        public Permute(PermuteArgs args) : base(args)
+        {
+            this.dims = args.dims;
+        }
+        public override void build(Shape input_shape)
+        {
+            var rank = input_shape.rank;
+            if (dims.Length != rank - 1)
+            {
+                throw new ValueError("Dimensions must match.");
             }
-            protected override void build ( Tensors inputs ) {
-                  var rank = inputs.rank;
-                  if ( dims.Length != rank - 1 ) {
-                        throw new ValueError("Dimensions must match.");
-                  }
-                  permute = new int[inputs.rank];
-                  dims.CopyTo(permute, 1);
-                  built = true;
+            permute = new int[input_shape.rank];
+            dims.CopyTo(permute, 1);
+            built = true;
+        }
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            Tensor outputs = inputs;
+            return tf.transpose(outputs, new Axis(permute));
+        }
+        public override Shape ComputeOutputShape(Shape input_shape)
+        {
+            Shape output_shape = new Shape(input_shape.dims);
+            for (int i = 0; i < dims.Length; i += 1)
+            {
+                var d = dims[i];
+                var target_dim = input_shape[d];
+                output_shape[i + 1] = target_dim;
             }
-            protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
-                  Tensor outputs = inputs;
-                  return tf.transpose(outputs, new Axis(permute));
-            }
-            public override Shape ComputeOutputShape ( Shape input_shape ) {
-                  Shape output_shape = new Shape(input_shape.dims);
-                  for ( int i = 0; i < dims.Length; i += 1 ) {
-                        var d = dims[i];
-                        var target_dim = input_shape[d];
-                        output_shape[i + 1] = target_dim;
-                  }
-                  return output_shape;
-            }
-      }
+            return output_shape;
+        }
+    }
 }

src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs

@@ -15,9 +15,8 @@ namespace Tensorflow.Keras.Layers.Rnn
             this.args = args;
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
-            var input_shape = inputs.shape;
             var input_dim = input_shape[-1];
 
             kernel = add_weight("kernel", (input_shape[-1], args.Units),

src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.cs

@@ -13,7 +13,7 @@ namespace Tensorflow.Keras.Layers.Rnn
 
         }
 
-        protected override void build(Tensors inputs)
+        public override void build(Shape input_shape)
         {
             
         }