Shape as_int_list

src/TensorFlowNET.Core/Data/MnistModelLoader.cs

@@ -123,7 +123,7 @@ namespace Tensorflow
 
                 bytestream.Read(buf, 0, buf.Length);
 
-                var data = np.frombuffer(buf, np.@byte.as_system_dtype());
+                var data = np.frombuffer(buf, np.@byte);
                 data = data.reshape((num_images, rows, cols, 1));
 
                 return data;
@@ -148,7 +148,7 @@ namespace Tensorflow
 
                 bytestream.Read(buf, 0, buf.Length);
 
-                var labels = np.frombuffer(buf, np.uint8.as_system_dtype());
+                var labels = np.frombuffer(buf, np.uint8);
 
                 if (one_hot)
                     return DenseToOneHot(labels, num_classes);

src/TensorFlowNET.Core/Gradients/gradients_util.cs

@@ -275,7 +275,7 @@ namespace Tensorflow
                     if (y.dtype.is_complex())
                         throw new TypeAccessException($"Gradients of complex tensors must set grad_ys (y.dtype = {y.dtype})");
                     var shape = array_ops.shape(y);
-                    var constant = constant_op.constant(y.dtype == TF_DataType.TF_DOUBLE ? (object)1.0 : (object)1.0f, name: $"grad_ys_{i}");
+                    var constant = constant_op.constant(1, y.dtype, name: $"grad_ys_{i}");
                     var fill = gen_array_ops.fill(shape, constant);
                     new_grad_ys.append(fill);
                     continue;

src/TensorFlowNET.Core/NumPy/Implementation/NumPyImpl.Creation.cs

@@ -33,6 +33,11 @@ namespace Tensorflow.NumPy
             return new NDArray(tensor);
         }
 
+        public NDArray frombuffer(byte[] bytes, TF_DataType dtype)
+        {
+            throw new NotImplementedException("");
+        }
+
         public NDArray linspace<T>(T start, T stop, int num = 50, bool endpoint = true, bool retstep = false,
             TF_DataType dtype = TF_DataType.TF_DOUBLE, int axis = 0)
         {

src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs

@@ -18,6 +18,7 @@ namespace Tensorflow.NumPy
         public NDArray(Array value, Shape? shape = null) => Init(value, shape);
         public NDArray(Shape shape, TF_DataType dtype = TF_DataType.TF_DOUBLE) => Init(shape, dtype: dtype);
         public NDArray(Tensor value, Shape? shape = null) => Init(value, shape);
+        public NDArray(byte[] bytes, TF_DataType dtype) => Init(bytes, dtype);
 
         public static NDArray Scalar<T>(T value) where T : unmanaged
             => value switch
@@ -68,5 +69,10 @@ namespace Tensorflow.NumPy
             _tensor = new Tensor(value.TensorDataPointer, shape ?? value.shape, value.dtype);
             _tensor.SetReferencedByNDArray();
         }
+
+        void Init(byte[] bytes, TF_DataType dtype)
+        {
+
+        }
     }
 }

src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs

@@ -33,6 +33,9 @@ namespace Tensorflow.NumPy
         public static NDArray full<T>(Shape shape, T fill_value)
             => new NDArray(tf.fill(tf.constant(shape), fill_value));
 
+        public static NDArray frombuffer(byte[] bytes, TF_DataType dtype)
+            => tf.numpy.frombuffer(bytes, dtype);
+
         public static NDArray linspace<T>(T start, T stop, int num = 50, bool endpoint = true, bool retstep = false,
             TF_DataType dtype = TF_DataType.TF_DOUBLE, int axis = 0) where T : unmanaged
             => tf.numpy.linspace(start, stop, num: num, endpoint: endpoint, retstep: retstep, dtype: dtype, axis: axis);

src/TensorFlowNET.Core/Numpy/Numpy.cs

@@ -56,9 +56,6 @@ namespace Tensorflow.NumPy
         public static NDArray concatenate(NDArray[] arrays, int axis = 0) 
             => throw new NotImplementedException("");
 
-        public static NDArray frombuffer(byte[] bytes, Type dtype) 
-            => throw new NotImplementedException("");
-
         public static NDArray frombuffer(byte[] bytes, string dtype)
             => throw new NotImplementedException("");
 

src/TensorFlowNET.Core/Numpy/Shape.cs

@@ -214,6 +214,11 @@ namespace Tensorflow
             return new Shape(new_dims.ToArray());
         }
 
+        public int[] as_int_list()
+        {
+            return _dims.Select(x => (int)x).ToArray();
+        }
+
         public void assert_has_rank(int rank)
         {
             if (rank != ndim)

src/TensorFlowNET.Core/Operations/array_ops.cs

@@ -591,8 +591,10 @@ namespace Tensorflow
                     var input_shape = input.shape;
                     if (optimize && input.ndim > -1 && input_shape.IsFullyDefined)
                     {
-                        var nd = np.array(input.shape.dims).astype(out_type.as_system_dtype());
-                        return constant_op.constant(nd, name: name);
+                        if(out_type == TF_DataType.TF_INT32)
+                            return constant_op.constant(input.shape.as_int_list(), name: name);
+                        else
+                            return constant_op.constant(input.shape.dims, name: name);
                     }
                 }
 

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -60,11 +60,11 @@ namespace Tensorflow
 
         public static NDArray MakeNdarray(TensorProto tensor)
         {
-            var shape = tensor.TensorShape.Dim.Select(x => (int)x.Size).ToArray();
-            int num_elements = np.prod(shape);
-            var tensor_dtype = tensor.Dtype.as_numpy_dtype();
+            var shape = new Shape(tensor.TensorShape.Dim.Select(x => x.Size).ToArray());
+            var num_elements = shape.size;
+            var tensor_dtype = tensor.Dtype.as_tf_dtype();
 
-            if (shape.Length > 0 && tensor.TensorContent.Length > 0)
+            if (shape.ndim > 0 && tensor.TensorContent.Length > 0)
             {
                 return np.frombuffer(tensor.TensorContent.ToByteArray(), tensor_dtype).reshape(shape);
             }