fix NumPy frombuffer.

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);
+                var data = np.frombuffer(buf, (rows, cols), 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);
+                var labels = np.frombuffer(buf, new Shape(num_items), np.uint8);
 
                 if (one_hot)
                     return DenseToOneHot(labels, num_classes);

src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs

@@ -53,7 +53,7 @@ namespace Tensorflow.Eager
         public EagerTensor(Array array, Shape shape) : base(array, shape)
             => NewEagerTensorHandle(_handle);
 
-        public EagerTensor(byte[] bytes, TF_DataType dtype) : base(bytes, dtype)
+        public EagerTensor(byte[] bytes, Shape shape, TF_DataType dtype) : base(bytes, shape, dtype)
             => NewEagerTensorHandle(_handle);
 
         void NewEagerTensorHandle(IntPtr h)

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

@@ -33,9 +33,9 @@ namespace Tensorflow.NumPy
             return new NDArray(tensor);
         }
 
-        public NDArray frombuffer(byte[] bytes, TF_DataType dtype)
+        public NDArray frombuffer(byte[] bytes, Shape shape, TF_DataType dtype)
         {
-            throw new NotImplementedException("");
+            return new NDArray(bytes, shape, dtype);
         }
 
         public NDArray linspace<T>(T start, T stop, int num = 50, bool endpoint = true, bool retstep = false,

src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Numerics;
+using System.Text;
+
+namespace Tensorflow.NumPy
+{
+    public partial class np
+    {
+        public static NDArray squeeze(NDArray x1, Axis? axis = null)
+            => new NDArray(array_ops.squeeze(x1, axis));
+    }
+}

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

@@ -18,7 +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 NDArray(byte[] bytes, Shape shape, TF_DataType dtype) => Init(bytes, shape, dtype);
 
         public static NDArray Scalar<T>(T value) where T : unmanaged
             => value switch
@@ -70,9 +70,10 @@ namespace Tensorflow.NumPy
             _tensor.SetReferencedByNDArray();
         }
 
-        void Init(byte[] bytes, TF_DataType dtype)
+        void Init(byte[] bytes, Shape shape, TF_DataType dtype)
         {
-
+            _tensor = new Tensor(bytes, shape, dtype);
+            _tensor.SetReferencedByNDArray();
         }
     }
 }

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

@@ -33,8 +33,8 @@ 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 frombuffer(byte[] bytes, Shape shape, TF_DataType dtype)
+            => tf.numpy.frombuffer(bytes, shape, 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

src/TensorFlowNET.Core/Numpy/Numpy.cs

@@ -59,9 +59,6 @@ namespace Tensorflow.NumPy
         public static NDArray frombuffer(byte[] bytes, string dtype)
             => throw new NotImplementedException("");
 
-        public static NDArray squeeze(NDArray x1)
-            => throw new NotImplementedException("");
-
         public static bool allclose(NDArray a, NDArray b, double rtol = 1.0E-5, double atol = 1.0E-8,
             bool equal_nan = false) => throw new NotImplementedException("");
 

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -380,7 +380,8 @@ namespace Tensorflow
                     attr_value.List.Type.AddRange((value as IList<TF_DataType>).Select(x => _MakeType(x, attr_def)));
                     break;
                 case "list(int)":
-                    attr_value.List.I.AddRange((value as int[]).Select(x => Convert.ToInt64(x)));
+                    if (value != null)
+                        attr_value.List.I.AddRange((value as int[]).Select(x => Convert.ToInt64(x)));
                     break;
                 case "bool":
                     attr_value.B = (bool)value;

src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -96,7 +96,7 @@ namespace Tensorflow
 
         public Tensor(Shape shape, TF_DataType dtype) => InitTensor(shape, dtype);
         public Tensor(Array array, Shape? shape = null) => InitTensor(array, shape);
-        public Tensor(byte[] bytes, TF_DataType dtype) => InitTensor(bytes, dtype);
+        public Tensor(byte[] bytes, Shape shape, TF_DataType dtype) => InitTensor(shape, bytes, dtype);
 
         public Tensor(Operation op, int value_index, TF_DataType dtype)
         {
@@ -113,12 +113,12 @@ namespace Tensorflow
             isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
-        protected unsafe void InitTensor(byte[] bytes, TF_DataType dtype)
+        protected unsafe void InitTensor(Shape shape, byte[] bytes, TF_DataType dtype)
         {
             if (dtype == TF_DataType.TF_STRING)
                 _handle = StringTensor(new byte[][] { bytes }, Shape.Scalar);
             else
-                throw new NotImplementedException("");
+                _handle = TF_NewTensor(bytes, shape, dtype);
             isCreatedInGraphMode = !tf.executing_eagerly();
         }
 

src/TensorFlowNET.Core/Tensors/c_api.tensor.cs

@@ -104,13 +104,26 @@ namespace Tensorflow
             return TF_NewTensor(dataType, dims, num_dims, data, len, EmptyDeallocator, DeallocatorArgs.Empty);
         }
 
+        public static unsafe IntPtr TF_NewTensor(byte[] data, Shape shape, TF_DataType dtype)
+        {
+            var length = data.Length;
+            var handle = TF_AllocateTensor(dtype, shape.dims, shape.ndim, (ulong)length);
+            var tensor = TF_TensorData(handle);
+            if (tensor == IntPtr.Zero)
+                throw new TensorflowException("AllocateTensor failed.");
+            fixed (void* addr = &data[0])
+                System.Buffer.MemoryCopy(addr, tensor.ToPointer(), length, length);
+            return handle;
+        }
+
         public static unsafe IntPtr TF_NewTensor(Shape shape, TF_DataType dtype, void* data)
         {
             var length = shape.size * dtype.get_datatype_size();
             var handle = TF_AllocateTensor(dtype, shape.dims, shape.ndim, (ulong)length);
             var tensor = TF_TensorData(handle);
-            if (tensor != IntPtr.Zero)
-                System.Buffer.MemoryCopy(data, tensor.ToPointer(), length, length);
+            if (tensor == IntPtr.Zero)
+                throw new TensorflowException("AllocateTensor failed.");
+            System.Buffer.MemoryCopy(data, tensor.ToPointer(), length, length);
             return handle;
         }
 

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -100,7 +100,7 @@ namespace Tensorflow
 
             // non ascii char
             if (dtype == TF_DataType.TF_STRING && value is byte[] bytes)
-                return new EagerTensor(bytes, TF_DataType.TF_STRING);
+                return new EagerTensor(bytes, Shape.Scalar, TF_DataType.TF_STRING);
 
             switch (value)
             {

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -66,7 +66,7 @@ namespace Tensorflow
 
             if (shape.ndim > 0 && tensor.TensorContent.Length > 0)
             {
-                return np.frombuffer(tensor.TensorContent.ToByteArray(), tensor_dtype).reshape(shape);
+                return np.frombuffer(tensor.TensorContent.ToByteArray(), shape, tensor_dtype);
             }
             else if (tensor.Dtype == DataType.DtHalf || tensor.Dtype == DataType.DtBfloat16)
             {