change tensorflow to non-static class in order to execute some initialization.

src/KerasNET.Core/Core.cs

@@ -1,4 +1,5 @@
 using Tensorflow;
+using static Tensorflow.Binding;
 
 namespace Keras
 {

src/KerasNET.Core/Layers/Dense.cs

@@ -19,6 +19,7 @@ using Tensorflow;
 using static Keras.Keras;
 using NumSharp;
 using Tensorflow.Operations.Activation;
+using static Tensorflow.Binding;
 
 namespace Keras.Layers
 {

src/KerasNET.Core/Model.cs

@@ -19,7 +19,7 @@ using NumSharp;
 using System;
 using System.Collections.Generic;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Keras
 {

src/TensorFlowNET.Core/APIs/tf.array.cs

@@ -20,7 +20,7 @@ using System.Linq;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
         /// <summary>
         /// Concatenates tensors along one dimension.
@@ -29,7 +29,7 @@ namespace Tensorflow
         /// <param name="axis"></param>
         /// <param name="name"></param>
         /// <returns>A `Tensor` resulting from concatenation of the input tensors.</returns>
-        public static Tensor concat(IList<Tensor> values, int axis, string name = "concat")
+        public Tensor concat(IList<Tensor> values, int axis, string name = "concat")
         {
             if (values.Count == 1)
                 throw new NotImplementedException("tf.concat length is 1");
@@ -48,7 +48,7 @@ namespace Tensorflow
         /// A `Tensor` with the same data as `input`, but its shape has an additional
         /// dimension of size 1 added.
         /// </returns>
-        public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
+        public Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
             => array_ops.expand_dims(input, axis, name, dim);
 
         /// <summary>
@@ -58,14 +58,14 @@ namespace Tensorflow
         /// <param name="value"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor fill<T>(Tensor dims, T value, string name = null)
+        public Tensor fill<T>(Tensor dims, T value, string name = null)
             => gen_array_ops.fill(dims, value, name: name);
 
         /// <summary>
         /// Return the elements, either from `x` or `y`, depending on the `condition`.
         /// </summary>
         /// <returns></returns>
-        public static Tensor where<Tx, Ty>(Tensor condition, Tx x, Ty y, string name = null)
+        public Tensor where<Tx, Ty>(Tensor condition, Tx x, Ty y, string name = null)
             => array_ops.where(condition, x, y, name);
 
         /// <summary>
@@ -76,10 +76,10 @@ namespace Tensorflow
         /// <param name="name"></param>
         /// <param name="conjugate"></param>
         /// <returns></returns>
-        public static Tensor transpose<T1>(T1 a, int[] perm = null, string name = "transpose", bool conjugate = false)
+        public Tensor transpose<T1>(T1 a, int[] perm = null, string name = "transpose", bool conjugate = false)
             => array_ops.transpose(a, perm, name, conjugate);
 
-        public static Tensor squeeze(Tensor input, int[] axis = null, string name = null, int squeeze_dims = -1)
+        public Tensor squeeze(Tensor input, int[] axis = null, string name = null, int squeeze_dims = -1)
             => gen_array_ops.squeeze(input, axis, name);
 
         /// <summary>
@@ -89,10 +89,10 @@ namespace Tensorflow
         /// <param name="axis"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor stack(object values, int axis = 0, string name = "stack")
+        public Tensor stack(object values, int axis = 0, string name = "stack")
             => array_ops.stack(values, axis, name: name);
 
-        public static Tensor one_hot(Tensor indices, int depth,
+        public Tensor one_hot(Tensor indices, int depth,
             Tensor on_value = null,
             Tensor off_value = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
@@ -110,7 +110,7 @@ namespace Tensorflow
         /// </param>
         /// <param name="name">A name for the operation (optional).</param>
         /// <returns>A `Tensor`. Has the same type as `input`.</returns>
-        public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = null)
+        public Tensor placeholder_with_default<T>(T input, int[] shape, string name = null)
             => gen_array_ops.placeholder_with_default(input, shape, name: name);
     }
 }

src/TensorFlowNET.Core/APIs/tf.control.cs

@@ -18,9 +18,9 @@ using System;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor while_loop(Func<Tensor, Tensor> cond, Func<Tensor, Tensor> body, Tensor[] loop_vars,
+        public Tensor while_loop(Func<Tensor, Tensor> cond, Func<Tensor, Tensor> body, Tensor[] loop_vars,
             TensorShape shape_invariants = null,
             int parallel_iterations = 10,
             bool back_prop = true,
@@ -37,7 +37,7 @@ namespace Tensorflow
                 maximum_iterations: maximum_iterations,
                 return_same_structure: return_same_structure);
 
-        public static _ControlDependenciesController control_dependencies(Operation[] control_inputs) 
+        public _ControlDependenciesController control_dependencies(Operation[] control_inputs) 
             => ops.control_dependencies(control_inputs);
     }
 }

src/TensorFlowNET.Core/APIs/tf.distributions.cs

@@ -16,11 +16,13 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static class distributions
+        public distributions_internal distributions { get; } = new distributions_internal();
+
+        public class distributions_internal
         {
-            public static Normal Normal(Tensor loc,
+            public Normal Normal(Tensor loc,
                 Tensor scale,
                 bool validate_args = false,
                 bool allow_nan_stats = true,

src/TensorFlowNET.Core/APIs/tf.exp.cs

@@ -16,9 +16,9 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor exp(Tensor x,
+        public Tensor exp(Tensor x,
                 string name = null) => gen_math_ops.exp(x, name);
 
     }

src/TensorFlowNET.Core/APIs/tf.gradients.cs

@@ -16,9 +16,9 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor[] gradients(Tensor[] ys,
+        public Tensor[] gradients(Tensor[] ys,
             Tensor[] xs,
             Tensor[] grad_ys = null,
             string name = "gradients",
@@ -36,7 +36,7 @@ namespace Tensorflow
                 stop_gradients: stop_gradients);
         }
 
-        public static Tensor[] gradients(Tensor ys,
+        public Tensor[] gradients(Tensor ys,
             Tensor[] xs,
             Tensor[] grad_ys = null,
             string name = "gradients",
@@ -54,7 +54,7 @@ namespace Tensorflow
                 stop_gradients: stop_gradients);
         }
 
-        public static Tensor[] gradients(Tensor ys,
+        public Tensor[] gradients(Tensor ys,
             Tensor xs,
             Tensor[] grad_ys = null,
             string name = "gradients",

src/TensorFlowNET.Core/APIs/tf.graph.cs

@@ -16,15 +16,15 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static graph_util_impl graph_util => new graph_util_impl();
-        public static Graph get_default_graph()
+        public graph_util_impl graph_util => new graph_util_impl();
+        public Graph get_default_graph()
         {
             return ops.get_default_graph();
         }
 
-        public static Graph Graph() 
+        public Graph Graph() 
             => new Graph();
     }
 }

src/TensorFlowNET.Core/APIs/tf.init.cs

@@ -18,14 +18,14 @@ using Tensorflow.Operations.Initializers;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static IInitializer zeros_initializer => new Zeros();
-        public static IInitializer ones_initializer => new Ones();
-        public static IInitializer glorot_uniform_initializer => new GlorotUniform();
-        public static IInitializer uniform_initializer => new RandomUniform();
+        public IInitializer zeros_initializer => new Zeros();
+        public IInitializer ones_initializer => new Ones();
+        public IInitializer glorot_uniform_initializer => new GlorotUniform();
+        public IInitializer uniform_initializer => new RandomUniform();
 
-        public static variable_scope variable_scope(string name,
+        public variable_scope variable_scope(string name,
                string default_name = null,
                Tensor[] values = null,
                bool? reuse = null,
@@ -35,7 +35,7 @@ namespace Tensorflow
                    reuse: reuse,
                    auxiliary_name_scope: auxiliary_name_scope);
 
-        public static variable_scope variable_scope(VariableScope scope,
+        public variable_scope variable_scope(VariableScope scope,
               string default_name = null,
               Tensor[] values = null,
               bool? reuse = null,
@@ -45,7 +45,7 @@ namespace Tensorflow
                   reuse: reuse,
                   auxiliary_name_scope: auxiliary_name_scope);
 
-        public static IInitializer truncated_normal_initializer(float mean = 0.0f,
+        public IInitializer truncated_normal_initializer(float mean = 0.0f,
             float stddev = 1.0f,
             int? seed = null,
             TF_DataType dtype = TF_DataType.DtInvalid) => new TruncatedNormal(mean: mean,

src/TensorFlowNET.Core/APIs/tf.io.cs

@@ -19,14 +19,14 @@ using Tensorflow.IO;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static GFile gfile = new GFile();
-        public static Tensor read_file(string filename, string name = null) => gen_io_ops.read_file(filename, name);
+        public GFile gfile = new GFile();
+        public Tensor read_file(string filename, string name = null) => gen_io_ops.read_file(filename, name);
 
-        public static gen_image_ops image => new gen_image_ops();
+        public gen_image_ops image => new gen_image_ops();
 
-        public static void import_graph_def(GraphDef graph_def,
+        public void import_graph_def(GraphDef graph_def,
             Dictionary<string, Tensor> input_map = null,
             string[] return_elements = null,
             string name = null,

src/TensorFlowNET.Core/APIs/tf.layers.cs

@@ -16,14 +16,17 @@
 
 using Tensorflow.Keras.Layers;
 using Tensorflow.Operations.Activation;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static class layers
+        public layers_internal layers { get; } = new layers_internal();
+
+        public class layers_internal
         {
-            public static Tensor conv2d(Tensor inputs,
+            public Tensor conv2d(Tensor inputs,
                 int filters,
                 int[] kernel_size,
                 int[] strides = null,
@@ -80,7 +83,7 @@ namespace Tensorflow
             /// <param name="renorm"></param>
             /// <param name="renorm_momentum"></param>
             /// <returns></returns>
-            public static Tensor batch_normalization(Tensor inputs,
+            public Tensor batch_normalization(Tensor inputs,
                 int axis = -1,
                 float momentum = 0.99f,
                 float epsilon = 0.001f,
@@ -124,7 +127,7 @@ namespace Tensorflow
             /// <param name="data_format"></param>
             /// <param name="name"></param>
             /// <returns></returns>
-            public static Tensor max_pooling2d(Tensor inputs,
+            public Tensor max_pooling2d(Tensor inputs,
                 int[] pool_size,
                 int[] strides,
                 string padding = "valid",
@@ -140,7 +143,7 @@ namespace Tensorflow
                 return layer.apply(inputs);
             }
 
-            public static Tensor dense(Tensor inputs,
+            public Tensor dense(Tensor inputs,
                 int units,
                 IActivation activation = null,
                 bool use_bias = true,

src/TensorFlowNET.Core/APIs/tf.linalg.cs

@@ -16,15 +16,15 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor diag(Tensor diagonal, string name = null)
+        public Tensor diag(Tensor diagonal, string name = null)
             => gen_array_ops.diag(diagonal, name: name);
 
-        public static Tensor matmul(Tensor a, Tensor b) 
+        public Tensor matmul(Tensor a, Tensor b) 
             => gen_math_ops.mat_mul(a, b);
 
-        public static Tensor batch_matmul(Tensor x, Tensor y)
+        public Tensor batch_matmul(Tensor x, Tensor y)
             => gen_math_ops.batch_mat_mul(x, y);
     }
 }

src/TensorFlowNET.Core/APIs/tf.loss.cs

@@ -16,8 +16,8 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static LossesImpl losses => new LossesImpl();
+        public LossesImpl losses => new LossesImpl();
     }
 }

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -16,9 +16,9 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor abs(Tensor x, string name = null)
+        public Tensor abs(Tensor x, string name = null)
             => math_ops.abs(x, name);
 
         /// <summary>
@@ -27,7 +27,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor acos(Tensor x, string name = null)
+        public Tensor acos(Tensor x, string name = null)
             => gen_math_ops.acos(x, name);
 
         /// <summary>
@@ -36,10 +36,10 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor asin(Tensor x, string name = null)
+        public Tensor asin(Tensor x, string name = null)
             => gen_math_ops.asin(x, name);
 
-        public static Tensor add<Tx, Ty>(Tx a, Ty b, string name = null) 
+        public Tensor add<Tx, Ty>(Tx a, Ty b, string name = null) 
             => gen_math_ops.add(a, b, name: name);
 
         /// <summary>
@@ -48,19 +48,19 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor atan(Tensor x, string name = null)
+        public Tensor atan(Tensor x, string name = null)
             => gen_math_ops.atan(x, name);
 
-        public static Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+        public Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
             => gen_math_ops.arg_max(input, dimension, output_type: output_type, name: name);
 
-        public static Tensor arg_min(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+        public Tensor arg_min(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
             => gen_math_ops.arg_min(input, dimension, output_type: output_type, name: name);
 
-        public static Tensor is_finite(Tensor input, string name = null)
+        public Tensor is_finite(Tensor input, string name = null)
             => gen_math_ops.is_finite(input, name);
 
-        public static Tensor is_nan(Tensor input, string name = null)
+        public Tensor is_nan(Tensor input, string name = null)
             => gen_math_ops.is_nan(input, name);
 
         /// <summary>
@@ -69,7 +69,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor ceil(Tensor x, string name = null)
+        public Tensor ceil(Tensor x, string name = null)
             => gen_math_ops.ceil(x, name);
 
         /// <summary>
@@ -78,7 +78,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor sin(Tensor x, string name = null)
+        public Tensor sin(Tensor x, string name = null)
             => gen_math_ops.sin(x, name);
 
         /// <summary>
@@ -87,7 +87,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor sinh(Tensor x, string name = null)
+        public Tensor sinh(Tensor x, string name = null)
             => gen_math_ops.sinh(x, name);
 
         /// <summary>
@@ -96,7 +96,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor cos(Tensor x, string name = null)
+        public Tensor cos(Tensor x, string name = null)
             => gen_math_ops.cos(x, name);
 
         /// <summary>
@@ -105,13 +105,13 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor cosh(Tensor x, string name = null)
+        public Tensor cosh(Tensor x, string name = null)
             => gen_math_ops.cosh(x, name);
 
-        public static Tensor tan(Tensor x, string name = null)
+        public Tensor tan(Tensor x, string name = null)
             => gen_math_ops.tan(x, name);
 
-        public static Tensor tanh(Tensor x, string name = null)
+        public Tensor tanh(Tensor x, string name = null)
             => gen_math_ops.tanh(x, name);
 
         /// <summary>
@@ -120,7 +120,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor floor(Tensor x, string name = null)
+        public Tensor floor(Tensor x, string name = null)
             => gen_math_ops.floor(x, name);
 
         /// <summary>
@@ -132,7 +132,7 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor greater<Tx, Ty>(Tx x, Ty y, string name = null)
+        public Tensor greater<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.greater(x, y, name);
 
         /// <summary>
@@ -144,7 +144,7 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor greater_equal<Tx, Ty>(Tx x, Ty y, string name = null)
+        public Tensor greater_equal<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.greater_equal(x, y, name);
 
         /// <summary>
@@ -156,7 +156,7 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
+        public Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.less(x, y, name);
 
         /// <summary>
@@ -165,7 +165,7 @@ namespace Tensorflow
         /// <param name="x">A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.</param>
         /// <param name="name">A name for the operation (optional).</param>
         /// <returns>A `Tensor`. Has the same type as `x`.</returns>
-        public static Tensor lgamma(Tensor x, string name = null)
+        public Tensor lgamma(Tensor x, string name = null)
             => gen_math_ops.lgamma(x, name: name);
 
         /// <summary>
@@ -177,7 +177,7 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor less_equal<Tx, Ty>(Tx x, Ty y, string name = null)
+        public Tensor less_equal<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.less_equal(x, y, name);
 
         /// <summary>
@@ -186,19 +186,19 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor log1p(Tensor x, string name = null)
+        public Tensor log1p(Tensor x, string name = null)
             => gen_math_ops.log1p(x, name);
 
-        public static Tensor logical_and(Tensor x, Tensor y, string name = null)
+        public Tensor logical_and(Tensor x, Tensor y, string name = null)
             => gen_math_ops.logical_and(x, y, name);
 
-        public static Tensor logical_not(Tensor x, string name = null)
+        public Tensor logical_not(Tensor x, string name = null)
             => gen_math_ops.logical_not(x, name);
 
-        public static Tensor logical_or(Tensor x, Tensor y, string name = null)
+        public Tensor logical_or(Tensor x, Tensor y, string name = null)
             => gen_math_ops.logical_or(x, y, name);
 
-        public static Tensor logical_xor(Tensor x, Tensor y, string name = "LogicalXor")
+        public Tensor logical_xor(Tensor x, Tensor y, string name = "LogicalXor")
             => gen_math_ops.logical_xor(x, y, name);
 
         /// <summary>
@@ -209,28 +209,28 @@ namespace Tensorflow
         /// <param name="clip_value_max"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = null)
+        public Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = null)
             => gen_math_ops._clip_by_value(t, clip_value_min, clip_value_max);
 
-        public static Tensor sub(Tensor a, Tensor b) 
+        public Tensor sub(Tensor a, Tensor b) 
             => gen_math_ops.sub(a, b);
 
-        public static Tensor divide(Tensor a, Tensor b)
+        public Tensor divide(Tensor a, Tensor b)
             => gen_math_ops.real_div(a, b);
 
-        public static Tensor sqrt(Tensor a, string name = null) 
+        public Tensor sqrt(Tensor a, string name = null) 
             => gen_math_ops.sqrt(a, name);
 
-        public static Tensor sign(Tensor a, string name = null)
+        public Tensor sign(Tensor a, string name = null)
             => gen_math_ops.sign(a, name);
 
-        public static Tensor subtract<T>(Tensor x, T[] y, string name = null) where T : struct
+        public Tensor subtract<T>(Tensor x, T[] y, string name = null) where T : struct
             => gen_math_ops.sub(x, ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y"), name);
 
-        public static Tensor log(Tensor x, string name = null)
+        public Tensor log(Tensor x, string name = null)
             => gen_math_ops.log(x, name);
 
-        public static Tensor equal(Tensor x, Tensor y, string name = null)
+        public Tensor equal(Tensor x, Tensor y, string name = null)
             => gen_math_ops.equal(x, y, name);
 
         /// <summary>
@@ -240,7 +240,7 @@ namespace Tensorflow
         /// <param name="x"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor atan2(Tensor y, Tensor x, string name = null)
+        public Tensor atan2(Tensor y, Tensor x, string name = null)
             => gen_math_ops.atan2(y, x, name);
 
         /// <summary>
@@ -253,7 +253,7 @@ namespace Tensorflow
         /// <param name="keep_dims"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor max<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
+        public Tensor max<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
             => gen_math_ops._max(input, axis, keep_dims: keep_dims, name: name);
 
         /// <summary>
@@ -266,7 +266,7 @@ namespace Tensorflow
         /// <param name="keep_dims"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor min<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
+        public Tensor min<Tx, Ty>(Tx input, Ty axis, bool keep_dims = false, string name = null)
             => gen_math_ops._min(input, axis, keep_dims: keep_dims, name: name);
 
         /// <summary>
@@ -278,7 +278,7 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
+        public Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
             => gen_math_ops.maximum(x, y, name: name);
 
         /// <summary>
@@ -290,13 +290,13 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor minimum<T1, T2>(T1 x, T2 y, string name = null)
+        public Tensor minimum<T1, T2>(T1 x, T2 y, string name = null)
             => gen_math_ops.minimum(x, y, name: name);
 
-        public static Tensor multiply<Tx, Ty>(Tx x, Ty y) 
+        public Tensor multiply<Tx, Ty>(Tx x, Ty y) 
             => gen_math_ops.mul(x, y);
 
-        public static Tensor negative(Tensor x, string name = null)
+        public Tensor negative(Tensor x, string name = null)
             => gen_math_ops.neg(x, name);
 
         /// <summary>
@@ -306,13 +306,13 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor div(Tensor x, Tensor y, string name = null)
+        public Tensor div(Tensor x, Tensor y, string name = null)
             => math_ops.div(x, y, name: name);
 
-        public static Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
+        public Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
             => x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
 
-        public static Tensor pow<T1, T2>(T1 x, T2 y) 
+        public Tensor pow<T1, T2>(T1 x, T2 y) 
             => gen_math_ops.pow(x, y);
 
         /// <summary>
@@ -321,14 +321,14 @@ namespace Tensorflow
         /// <param name="input"></param>
         /// <param name="axis"></param>
         /// <returns></returns>
-        public static Tensor reduce_sum(Tensor input, int? axis = null, int? reduction_indices = null)
+        public Tensor reduce_sum(Tensor input, int? axis = null, int? reduction_indices = null)
         {
             if(!axis.HasValue && reduction_indices.HasValue)
                 return math_ops.reduce_sum(input, reduction_indices.Value);
             return math_ops.reduce_sum(input);
         }
 
-        public static Tensor reduce_sum(Tensor input, int axis, int? reduction_indices = null)
+        public Tensor reduce_sum(Tensor input, int axis, int? reduction_indices = null)
             => math_ops.reduce_sum(input, axis);
 
         /// <summary>
@@ -339,34 +339,34 @@ namespace Tensorflow
         /// <param name="keepdims"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+        public Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
             => math_ops.reduce_max(input_tensor, axis, keepdims, name);
 
-        public static Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+        public Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
             => math_ops.reduce_min(input_tensor, axis, keepdims, name);
 
-        public static Tensor sigmoid<T>(T x, string name = null)
+        public Tensor sigmoid<T>(T x, string name = null)
             => math_ops.sigmoid(x, name: name);
 
-        public static Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
+        public Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
             => gen_math_ops._sum(input, axis, keep_dims: keep_dims, name: name);
 
-        public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
+        public Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
             => math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name, reduction_indices: reduction_indices);
 
-        public static Tensor round(Tensor x, string name = null)
+        public Tensor round(Tensor x, string name = null)
             => gen_math_ops.round(x, name: name);
 
-        public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null) 
+        public Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null) 
             => math_ops.cast(x, dtype, name);
 
-        public static Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
+        public Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
             => math_ops.cumsum(x, axis: axis, exclusive: exclusive, reverse: reverse, name: name);
 
-        public static Tensor argmax(Tensor input, int axis = -1, string name = null, int? dimension = null, TF_DataType output_type = TF_DataType.TF_INT64)
+        public Tensor argmax(Tensor input, int axis = -1, string name = null, int? dimension = null, TF_DataType output_type = TF_DataType.TF_INT64)
             => gen_math_ops.arg_max(input, axis, name: name, output_type: output_type);
 
-        public static Tensor square(Tensor x, string name = null)
+        public Tensor square(Tensor x, string name = null)
             => gen_math_ops.square(x, name: name);
     }
 }

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -20,11 +20,13 @@ using static Tensorflow.Python;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static class nn
+        public nn_internal nn { get; } = new nn_internal();
+
+        public class nn_internal
         {
-            public static Tensor conv2d(Tensor input, RefVariable filter, int[] strides, string padding, bool use_cudnn_on_gpu = true, 
+            public Tensor conv2d(Tensor input, RefVariable filter, int[] strides, string padding, bool use_cudnn_on_gpu = true, 
                 string data_format= "NHWC", int[] dilations= null, string name = null)
             {
                 var parameters = new Conv2dParams
@@ -54,7 +56,7 @@ namespace Tensorflow
             /// <param name="name"></param>
             /// <param name="rate">A scalar `Tensor` with the same type as `x`.</param>
             /// <returns>A Tensor of the same shape of `x`.</returns>
-            public static Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = null, int? seed = null, string name = null,
+            public Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = null, int? seed = null, string name = null,
                 float? rate = null)
             {
                 Tensor keep = null;
@@ -74,17 +76,17 @@ namespace Tensorflow
             /// <param name="swap_memory"></param>
             /// <param name="time_major"></param>
             /// <returns>A pair (outputs, state)</returns>
-            public static (Tensor, Tensor) dynamic_rnn(RNNCell cell, Tensor inputs,
+            public (Tensor, Tensor) dynamic_rnn(RNNCell cell, Tensor inputs,
                 Tensor sequence_length = null, TF_DataType dtype = TF_DataType.DtInvalid,
                 int? parallel_iterations = null, bool swap_memory = false, bool time_major = false)
                 => rnn.dynamic_rnn(cell, inputs, sequence_length: sequence_length, dtype: dtype,
                     parallel_iterations: parallel_iterations, swap_memory: swap_memory,
                     time_major: time_major);
 
-            public static Tensor elu(Tensor features, string name = null)
+            public Tensor elu(Tensor features, string name = null)
                 => gen_nn_ops.elu(features, name: name);
 
-            public static (Tensor, Tensor) moments(Tensor x,
+            public (Tensor, Tensor) moments(Tensor x,
                 int[] axes,
                 string name = null,
                 bool keep_dims = false) => nn_impl.moments(x, 
@@ -92,7 +94,7 @@ namespace Tensorflow
                     name: name, 
                     keep_dims: keep_dims);
 
-            public static Tensor embedding_lookup(RefVariable @params,
+            public Tensor embedding_lookup(RefVariable @params,
                 Tensor ids,
                 string partition_strategy = "mod",
                 string name = null) => embedding_ops._embedding_lookup_and_transform(@params,
@@ -100,7 +102,7 @@ namespace Tensorflow
                     partition_strategy: partition_strategy,
                     name: name);
 
-            public static Tensor embedding_lookup(Tensor @params,
+            public Tensor embedding_lookup(Tensor @params,
                 Tensor ids,
                 string partition_strategy = "mod",
                 string name = null) => embedding_ops._embedding_lookup_and_transform(new Tensor[] { @params },
@@ -108,11 +110,11 @@ namespace Tensorflow
                     partition_strategy: partition_strategy,
                     name: name);
 
-            public static IActivation relu() => new relu();
+            public IActivation relu() => new relu();
 
-            public static Tensor relu(Tensor features, string name = null) => gen_nn_ops.relu(features, name);
+            public Tensor relu(Tensor features, string name = null) => gen_nn_ops.relu(features, name);
 
-            public static Tensor[] fused_batch_norm(Tensor x,
+            public Tensor[] fused_batch_norm(Tensor x,
                 RefVariable scale,
                 RefVariable offset,
                 Tensor mean = null,
@@ -126,18 +128,18 @@ namespace Tensorflow
                     is_training: is_training,
                     name: name);
 
-            public static IPoolFunction max_pool_fn => new MaxPoolFunction();
+            public IPoolFunction max_pool_fn => new MaxPoolFunction();
 
-            public static Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null) 
+            public Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null) 
                 => nn_ops.max_pool(value, ksize, strides, padding, data_format: data_format, name: name);
 
-            public static Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = "InTopK")
+            public Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = "InTopK")
                 => gen_ops.in_top_k(predictions, targets, k, name);
 
-            public static Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
+            public Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
                 => gen_nn_ops.top_kv2(input, k: k, sorted: sorted, name: name);
 
-            public static Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
+            public Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
             {
                 return Python.tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
                 {
@@ -146,9 +148,9 @@ namespace Tensorflow
                 });
             }
 
-            public static rnn_cell_impl rnn_cell => new rnn_cell_impl();
+            public rnn_cell_impl rnn_cell => new rnn_cell_impl();
 
-            public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
+            public Tensor softmax(Tensor logits, int axis = -1, string name = null)
                 => gen_nn_ops.softmax(logits, name);
 
             /// <summary>
@@ -158,7 +160,7 @@ namespace Tensorflow
             /// <param name="logits"></param>
             /// <param name="name"></param>
             /// <returns></returns>
-            public static Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = null,
+            public Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = null,
             Tensor logits = null, string name = null)
                 => nn_ops.sparse_softmax_cross_entropy_with_logits(labels: labels, logits: logits, name: name);
 
@@ -170,7 +172,7 @@ namespace Tensorflow
             /// <param name="dim"></param>
             /// <param name="name"></param>
             /// <returns></returns>
-            public static Tensor softmax_cross_entropy_with_logits(Tensor labels, Tensor logits, int dim = -1, string name = null)
+            public Tensor softmax_cross_entropy_with_logits(Tensor labels, Tensor logits, int dim = -1, string name = null)
             {
                 tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
                 {
@@ -181,7 +183,7 @@ namespace Tensorflow
                 return softmax_cross_entropy_with_logits_v2(labels, logits, axis: dim, name: name);
             }
 
-            public static Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)
+            public Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)
                 => nn_ops.softmax_cross_entropy_with_logits_v2_helper(labels, logits, axis: axis, name: name);
         }
     }

src/TensorFlowNET.Core/APIs/tf.ops.cs

@@ -16,12 +16,12 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor assign(Tensor @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null) 
+        public Tensor assign(Tensor @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null) 
             => state_ops.assign(@ref, value, validate_shape, use_locking, name);
 
-        public static object get_collection(string key, string scope = "") 
+        public object get_collection(string key, string scope = "") 
             => get_default_graph().get_collection(key, scope: scope);
 
         /// <summary>
@@ -31,7 +31,7 @@ namespace Tensorflow
         /// <param name="default_name">The default name to use if the name argument is None.</param>
         /// <param name="values">The list of Tensor arguments that are passed to the op function.</param>
         /// <returns>The scope name.</returns>
-        public static ops.NameScope name_scope(string name, string default_name = "", object values = null) 
+        public ops.NameScope name_scope(string name, string default_name = "", object values = null) 
             => new ops.NameScope(name, default_name, values);
     }
 }

src/TensorFlowNET.Core/APIs/tf.random.cs

@@ -16,7 +16,7 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
         /// <summary>
         /// Outputs random values from a normal distribution.
@@ -28,21 +28,21 @@ namespace Tensorflow
         /// <param name="seed"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor random_normal(int[] shape,
+        public Tensor random_normal(int[] shape,
             float mean = 0.0f,
             float stddev = 1.0f,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
             string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
 
-        public static Tensor random_uniform(int[] shape,
+        public Tensor random_uniform(int[] shape,
             float minval = 0,
             float maxval = 1,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
             string name = null) => random_ops.random_uniform(shape, minval, maxval, dtype, seed, name);
 
-        public static Tensor truncated_normal(int[] shape,
+        public Tensor truncated_normal(int[] shape,
             float mean = 0.0f,
             float stddev = 1.0f,
             TF_DataType dtype = TF_DataType.TF_FLOAT,

src/TensorFlowNET.Core/APIs/tf.reduce_logsumexp.cs

@@ -16,9 +16,9 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor reduce_logsumexp(Tensor input_tensor,
+        public Tensor reduce_logsumexp(Tensor input_tensor,
                 int[] axis = null,
                 bool keepdims = false,
                 string name = null) => math_ops.reduce_logsumexp(input_tensor, axis, keepdims, name);

src/TensorFlowNET.Core/APIs/tf.reshape.cs

@@ -16,13 +16,13 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor reshape(Tensor tensor,
+        public Tensor reshape(Tensor tensor,
                 Tensor shape,
                 string name = null) => gen_array_ops.reshape(tensor, shape, name);
 
-        public static Tensor reshape(Tensor tensor,
+        public Tensor reshape(Tensor tensor,
                 int[] shape,
                 string name = null) => gen_array_ops.reshape(tensor, shape, name);
     }

src/TensorFlowNET.Core/APIs/tf.summary.cs

@@ -16,11 +16,11 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Summaries.Summary summary = new Summaries.Summary();
+        public Summaries.Summary summary = new Summaries.Summary();
 
-        public static Tensor scalar(string name, Tensor tensor) 
+        public Tensor scalar(string name, Tensor tensor) 
             => summary.scalar(name, tensor);
     }
 }

src/TensorFlowNET.Core/APIs/tf.tensor.cs

@@ -16,12 +16,12 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor convert_to_tensor(object value,
+        public Tensor convert_to_tensor(object value,
             string name = null) => ops.convert_to_tensor(value, name: name);
 
-        public static Tensor strided_slice(Tensor input, Tensor begin, Tensor end, Tensor strides = null,
+        public Tensor strided_slice(Tensor input, Tensor begin, Tensor end, Tensor strides = null,
             int begin_mask = 0,
             int end_mask = 0,
             int ellipsis_mask = 0,
@@ -38,7 +38,7 @@ namespace Tensorflow
                 shrink_axis_mask: shrink_axis_mask,
                 name: name);
 
-        public static Tensor strided_slice<T>(Tensor input, T[] begin, T[] end, T[] strides = null,
+        public Tensor strided_slice<T>(Tensor input, T[] begin, T[] end, T[] strides = null,
             int begin_mask = 0,
             int end_mask = 0,
             int ellipsis_mask = 0,

src/TensorFlowNET.Core/APIs/tf.tile.cs

@@ -18,12 +18,12 @@ using NumSharp;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static Tensor tile(Tensor input,
+        public Tensor tile(Tensor input,
                 Tensor multiples,
                 string name = null) => gen_array_ops.tile(input, multiples, name);
-        public static Tensor tile(NDArray input,
+        public Tensor tile(NDArray input,
                 int[] multiples,
                 string name = null) => gen_array_ops.tile(input, multiples, name);
 

src/TensorFlowNET.Core/APIs/tf.variable.cs

@@ -18,21 +18,21 @@ using System.Collections.Generic;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static VariableV1[] global_variables(string scope = null)
+        public VariableV1[] global_variables(string scope = null)
         {
             return (ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope) as List<VariableV1>)
                 .ToArray();
         }
 
-        public static Operation global_variables_initializer()
+        public Operation global_variables_initializer()
         {
             var g = variables.global_variables();
             return variables.variables_initializer(g.ToArray());
         }
 
-        public static RefVariable get_variable(string name,
+        public RefVariable get_variable(string name,
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
             object initializer = null, // IInitializer or Tensor

src/TensorFlowNET.Core/Binding.cs

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public static class Binding
+    {
+        public static tensorflow tf { get; } = Python.New<tensorflow>();
+    }
+}

src/TensorFlowNET.Core/Clustering/KMeans.cs

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Clustering
 {

src/TensorFlowNET.Core/Framework/meta_graph.py.cs

@@ -22,6 +22,7 @@ using System.Linq;
 using Tensorflow.Operations;
 using static Tensorflow.CollectionDef;
 using static Tensorflow.MetaGraphDef.Types;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Gradients/array_grad.cs

@@ -18,6 +18,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Framework;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Gradients
 {

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -18,6 +18,7 @@ using System;
 using System.Linq;
 using Tensorflow.Operations;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Gradients
 {

src/TensorFlowNET.Core/Graphs/DefaultGraphStack.cs

@@ -16,6 +16,7 @@
 
 using System.Collections.Generic;
 using System.Linq;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Graphs/Graph.Operation.cs

@@ -18,6 +18,7 @@ using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Runtime.InteropServices;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/IPyClass.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
         /// Called when the instance is created.
         /// </summary>
         /// <param name="args"></param>
-        void __init__(IPyClass self, dynamic args);
+        void __init__(IPyClass self);
 
         void __enter__(IPyClass self);
 

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

@@ -17,6 +17,7 @@
 using System;
 using System.Linq;
 using Tensorflow.Keras.Utils;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
 {

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

@@ -19,7 +19,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Operations.Activation;
-using static Tensorflow.tf;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -86,7 +86,7 @@ namespace Tensorflow.Keras.Layers
             }
 
             if (use_bias)
-                outputs = nn.bias_add(outputs, bias);
+                outputs = tf.nn.bias_add(outputs, bias);
             if (activation != null)
                 return activation.Activate(outputs);
 

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

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using static Tensorflow.Binding;
+
 namespace Tensorflow.Keras.Layers
 {
     public class Embedding : Layer

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

@@ -21,6 +21,7 @@ using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;
 using Tensorflow.Train;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
 {

src/TensorFlowNET.Core/Keras/Layers/MaxPooling2D.cs

@@ -1,4 +1,4 @@
-using static Tensorflow.tf;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -9,7 +9,7 @@ namespace Tensorflow.Keras.Layers
             int[] strides,
             string padding = "valid",
             string data_format = null,
-            string name = null) : base(nn.max_pool_fn, pool_size,
+            string name = null) : base(tf.nn.max_pool_fn, pool_size,
                 strides,
                 padding: padding,
                 data_format: data_format,

src/TensorFlowNET.Core/Keras/Utils/base_layer_utils.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Utils
 {

src/TensorFlowNET.Core/Keras/backend.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Collections.Generic;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras
 {
@@ -28,7 +29,7 @@ namespace Tensorflow.Keras
         //Func<Array, bool> py_any = any;
         //Func<double, double, double, IEnumerable<double>> py_slice = slice;
 
-        public static Session _SESSION = Tensorflow.tf.defaultSession;
+        public static Session _SESSION = tf.defaultSession;
         public static Graph _GRAPH = null;
         public static Dictionary<Graph, GraphLearningPhase> _GRAPH_LEARNING_PHASES;
         //Dictionary<Graph, Dictionary<string, int>> PER_GRAPH_LAYER_NAME_UIDS;

src/TensorFlowNET.Core/Keras/tf.keras.cs

@@ -2,9 +2,9 @@
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static class keras
+        public class keras
         {
             public static Initializers initializers => new Initializers();
         }

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Collections.Generic;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Layers
 {

src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Collections.Generic;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Operations/NnOps/rnn.cs

@@ -19,7 +19,7 @@ using System.Collections.Generic;
 using System.Linq;
 using static Tensorflow.Python;
 using Tensorflow.Util;
-using NumSharp;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Operations
 {

src/TensorFlowNET.Core/Operations/array_ops.py.cs

@@ -18,6 +18,7 @@ using NumSharp;
 using System;
 using System.Collections.Generic;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Operations/gen_image_ops.py.cs

@@ -16,6 +16,7 @@
 
 using System;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Python.cs

@@ -66,12 +66,10 @@ namespace Tensorflow
             return Enumerable.Range(start, end - start);
         }
 
-        public static T New<T>(object args) where T : IPyClass
+        public static T New<T>() where T : IPyClass, new()
         {
-            var instance = Activator.CreateInstance<T>();
-
-            instance.__init__(instance, args);
-
+            var instance = new T();
+            instance.__init__(instance);
             return instance;
         }
 
@@ -347,9 +345,4 @@ namespace Tensorflow
 
         void __exit__();
     }
-
-    public class PyObject<T> where T : IPyClass
-    {
-        public T Instance { get; set; }
-    }
 }

src/TensorFlowNET.Core/Sessions/Session.cs

@@ -15,7 +15,7 @@
 ******************************************************************************/
 
 using System;
-using System.Runtime.InteropServices;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -16,6 +16,7 @@
 
 using System;
 using System.Collections.Generic;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Tensors/tf.constant.cs

@@ -18,11 +18,11 @@ using NumSharp;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
         // public static Tensor constant(NDArray nd, string name = "Const") => constant_op.constant(nd, name: name);
 
-        public static Tensor constant(object value,
+        public Tensor constant(object value,
             TF_DataType dtype = TF_DataType.DtInvalid,
             int[] shape = null,
             string name = "Const",
@@ -33,30 +33,30 @@ namespace Tensorflow
                 verify_shape: verify_shape,
                 allow_broadcast: false);
 
-        public static Tensor constant(string value,
+        public Tensor constant(string value,
             string name = "Const") => constant_op._constant_impl(value,
-                tf.@string,
+                @string,
                 new int[] { 1 },
                 name,
                 verify_shape: false,
                 allow_broadcast: false);
 
-        public static Tensor constant(float value,
+        public Tensor constant(float value,
             int shape,
             string name = "Const") => constant_op._constant_impl(value,
-                tf.float32,
+                float32,
                 new int[] { shape },
                 name,
                 verify_shape: false,
                 allow_broadcast: false);
 
-        public static Tensor zeros(TensorShape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) 
+        public Tensor zeros(TensorShape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) 
             => array_ops.zeros(shape, dtype, name);
 
-        public static Tensor ones(TensorShape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) 
+        public Tensor ones(TensorShape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) 
             => array_ops.ones(shape, dtype, name);
 
-        public static Tensor size(Tensor input,
+        public Tensor size(Tensor input,
             string name = null,
             TF_DataType out_type = TF_DataType.TF_INT32) => array_ops.size(input,
                 name,

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -20,6 +20,7 @@ using System.Linq;
 using Tensorflow.Framework;
 using Tensorflow.Train;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Train/Saving/Saver.cs

@@ -19,6 +19,7 @@ using System.Collections.Generic;
 using System.IO;
 using System.Linq;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Train/SlotCreator.cs

@@ -17,6 +17,7 @@
 using System;
 using Tensorflow.Operations.Initializers;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Train
 {

src/TensorFlowNET.Core/Train/tf.optimizers.cs

@@ -19,28 +19,30 @@ using Tensorflow.Train;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow
     {
-        public static class train
+        public train_internal train { get; } = new train_internal();
+
+        public class train_internal
         {
-            public static Optimizer GradientDescentOptimizer(float learning_rate) 
+            public Optimizer GradientDescentOptimizer(float learning_rate) 
                 => new GradientDescentOptimizer(learning_rate);
 
-            public static Optimizer AdamOptimizer(float learning_rate, string name = "Adam") 
+            public Optimizer AdamOptimizer(float learning_rate, string name = "Adam") 
                 => new AdamOptimizer(learning_rate, name: name);
 
-            public static Saver Saver(VariableV1[] var_list = null) => new Saver(var_list: var_list);
+            public Saver Saver(VariableV1[] var_list = null) => new Saver(var_list: var_list);
 
-            public static string write_graph(Graph graph, string logdir, string name, bool as_text = true) 
+            public string write_graph(Graph graph, string logdir, string name, bool as_text = true) 
                 => graph_io.write_graph(graph, logdir, name, as_text);
 
-            public static Saver import_meta_graph(string meta_graph_or_file,
+            public Saver import_meta_graph(string meta_graph_or_file,
                 bool clear_devices = false,
                 string import_scope = "") => saver._import_meta_graph_with_return_elements(meta_graph_or_file,
                     clear_devices,
                     import_scope).Item1;
 
-            public static (MetaGraphDef, Dictionary<string, RefVariable>) export_meta_graph(string filename = "",
+            public (MetaGraphDef, Dictionary<string, RefVariable>) export_meta_graph(string filename = "",
                 bool as_text = false,
                 bool clear_devices = false,
                 bool clear_extraneous_savers = false,

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -16,6 +16,7 @@
 
 using System;
 using System.Collections.Generic;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/ops.py.cs

@@ -21,6 +21,7 @@ using Google.Protobuf;
 using System.Linq;
 using NumSharp;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/tf.cs → src/TensorFlowNET.Core/tensorflow.cs

@@ -18,25 +18,25 @@ using Tensorflow.Eager;
 
 namespace Tensorflow
 {
-    public static partial class tf
+    public partial class tensorflow : IPyClass
     {
-        public static TF_DataType @byte = TF_DataType.TF_UINT8;
-        public static TF_DataType @sbyte = TF_DataType.TF_INT8;
-        public static TF_DataType int16 = TF_DataType.TF_INT16;
-        public static TF_DataType int32 = TF_DataType.TF_INT32;
-        public static TF_DataType int64 = TF_DataType.TF_INT64;
-        public static TF_DataType float16 = TF_DataType.TF_HALF;
-        public static TF_DataType float32 = TF_DataType.TF_FLOAT;
-        public static TF_DataType float64 = TF_DataType.TF_DOUBLE;
-        public static TF_DataType @bool = TF_DataType.TF_BOOL;
-        public static TF_DataType chars = TF_DataType.TF_STRING;
-        public static TF_DataType @string = TF_DataType.TF_STRING;
-
-        public static Context context = new Context(new ContextOptions(), new Status());
-
-        public static Session defaultSession;
-
-        public static RefVariable Variable<T>(T data,
+        public TF_DataType @byte = TF_DataType.TF_UINT8;
+        public TF_DataType @sbyte = TF_DataType.TF_INT8;
+        public TF_DataType int16 = TF_DataType.TF_INT16;
+        public TF_DataType int32 = TF_DataType.TF_INT32;
+        public TF_DataType int64 = TF_DataType.TF_INT64;
+        public TF_DataType float16 = TF_DataType.TF_HALF;
+        public TF_DataType float32 = TF_DataType.TF_FLOAT;
+        public TF_DataType float64 = TF_DataType.TF_DOUBLE;
+        public TF_DataType @bool = TF_DataType.TF_BOOL;
+        public TF_DataType chars = TF_DataType.TF_STRING;
+        public TF_DataType @string = TF_DataType.TF_STRING;
+
+        public Context context = new Context(new ContextOptions(), new Status());
+
+        public Session defaultSession;
+
+        public RefVariable Variable<T>(T data,
             bool trainable = true,
             bool validate_shape = true,
             string name = null,
@@ -49,33 +49,53 @@ namespace Tensorflow
                 dtype: dtype);
         }
 
-        public static unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
+        public unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
         {
             return gen_array_ops.placeholder(dtype, shape, name);
         }
 
-        public static void enable_eager_execution()
+        public void enable_eager_execution()
         {
             // contex = new Context();
             context.default_execution_mode = Context.EAGER_MODE;
         }
 
-        public static string VERSION => c_api.StringPiece(c_api.TF_Version());
+        public string VERSION => c_api.StringPiece(c_api.TF_Version());
 
-        public static Session Session()
+        public Session Session()
         {
             defaultSession = new Session();
             return defaultSession;
         }
 
-        public static Session Session(Graph graph)
+        public Session Session(Graph graph)
         {
             return new Session(graph);
         }
 
-        public static Session Session(SessionOptions opts)
+        public Session Session(SessionOptions opts)
         {
             return new Session(null, opts);
         }
+
+        public void __init__(IPyClass self)
+        {
+            
+        }
+
+        public void __enter__(IPyClass self)
+        {
+            
+        }
+
+        public void __exit__(IPyClass self)
+        {
+            
+        }
+
+        public void __del__(IPyClass self)
+        {
+            
+        }
     }
 }

src/TensorFlowNet.Benchmarks/TensorBenchmark.cs

@@ -2,6 +2,7 @@
 using BenchmarkDotNet.Attributes;
 using NumSharp;
 using Tensorflow;
+using static Tensorflow.Binding;
 
 namespace TensorFlowBenchmark
 {

test/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs

@@ -6,7 +6,6 @@ open NumSharp
 open Tensorflow
 open System
 
-
 let run()=
     
     let N_points = 75 // Number of points for constructing function
@@ -40,6 +39,7 @@ let run()=
     let n_hidden_layer_1 = 25 // Hidden layer 1
     let n_hidden_layer_2 = 25 // Hidden layer 2
 
+    let tf = Python.New<tensorflow>()
     let x = tf.placeholder(tf.float64, new TensorShape(N_points,n_input))
     let y = tf.placeholder(tf.float64, new TensorShape(n_output))
     

test/TensorFlowNET.Examples/BasicEagerApi.cs

@@ -1,5 +1,6 @@
 using System;
 using Tensorflow;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs

@@ -20,6 +20,7 @@ using System.Diagnostics;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs

@@ -18,6 +18,7 @@ using NumSharp;
 using System;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs

@@ -21,6 +21,7 @@ using System.IO;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs

@@ -18,7 +18,7 @@ using System;
 using System.Collections.Generic;
 using Tensorflow;
 using NumSharp;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 using System.IO;
 using TensorFlowNET.Examples.Utility;
 

test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs

@@ -19,6 +19,7 @@ using System;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs

@@ -18,7 +18,7 @@ using System;
 using NumSharp;
 using Tensorflow;
 using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -1,7 +1,7 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -95,7 +95,7 @@ namespace TensorFlowNET.Examples
                 Console.WriteLine(result.ToString()); // ==> [[ 12.]]
             };
 
-            // `BatchMatMul` is actually embedded into the `MatMul` operation on the tensorflow.dll side. Every time we ask
+            // `BatchMatMul` is actually embedded into the `MatMul` operation on the tf.dll side. Every time we ask
             // for a multiplication between matrices with rank > 2, the first rank - 2 dimensions are checked to be consistent
             // across the two matrices and a common matrix multiplication is done on the residual 2 dimensions.
             //

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -1,6 +1,6 @@
 using System;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -20,6 +20,7 @@ using System.Diagnostics;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -27,7 +28,7 @@ namespace TensorFlowNET.Examples
     /// Convolutional Neural Network classifier for Hand Written Digits
     /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end.
     /// Use Stochastic Gradient Descent (SGD) optimizer. 
-    /// http://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
+    /// http://www.easy-tf.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
     /// </summary>
     public class DigitRecognitionCNN : IExample
     {

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -19,6 +19,7 @@ using System;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -26,7 +27,7 @@ namespace TensorFlowNET.Examples
     /// Neural Network classifier for Hand Written Digits
     /// Sample Neural Network architecture with two layers implemented for classifying MNIST digits.
     /// Use Stochastic Gradient Descent (SGD) optimizer. 
-    /// http://www.easy-tensorflow.com/tf-tutorials/neural-networks
+    /// http://www.easy-tf.com/tf-tutorials/neural-networks
     /// </summary>
     public class DigitRecognitionNN : IExample
     {

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -19,6 +19,7 @@ using System;
 using Tensorflow;
 using Tensorflow.Hub;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs

@@ -2,7 +2,7 @@
 using System.IO;
 using Tensorflow;
 using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs

@@ -6,7 +6,7 @@ using System.IO;
 using Console = Colorful.Console;
 using Tensorflow;
 using System.Drawing;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -89,7 +89,7 @@ namespace TensorFlowNET.Examples
             Directory.CreateDirectory(dir);
 
             // get model file
-            string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";
+            string url = "https://storage.googleapis.com/download.tf.org/models/inception5h.zip";
 
             Utility.Web.Download(url, dir, "inception5h.zip");
 

test/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs

@@ -3,7 +3,7 @@ using System;
 using System.IO;
 using System.Linq;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -93,7 +93,7 @@ namespace TensorFlowNET.Examples
             Directory.CreateDirectory(dir);
 
             // get model file
-            string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz";
+            string url = "https://storage.googleapis.com/download.tf.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz";
             
             Utility.Web.Download(url, dir, $"{pbFile}.tar.gz");
 

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -22,7 +22,7 @@ using TensorFlowNET.Examples.Utility;
 using System.Drawing;
 using System.Drawing.Drawing2D;
 using System.Linq;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -84,7 +84,7 @@ namespace TensorFlowNET.Examples
         public void PrepareData()
         {
             // get model file
-            string url = "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz";
+            string url = "http://download.tf.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz";
             Web.Download(url, modelDir, "ssd_mobilenet_v1_coco.tar.gz");
 
             Compress.ExtractTGZ(Path.Join(modelDir, "ssd_mobilenet_v1_coco.tar.gz"), "./");

test/TensorFlowNET.Examples/ImageProcessing/RetrainImageClassifier.cs

@@ -24,6 +24,7 @@ using System.Linq;
 using Tensorflow;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {
@@ -32,7 +33,7 @@ namespace TensorFlowNET.Examples
     /// and simply train a new classification layer on top. Transfer learning is a technique that shortcuts much of this 
     /// by taking a piece of a model that has already been trained on a related task and reusing it in a new model.
     /// 
-    /// https://www.tensorflow.org/hub/tutorials/image_retraining
+    /// https://www.tf.org/hub/tutorials/image_retraining
     /// </summary>
     public class RetrainImageClassifier : IExample
     {
@@ -167,7 +168,7 @@ namespace TensorFlowNET.Examples
         /// weights, and then sets up all the gradients for the backward pass.
         /// 
         /// The set up for the softmax and fully-connected layers is based on:
-        /// https://www.tensorflow.org/tutorials/mnist/beginners/index.html
+        /// https://www.tf.org/tutorials/mnist/beginners/index.html
         /// </summary>
         /// <param name="class_count"></param>
         /// <param name="final_tensor_name"></param>
@@ -508,7 +509,7 @@ namespace TensorFlowNET.Examples
         {
             // get a set of images to teach the network about the new classes
             string fileName = "flower_photos.tgz";
-            string url = $"http://download.tensorflow.org/example_images/{fileName}";
+            string url = $"http://download.tf.org/example_images/{fileName}";
             Web.Download(url, data_dir, fileName);
             Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir);
 

test/TensorFlowNET.Examples/Keras.cs

@@ -4,6 +4,7 @@ using Tensorflow;
 using Keras.Layers;
 using NumSharp;
 using Keras;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/Program.cs

@@ -22,6 +22,7 @@ using System.Linq;
 using System.Reflection;
 using Tensorflow;
 using Console = Colorful.Console;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs

@@ -25,6 +25,7 @@ using Tensorflow.Sessions;
 using TensorFlowNET.Examples.Text;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/TextProcessing/NER/LstmCrfNer.cs

@@ -7,6 +7,7 @@ using Tensorflow;
 using Tensorflow.Estimator;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 using static TensorFlowNET.Examples.DataHelpers;
 
 namespace TensorFlowNET.Examples.Text.NER

test/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs

@@ -6,6 +6,7 @@ using System.Linq;
 using Tensorflow;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples
 {

test/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs

@@ -1,5 +1,6 @@
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples.Text
 {

test/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs

@@ -2,6 +2,7 @@
 using System.Linq;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples.Text
 {
@@ -32,7 +33,7 @@ namespace TensorFlowNET.Examples.Text
             num_filters = new int[] { 64, 64, 128, 256, 512 };
             num_blocks = new int[] { 2, 2, 2, 2 };
             learning_rate = 0.001f;
-            cnn_initializer = tf.keras.initializers.he_normal();
+            cnn_initializer = tensorflow.keras.initializers.he_normal();
             fc_initializer = tf.truncated_normal_initializer(stddev: 0.05f);
 
             x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");

test/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs

@@ -17,6 +17,7 @@
 using System.Collections.Generic;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.Examples.Text
 {

test/TensorFlowNET.UnitTest/Basics/AssignTests.cs

@@ -1,6 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.Basics
 {

test/TensorFlowNET.UnitTest/Basics/NegativeTests.cs

@@ -1,6 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.Basics
 {

test/TensorFlowNET.UnitTest/ConstantTest.cs

@@ -4,7 +4,7 @@ using System;
 using System.Linq;
 using System.Runtime.InteropServices;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/ConsumersTest.cs

@@ -1,5 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using TensorFlowNET.Examples;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.ExamplesTests
 {

test/TensorFlowNET.UnitTest/GradientTest.cs

@@ -3,6 +3,7 @@ using NumSharp;
 using System.Linq;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/GraphTest.cs

@@ -3,7 +3,7 @@ using System;
 using System.Collections.Generic;
 using Tensorflow;
 using Buffer = Tensorflow.Buffer;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/KerasTests.cs

@@ -2,6 +2,7 @@
 using Keras.Layers;
 using NumSharp;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/NameScopeTest.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/OperationsTest.cs

@@ -5,6 +5,7 @@ using System.Linq;
 using NumSharp;
 using Tensorflow;
 using Buffer = Tensorflow.Buffer;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/PlaceholderTest.cs

@@ -1,6 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/PythonTest.cs

@@ -5,8 +5,7 @@ using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Newtonsoft.Json.Linq;
 using NumSharp;
 using Tensorflow;
-using Tensorflow.Util;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/SessionTest.cs

@@ -3,7 +3,7 @@ using NumSharp;
 using System;
 using System.Collections.Generic;
 using Tensorflow;
-using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/TensorTest.cs

@@ -6,6 +6,7 @@ using System.Runtime.InteropServices;
 using System.Threading;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/TrainSaverTest.cs

@@ -2,6 +2,7 @@
 using System;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using static Tensorflow.Python;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {
@@ -28,7 +29,7 @@ namespace TensorFlowNET.UnitTest
         }
 
         /// <summary>
-        /// https://www.tensorflow.org/api_docs/python/tf/variable_scope
+        /// https://www.tf.org/api_docs/python/tf/variable_scope
         /// how to create a new variable
         /// </summary>
         [TestMethod]

test/TensorFlowNET.UnitTest/VersionTest.cs

@@ -1,5 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
+using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {