tf.log, tf.nn.softmax

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

@@ -18,6 +18,9 @@ namespace Tensorflow
         public static 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)
+            => gen_math_ops.log(x, name);
+
         public static Tensor multiply(Tensor x, Tensor y) 
             => gen_math_ops.mul(x, y);
 
@@ -33,11 +36,11 @@ namespace Tensorflow
         /// <param name="input"></param>
         /// <param name="axis"></param>
         /// <returns></returns>
-        public static Tensor reduce_sum(Tensor input, int[] axis = null) 
+        public static Tensor reduce_sum(Tensor input, int[] axis = null, int? reduction_indices = null) 
             => math_ops.reduce_sum(input);
 
-        public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
-            => math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name);
+        public static 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 cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null) 
             => math_ops.cast(x, dtype, name);

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

@@ -56,6 +56,9 @@ namespace Tensorflow
                 });
             }
 
+            public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
+                => gen_nn_ops.softmax(logits, name);
+
             public static 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/Operations/NnOps/gen_nn_ops.cs

@@ -146,6 +146,16 @@ namespace Tensorflow.Operations
             return _op.outputs[0];
         }
 
+        public static Tensor softmax(Tensor logits, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Softmax", name: name, args: new
+            {
+                logits
+            });
+
+            return _op.outputs[0];
+        }
+
         /// <summary>
         /// Computes softmax cross entropy cost and gradients to backpropagate.
         /// </summary>

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

@@ -42,7 +42,7 @@ namespace Tensorflow
             else
             {
                 tShape = constant_op._tensor_shape_tensor_conversion_function(shape.as_shape());
-                var c = constant_op.constant(0);
+                var c = constant_op.constant(0, dtype: dtype);
                 return gen_array_ops.fill(tShape, c, name: name);
             }
         }

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

@@ -38,7 +38,7 @@ namespace Tensorflow
         /// dimensions.Must be in the range `[-rank(input_tensor), rank(input_tensor))`.</param>
         /// <param name="keepdims"> If true, retains reduced dimensions with length 1.</param>
         /// <param name="name"> A name for the operation (optional).</param>
-        public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+        public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
         {
             var r = _ReductionDims(input_tensor, axis);
             if (axis == null)

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -1,4 +1,5 @@
-using System;
+using NumSharp.Core;
+using System;
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow;
@@ -13,6 +14,11 @@ namespace TensorFlowNET.Examples
     /// </summary>
     public class LogisticRegression : Python, IExample
     {
+        private float learning_rate = 0.01f;
+        private int training_epochs = 25;
+        private int batch_size = 100;
+        private int display_step = 1;
+
         public void Run()
         {
             PrepareData();
@@ -20,8 +26,34 @@ namespace TensorFlowNET.Examples
 
         private void PrepareData()
         {
-            MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
-            
+           var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
+
+            // tf Graph Input
+            var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784
+            var y = tf.placeholder(tf.float32, new TensorShape(-1, 10)); // 0-9 digits recognition => 10 classes
+
+            // Set model weights
+            var W = tf.Variable(tf.zeros(new Shape(784, 10)));
+            var b = tf.Variable(tf.zeros(new Shape(10)));
+
+            // Construct model
+            var pred = tf.nn.softmax(tf.matmul(x, W) + b); // Softmax
+
+            // Minimize error using cross entropy
+            var sum = -tf.reduce_sum(y * tf.log(pred), reduction_indices: 1);
+            var cost = tf.reduce_mean(sum);
+
+            // Gradient Descent
+            var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
+
+            // Initialize the variables (i.e. assign their default value)
+            var init = tf.global_variables_initializer();
+
+            with(tf.Session(), sess =>
+            {
+                // Run the initializer
+                sess.run(init);
+            });
         }
     }
 }

test/TensorFlowNET.Examples/Utility/DataSet.cs

@@ -9,12 +9,22 @@ namespace TensorFlowNET.Examples.Utility
     public class DataSet
     {
         private int _num_examples;
+        private int _epochs_completed;
+        private int _index_in_epoch;
+        private NDArray _images;
+        private NDArray _labels;
 
         public DataSet(NDArray images, NDArray labels, TF_DataType dtype, bool reshape)
         {
             _num_examples = images.shape[0];
             images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);
+            images.astype(dtype.as_numpy_datatype());
             images = np.multiply(images, 1.0f / 255.0f);
+
+            _images = images;
+            _labels = labels;
+            _epochs_completed = 0;
+            _index_in_epoch = 0;
         }
     }
 }

test/TensorFlowNET.Examples/Utility/Datasets.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace TensorFlowNET.Examples.Utility
+{
+    public class Datasets
+    {
+        private DataSet _train;
+        public DataSet train => _train;
+
+        private DataSet _validation;
+        public DataSet validation => _validation;
+
+        private DataSet _test;
+        public DataSet test => _test;
+
+        public Datasets(DataSet train, DataSet validation, DataSet test)
+        {
+            _train = train;
+            _validation = validation;
+            _test = test;
+        }
+    }
+}

test/TensorFlowNET.Examples/Utility/MnistDataSet.cs

@@ -18,9 +18,9 @@ namespace TensorFlowNET.Examples.Utility
         private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
         private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
 
-        public static void read_data_sets(string train_dir, 
+        public static Datasets read_data_sets(string train_dir, 
             bool one_hot = false,
-            TF_DataType dtype = TF_DataType.DtInvalid,
+            TF_DataType dtype = TF_DataType.TF_FLOAT,
             bool reshape = true,
             int validation_size = 5000,
             string source_url = DEFAULT_SOURCE_URL)
@@ -48,6 +48,10 @@ namespace TensorFlowNET.Examples.Utility
             train_labels = train_labels[np.arange(validation_size, end)];
 
             var train = new DataSet(train_images, train_labels, dtype, reshape);
+            var validation = new DataSet(validation_images, validation_labels, dtype, reshape);
+            var test = new DataSet(test_images, test_labels, dtype, reshape);
+
+            return new Datasets(train, validation, test);
         }
 
         public static NDArray extract_images(string file)