implemented math_ops: 1) reduce_logsumexp, 2) reduce_max, 3) log, 4) square

Author: PppBr

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

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public static partial class tf
+    {
+        public static Tensor reshape(Tensor tensor,
+                Tensor shape,
+                string name = null) => gen_array_ops.reshape(tensor, shape, name);
+
+    }
+}

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

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public static partial class tf
+    {
+        public static Tensor tile(Tensor input,
+                Tensor multiples,
+                string name = null) => gen_array_ops.tile(input, multiples, name);
+
+    }
+}

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

@@ -66,6 +66,11 @@ public static Tensor rank(Tensor input, string name = null)
         public static Tensor ones_like<T>(T tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
             => ones_like_impl(tensor, dtype, name, optimize);
 
+        public static Tensor reshape(Tensor tensor, Tensor shape, string name = null)
+        {
+            return gen_array_ops.reshape(tensor, shape, null);
+        }
+
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
             return with(ops.name_scope(name, "ones_like", new { tensor }), scope =>

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -61,6 +61,32 @@ public static Tensor square(Tensor x, string name = null)
             return _op.outputs[0];
         }
 
+        /// <summary>
+        /// Returns which elements of x are finite.
+        /// </summary>
+        /// <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` of type `bool`.</returns>
+        public static Tensor is_finite(Tensor x, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("IsFinite", name, args: new { x });
+
+            return _op.outputs[0];
+        }
+
+        /// <summary>
+        /// Computes exponential of x element-wise.  \\(y = e^x\\).
+        /// </summary>
+        /// <param name="x"> A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.</param>
+        /// <param name="name"> A name for the operation (optional).</param>
+        /// <returns> A `Tensor`. Has the same type as `x`.</returns>
+        public static Tensor exp(Tensor x, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Exp", name, args: new { x });
+
+            return _op.outputs[0];
+        }
+
         /// <summary>
         /// Computes natural logarithm of x element-wise.
         /// </summary>
@@ -160,6 +186,13 @@ public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
             return _op.outputs[0];
         }
 
+        public static Tensor _max(Tensor input, int[] axis, bool keep_dims=false, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Max", name, new { input, reduction_indices = axis, keep_dims });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Pow", name, args: new { x, y });

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

@@ -87,6 +87,51 @@ public static Tensor reduced_shape(Tensor input_shape, Tensor axes)
             return gen_data_flow_ops.dynamic_stitch(a1, a2);
         }
 
+        /// <summary>
+        /// Computes log(sum(exp(elements across dimensions of a tensor))).
+        /// Reduces `input_tensor` along the dimensions given in `axis`.
+        /// Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
+        /// entry in `axis`. If `keepdims` is true, the reduced dimensions
+        /// are retained with length 1.
+
+        /// If `axis` has no entries, all dimensions are reduced, and a
+        /// tensor with a single element is returned.
+
+        /// This function is more numerically stable than log(sum(exp(input))). It avoids
+        /// overflows caused by taking the exp of large inputs and underflows caused by
+        /// taking the log of small inputs.
+        /// </summary>
+        /// <param name="input_tensor"> The tensor to reduce. Should have numeric type.</param>
+        /// <param name="axis"> The dimensions to reduce. If `None` (the default), reduces all 
+        /// dimensions.Must be in the range `[-rank(input_tensor), rank(input_tensor))`.</param>
+        /// <param name="keepdims"></param>
+        /// <returns> The reduced tensor.</returns>
+        public static Tensor reduce_logsumexp(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+        {
+            with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
+            {
+                var raw_max = reduce_max(input_tensor, axis, true);
+                var my_max = array_ops.stop_gradient(array_ops.where(gen_math_ops.is_finite(raw_max), raw_max, array_ops.zeros_like(raw_max)));
+                var result = gen_math_ops.log(
+                reduce_sum(
+                    gen_math_ops.exp(gen_math_ops.sub(input_tensor, my_max)),
+                    new Tensor(axis),
+                    keepdims));
+                if (!keepdims)
+                {
+                    my_max = array_ops.reshape(my_max, array_ops.shape(result));
+                }
+                result = gen_math_ops.add(result, my_max);
+                return _may_reduce_to_scalar(keepdims, axis, result);
+            });
+            return null;
+        }
+
+        public static Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+        {
+            return _may_reduce_to_scalar(keepdims, axis, gen_math_ops._max(input_tensor, (int[])_ReductionDims(input_tensor, axis), keepdims, name));
+        }
+
         /// <summary>
         /// Casts a tensor to type `int32`.
         /// </summary>

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -12,6 +12,7 @@ namespace TensorFlowNET.Examples
     /// </summary>
     public class NaiveBayesClassifier : Python, IExample
     { 
+        public Normal dist { get; set; }
         public void Run()
         {
             np.array<float>(1.0f, 1.0f);
@@ -72,16 +73,36 @@ public void fit(NDArray X, NDArray y)
             // Create a 3x2 univariate normal distribution with the 
             // Known mean and variance           
             var dist = tf.distributions.Normal(mean, tf.sqrt(variance));
-
+            this.dist = dist;
         }
 
         public void predict (NDArray X)
         {
-            // assert self.dist is not None
-            // nb_classes, nb_features = map(int, self.dist.scale.shape)
+            if (dist == null)
+            {
+                throw new ArgumentNullException("cant not find the model (normal distribution)!");
+            }
+            int nb_classes = (int) dist.scale().shape[0];
+            int nb_features = (int)dist.scale().shape[1];
 
+            // Conditional probabilities log P(x|c) with shape
+            // (nb_samples, nb_classes)
+            Tensor tile = tf.tile(new Tensor(X), new Tensor(new int[] { -1, nb_classes, nb_features }));
+            Tensor r = tf.reshape(tile, new Tensor(new int[] { -1, nb_classes, nb_features }));
+            var cond_probs = tf.reduce_sum(dist.log_prob(r));
+            // uniform priors
+            var priors = np.log(np.array<double>((1.0 / nb_classes) * nb_classes));
 
-            throw new NotFiniteNumberException();
+            // posterior log probability, log P(c) + log P(x|c)
+            var joint_likelihood = tf.add(new Tensor(priors), cond_probs);
+            // normalize to get (log)-probabilities
+            /*
+            var norm_factor = tf.reduce_logsumexp(joint_likelihood, axis = 1, keep_dims = True)
+            var log_prob = joint_likelihood - norm_factor;
+            // exp to get the actual probabilities
+            return tf.exp(log_prob)
+            */
+            throw new NotImplementedException();
         }
     }
 }