From 4cdf6b9f7213e5bb405eb8d412e23e16ef671e6f Mon Sep 17 00:00:00 2001 From: Bo Peng Date: Wed, 27 Mar 2019 17:10:59 -0500 Subject: [PATCH 1/2] implementing NB classifier --- src/TensorFlowNET.Core/APIs/tf.tile.cs | 6 +- .../Operations/array_ops.py.cs | 5 +- .../Operations/gen_array_ops.cs | 8 +- src/TensorFlowNET.Core/Tensors/dtypes.cs | 2 +- src/TensorFlowNET.Core/ops.py.cs | 2 + .../NaiveBayesClassifier.cs | 94 ++++++++++++++++--- 6 files changed, 100 insertions(+), 17 deletions(-) diff --git a/src/TensorFlowNET.Core/APIs/tf.tile.cs b/src/TensorFlowNET.Core/APIs/tf.tile.cs index 16bd48fd..2836c4dd 100644 --- a/src/TensorFlowNET.Core/APIs/tf.tile.cs +++ b/src/TensorFlowNET.Core/APIs/tf.tile.cs @@ -1,4 +1,5 @@ -using System; +using NumSharp.Core; +using System; using System.Collections.Generic; using System.Text; @@ -9,6 +10,9 @@ namespace Tensorflow public static Tensor tile(Tensor input, Tensor multiples, string name = null) => gen_array_ops.tile(input, multiples, name); + public static Tensor tile(NDArray input, + int[] multiples, + string name = null) => gen_array_ops.tile(input, multiples, name); } } diff --git a/src/TensorFlowNET.Core/Operations/array_ops.py.cs b/src/TensorFlowNET.Core/Operations/array_ops.py.cs index c5641f06..83acd21e 100644 --- a/src/TensorFlowNET.Core/Operations/array_ops.py.cs +++ b/src/TensorFlowNET.Core/Operations/array_ops.py.cs @@ -224,7 +224,10 @@ namespace Tensorflow /// /// When building ops to compute gradients, this op prevents the contribution of - /// its inputs to be taken into account.Normally, the gradient generator adds ops /// to a graph to compute the derivatives of a specified 'loss' by recursively /// finding out inputs that contributed to its computation.If you insert this op /// in the graph it inputs are masked from the gradient generator. They are not + /// its inputs to be taken into account.Normally, the gradient generator adds ops + /// to a graph to compute the derivatives of a specified 'loss' by recursively + /// finding out inputs that contributed to its computation.If you insert this op + /// in the graph it inputs are masked from the gradient generator. They are not /// taken into account for computing gradients. /// /// diff --git a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs index de8cc9d5..f2c5841f 100644 --- a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs +++ b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs @@ -1,4 +1,5 @@ -using System; +using NumSharp.Core; +using System; using System.Collections.Generic; using System.IO; using System.Text; @@ -156,6 +157,11 @@ namespace Tensorflow var _op = _op_def_lib._apply_op_helper("Tile", name, new { input, multiples }); return _op.outputs[0]; } + public static Tensor tile(NDArray input, int[] multiples, string name = null) + { + var _op = _op_def_lib._apply_op_helper("Tile", name, new { input, multiples }); + return _op.outputs[0]; + } public static Tensor transpose(Tensor x, int[] perm, string name = null) { diff --git a/src/TensorFlowNET.Core/Tensors/dtypes.cs b/src/TensorFlowNET.Core/Tensors/dtypes.cs index 005b5df9..e010c432 100644 --- a/src/TensorFlowNET.Core/Tensors/dtypes.cs +++ b/src/TensorFlowNET.Core/Tensors/dtypes.cs @@ -47,7 +47,7 @@ namespace Tensorflow dtype = TF_DataType.TF_STRING; break; default: - throw new Exception("Not Implemented"); + throw new Exception("as_dtype Not Implemented"); } return dtype; diff --git a/src/TensorFlowNET.Core/ops.py.cs b/src/TensorFlowNET.Core/ops.py.cs index 50fd59c9..00f1add5 100644 --- a/src/TensorFlowNET.Core/ops.py.cs +++ b/src/TensorFlowNET.Core/ops.py.cs @@ -441,6 +441,8 @@ namespace Tensorflow switch (value) { + case NDArray nd: + return constant_op.constant(nd, dtype: dtype, name: name); case Tensor tensor: return tensor; case string str: diff --git a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs index d3300519..5c4874b2 100644 --- a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs +++ b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs @@ -15,22 +15,87 @@ namespace TensorFlowNET.Examples public Normal dist { get; set; } public void Run() { - np.array(1.0f, 1.0f); - var X = np.array(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, }); - var y = np.array(0,0,1,1,2,2); + var X = np.array(new double[][] { new double[] { 5.1, 3.5},new double[] { 4.9, 3.0 },new double[] { 4.7, 3.2 }, + new double[] { 4.6, 3.1 },new double[] { 5.0, 3.6 },new double[] { 5.4, 3.9 }, + new double[] { 4.6, 3.4 },new double[] { 5.0, 3.4 },new double[] { 4.4, 2.9 }, + new double[] { 4.9, 3.1 },new double[] { 5.4, 3.7 },new double[] {4.8, 3.4 }, + new double[] {4.8, 3.0 },new double[] {4.3, 3.0 },new double[] {5.8, 4.0 }, + new double[] {5.7, 4.4 },new double[] {5.4, 3.9 },new double[] {5.1, 3.5 }, + new double[] {5.7, 3.8 },new double[] {5.1, 3.8 },new double[] {5.4, 3.4 }, + new double[] {5.1, 3.7 },new double[] {5.1, 3.3 },new double[] {4.8, 3.4 }, + new double[] {5.0 , 3.0 },new double[] {5.0 , 3.4 },new double[] {5.2, 3.5 }, + new double[] {5.2, 3.4 },new double[] {4.7, 3.2 },new double[] {4.8, 3.1 }, + new double[] {5.4, 3.4 },new double[] {5.2, 4.1},new double[] {5.5, 4.2 }, + new double[] {4.9, 3.1 },new double[] {5.0 , 3.2 },new double[] {5.5, 3.5 }, + new double[] {4.9, 3.6 },new double[] {4.4, 3.0 },new double[] {5.1, 3.4 }, + new double[] {5.0 , 3.5 },new double[] {4.5, 2.3 },new double[] {4.4, 3.2 }, + new double[] {5.0 , 3.5 },new double[] {5.1, 3.8 },new double[] {4.8, 3.0}, + new double[] {5.1, 3.8 },new double[] {4.6, 3.2 },new double[] { 5.3, 3.7 }, + new double[] {5.0 , 3.3 },new double[] {7.0 , 3.2 },new double[] {6.4, 3.2 }, + new double[] {6.9, 3.1 },new double[] {5.5, 2.3 },new double[] {6.5, 2.8 }, + new double[] {5.7, 2.8 },new double[] {6.3, 3.3 },new double[] {4.9, 2.4 }, + new double[] {6.6, 2.9 },new double[] {5.2, 2.7 },new double[] {5.0 , 2.0 }, + new double[] {5.9, 3.0 },new double[] {6.0 , 2.2 },new double[] {6.1, 2.9 }, + new double[] {5.6, 2.9 },new double[] {6.7, 3.1 },new double[] {5.6, 3.0 }, + new double[] {5.8, 2.7 },new double[] {6.2, 2.2 },new double[] {5.6, 2.5 }, + new double[] {5.9, 3.0},new double[] {6.1, 2.8},new double[] {6.3, 2.5}, + new double[] {6.1, 2.8},new double[] {6.4, 2.9},new double[] {6.6, 3.0 }, + new double[] {6.8, 2.8},new double[] {6.7, 3.0 },new double[] {6.0 , 2.9}, + new double[] {5.7, 2.6},new double[] {5.5, 2.4},new double[] {5.5, 2.4}, + new double[] {5.8, 2.7},new double[] {6.0 , 2.7},new double[] {5.4, 3.0 }, + new double[] {6.0 , 3.4},new double[] {6.7, 3.1},new double[] {6.3, 2.3}, + new double[] {5.6, 3.0 },new double[] {5.5, 2.5},new double[] {5.5, 2.6}, + new double[] {6.1, 3.0 },new double[] {5.8, 2.6},new double[] {5.0 , 2.3}, + new double[] {5.6, 2.7},new double[] {5.7, 3.0 },new double[] {5.7, 2.9}, + new double[] {6.2, 2.9},new double[] {5.1, 2.5},new double[] {5.7, 2.8}, + new double[] {6.3, 3.3},new double[] {5.8, 2.7},new double[] {7.1, 3.0 }, + new double[] {6.3, 2.9},new double[] {6.5, 3.0 },new double[] {7.6, 3.0 }, + new double[] {4.9, 2.5},new double[] {7.3, 2.9},new double[] {6.7, 2.5}, + new double[] {7.2, 3.6},new double[] {6.5, 3.2},new double[] {6.4, 2.7}, + new double[] {6.8, 3.00 },new double[] {5.7, 2.5},new double[] {5.8, 2.8}, + new double[] {6.4, 3.2},new double[] {6.5, 3.0 },new double[] {7.7, 3.8}, + new double[] {7.7, 2.6},new double[] {6.0 , 2.2},new double[] {6.9, 3.2}, + new double[] {5.6, 2.8},new double[] {7.7, 2.8},new double[] {6.3, 2.7}, + new double[] {6.7, 3.3},new double[] {7.2, 3.2},new double[] {6.2, 2.8}, + new double[] {6.1, 3.0 },new double[] {6.4, 2.8},new double[] {7.2, 3.0 }, + new double[] {7.4, 2.8},new double[] {7.9, 3.8},new double[] {6.4, 2.8}, + new double[] {6.3, 2.8},new double[] {6.1, 2.6},new double[] {7.7, 3.0 }, + new double[] {6.3, 3.4},new double[] {6.4, 3.1},new double[] {6.0, 3.0}, + new double[] {6.9, 3.1},new double[] {6.7, 3.1},new double[] {6.9, 3.1}, + new double[] {5.8, 2.7},new double[] {6.8, 3.2},new double[] {6.7, 3.3}, + new double[] {6.7, 3.0 },new double[] {6.3, 2.5},new double[] {6.5, 3.0 }, + new double[] {6.2, 3.4},new double[] {5.9, 3.0 }, new double[] {5.8, 3.0 }}); + + var y = np.array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2); fit(X, y); // Create a regular grid and classify each point + double x_min = (double) X.amin(0)[0] - 0.5; + double y_min = (double) X.amin(0)[1] - 0.5; + double x_max = (double) X.amax(0)[0] + 0.5; + double y_max = (double) X.amax(0)[1] + 0.5; + + var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30)); + var s = tf.Session(); + var samples = np.vstack(xx.ravel(), yy.ravel()); + var Z = s.run(predict(samples)); + } public void fit(NDArray X, NDArray y) { NDArray unique_y = y.unique(); - Dictionary>> dic = new Dictionary>>(); + Dictionary>> dic = new Dictionary>>(); // Init uy in dic foreach (int uy in unique_y.Data()) { - dic.Add(uy, new List>()); + dic.Add(uy, new List>()); } // Separate training points by class // Shape : nb_classes * nb_samples * nb_features @@ -38,10 +103,10 @@ namespace TensorFlowNET.Examples for (int i = 0; i < y.size; i++) { long curClass = (long)y[i]; - List> l = dic[curClass]; - List pair = new List(); - pair.Add((float)X[i,0]); - pair.Add((float)X[i, 1]); + List> l = dic[curClass]; + List pair = new List(); + pair.Add((double)X[i,0]); + pair.Add((double)X[i, 1]); l.Add(pair); if (l.Count > maxCount) { @@ -49,8 +114,8 @@ namespace TensorFlowNET.Examples } dic[curClass] = l; } - float[,,] points = new float[dic.Count, maxCount, X.shape[1]]; - foreach (KeyValuePair>> kv in dic) + double[,,] points = new double[dic.Count, maxCount, X.shape[1]]; + foreach (KeyValuePair>> kv in dic) { int j = (int) kv.Key; for (int i = 0; i < maxCount; i++) @@ -62,7 +127,7 @@ namespace TensorFlowNET.Examples } } - NDArray points_by_class = np.array(points); + NDArray points_by_class = np.array(points); // estimate mean and variance for each class / feature // shape : nb_classes * nb_features var cons = tf.constant(points_by_class); @@ -87,7 +152,10 @@ namespace TensorFlowNET.Examples // 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 })); + var t1= ops.convert_to_tensor(X, TF_DataType.TF_DOUBLE); + //var t2 = ops.convert_to_tensor(new int[] { 1, nb_classes }); + //Tensor tile = tf.tile(t1, t2); + Tensor tile = tf.tile(X, new int[] { 1, nb_classes }); 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 From 97702316f866d8b63e7b2e695b7e28ac84dbe319 Mon Sep 17 00:00:00 2001 From: Bo Peng Date: Wed, 27 Mar 2019 17:28:01 -0500 Subject: [PATCH 2/2] implementing NB classifier --- test/TensorFlowNET.Examples/NaiveBayesClassifier.cs | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs index d3cca828..d0a841e4 100644 --- a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs +++ b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs @@ -13,7 +13,7 @@ namespace TensorFlowNET.Examples public class NaiveBayesClassifier : Python, IExample { public int Priority => 100; - public bool Enabled => false; + public bool Enabled => true; public string Name => "Naive Bayes Classifier"; public Normal dist { get; set; } @@ -89,6 +89,8 @@ namespace TensorFlowNET.Examples var samples = np.vstack(xx.ravel(), yy.ravel()); var Z = s.run(predict(samples)); + + return true; } public void fit(NDArray X, NDArray y)