diff options
author | Lester Solbakken <lesters@oath.com> | 2020-08-31 09:08:26 +0200 |
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committer | Lester Solbakken <lesters@oath.com> | 2020-09-04 08:30:25 +0200 |
commit | 67caf3b6eee690bcd0c7fc7a7666bd2cf41b8816 (patch) | |
tree | f0b424c80c357091d738831bfcce16d071f006a1 /config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java | |
parent | 9a735030cd58e7f7ce7c2cd9bcaae121089e6ee7 (diff) |
Import TensorFlow models via ONNX conversion
Diffstat (limited to 'config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java')
-rw-r--r-- | config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java | 13 |
1 files changed, 5 insertions, 8 deletions
diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java index cad0ad7bae0..854c4d32a64 100644 --- a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java +++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java @@ -44,8 +44,8 @@ public class MlModelsTest { } private void verify(VespaModel model) { - assertEquals("Global models are created (although not used directly here", - 5, model.rankProfileList().getRankProfiles().size()); + assertEquals("Global models are created (although not used directly here)", + 3, model.rankProfileList().getRankProfiles().size()); RankProfilesConfig.Builder builder = new RankProfilesConfig.Builder(); model.getSearchClusters().get(0).getConfig(builder); @@ -62,18 +62,15 @@ public class MlModelsTest { } private final String testProfile = - "rankingExpression(input).rankingScript: attribute(argument)\n" + - "rankingExpression(input).type: tensor<float>(d0[1],d1[784])\n" + - "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(rankingExpression(input), (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))\n" + - "rankingExpression(mnist_tensorflow).rankingScript: join(reduce(join(map(join(reduce(join(join(join(0.009999999776482582, rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(a * b)), rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.0507009873554805 * if (a >= 0, a, 1.6732632423543772 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))\n" + "rankingExpression(Placeholder).rankingScript: attribute(argument)\n" + "rankingExpression(Placeholder).type: tensor<float>(d0[1],d1[784])\n" + - "rankingExpression(mnist_softmax_tensorflow).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))\n" + "rankingExpression(mnist_softmax_onnx).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))\n" + "rankingExpression(my_xgboost).rankingScript: if (f29 < -0.1234567, if (!(f56 >= -0.242398), 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (!(f60 >= -0.482947), if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)\n" + "rankingExpression(my_lightgbm).rankingScript: if (!(numerical_2 >= 0.46643291586559305), 2.1594397038037663, if (categorical_2 in [\"k\", \"l\", \"m\"], 2.235297305276056, 2.1792953471546546)) + if (categorical_1 in [\"d\", \"e\"], 0.03070842919354316, if (!(numerical_1 >= 0.5102250691730842), -0.04439151147520909, 0.005117411709368601)) + if (!(numerical_2 >= 0.668665477622446), if (!(numerical_2 >= 0.008118820676863816), -0.15361238490967524, -0.01192330846157292), 0.03499044894987518) + if (!(numerical_1 >= 0.5201391072644542), -0.02141000620783247, if (categorical_1 in [\"a\", \"b\"], -0.004121485787596721, 0.04534090904886873)) + if (categorical_2 in [\"k\", \"l\", \"m\"], if (!(numerical_2 >= 0.27283279016959255), -0.01924803254356527, 0.03643772842347651), -0.02701711918923075)\n" + + "rankingExpression(input).rankingScript: attribute(argument)\n" + + "rankingExpression(input).type: tensor<float>(d0[1],d1[784])\n" + "vespa.rank.firstphase: rankingExpression(firstphase)\n" + - "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_tensorflow) + rankingExpression(mnist_softmax_tensorflow) + rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost) + rankingExpression(my_lightgbm)\n" + + "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost) + rankingExpression(my_lightgbm)\n" + "vespa.type.attribute.argument: tensor<float>(d0[1],d1[784])\n"; } |